pax_global_header00006660000000000000000000000064116754001740014517gustar00rootroot0000000000000052 comment=463cdb54ceeefecb62313cd81add9b248a94f7af vlfeat-0.9.14/000077500000000000000000000000001167540017400130735ustar00rootroot00000000000000vlfeat-0.9.14/.gitattributes000066400000000000000000000000731167540017400157660ustar00rootroot00000000000000*.manifest -crlf -diff -merge *.vcproj binary *.sln binary vlfeat-0.9.14/.gitignore000066400000000000000000000012661167540017400150700ustar00rootroot00000000000000# Mac OS Finder Poop .DS_Store # MEX files *.mexglx *.mexmac *.mexmaci *.mexmaci64 *.mexa64 *.mex # Python complied files *.pyc # Other generated files and directories VERSION docsrc/version.html bin results doc build toolbox/mexw32 toolbox/mexmaci toolbox/mexmaci64 toolbox/mexmac toolbox/mexglx toolbox/mexa64 toolbox/mexw64 toolbox/mex toolbox/noprefix # Xcode vlfeat.xcodeproj/*.mode1 vlfeat.xcodeproj/*.mode1v3 vlfeat.xcodeproj/*.mode2v3 vlfeat.xcodeproj/*.pbxuser vlfeat.xcodeproj/*.perspective* vlfeat.xcodeproj/*.xcworkspace vlfeat.xcodeproj/xcuserdata # Visual C++ vlfeat.suo vlfeat.ncb vlfeat.vcproj.*.user vc/* # Emacs backups *~ # Vim swap files .*.swp # Opt directory opt/*vlfeat-0.9.14/COPYING000066400000000000000000000024561167540017400141350ustar00rootroot00000000000000Copyright (C) 2007-12, Andrea Vedaldi and Brian Fulkerson All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. vlfeat-0.9.14/Makefile000066400000000000000000000246561167540017400145500ustar00rootroot00000000000000# file: Makefile # description: Build everything # author: Andrea Vedaldi # Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. # All rights reserved. # # This file is part of the VLFeat library and is made available under # the terms of the BSD license (see the COPYING file). # VLFEAT BUILDING INSTRUCTIONS # # This makefile builds VLFeat on modern UNIX boxes with the GNU # toolchain. Mac OS X and Linux are explicitly supported, and support # for similar architectures can be easily added. # # Usually, compiling VLFeat reduces to typing # # > cd PATH_TO_VLFEAT_SOURCE_TREE # > make # # The makefile attempts to automatically determine the host # architecture. If this fails, or if the architecture is ambiguous, # the architecture can be set by specifying the ARCH variable. For # instance: # # > make ARCH=maci64 # # builds VLFeat for Mac OS X Intel 64 bit. # # !! Unforunately MATLAB mex script (at least up to version 2009B) has # !! a bug that prevents selecting an architecture different from the # !! default one for compilation. For instance, compiling maci64 may # !! not work as the default architecture is maci if both 32 and 64 # !! bit MATLAB are installed. This bug is easy to fix, but requires # !! patching the mex script. See www.vlfeat.org for detailed # !! instructions. # # Other useful variables are listed below (their default value is in # square bracked). # # ARCH [undefined] - Active architecture. The supported # architectures are maci, maci64, glnx86, or glnxa64 (these are # the same architecture identifers used by MATLAB: # http://www.mathworks.com/help/techdoc/ref/computer.html). If # undefined, the makefile attempts to automatically detect the # architecture. # # DEBUG [undefined] - If defined, turns on debugging symbols and # turns off optimizations # # PROFILE [undefined] - If defined, turns on debugging symbols but # does NOT turn off optimizations. # # VERB [undefined] - If defined, display in full the command # executed and their output. # # MEX [mex]- Path to MATLAB MEX compiler. If undefined, MATLAB supprot # is disabled. # # MKOCTFILE [undefined] - Path to Octave MKOCTFILE compiler. If undefined, # Octave support is disabled. # # To completely remove all build products use # # > make distclean # # Other useful targets include: # # clean - Removes intermediate build products for the active architecture. # archclean - Removes all build products for the active architecture. # distclean - Removes all build products. # info - Display a list of the variables defined by the Makefile. # help - Print this message. # # VLFeat is compsed of different parts (DLL, command line utilities, # MATLAB interface, Octave interface) so the makefile is divided in # components, located in make/*.mak. Please check out the # corresponding files in order to adjust parameters. # AUTORIGHTS SHELL = /bin/bash .PHONY : all all: # Feature selection DISABLE_SSE2=no DISABLE_THREADS=no # -------------------------------------------------------------------- # Error Messages # -------------------------------------------------------------------- err_no_arch = err_no_arch +=$(shell echo "** Unknown host architecture '$(UNAME)'. This identifier" 1>&2) err_no_arch +=$(shell echo "** was obtained by running 'uname -sm'. Edit the Makefile " 1>&2) err_no_arch +=$(shell echo "** to add the appropriate configuration." 1>&2) err_no_arch +=config err_internal =$(shell echo Internal error) err_internal +=internal err_spaces = $(shell echo "** VLFeat root dir VLDIR='$(VLDIR)' contains spaces." 1>&2) err_spaces += $(shell echo "** This is not supported due to GNU Make limitations." 1>&2) err_spaces +=spaces # -------------------------------------------------------------------- # Auto-detect architecture # -------------------------------------------------------------------- Darwin_PPC_ARCH := mac Darwin_Power_Macintosh_ARCH := mac Darwin_i386_ARCH := maci64 Darwin_x86_64_ARCH := maci64 Linux_i386_ARCH := glnx86 Linux_i686_ARCH := glnx86 Linux_unknown_ARC := glnx86 Linux_x86_64_ARCH := glnxa64 UNAME := $(shell uname -sm) ARCH ?= $($(shell echo "$(UNAME)" | tr \ _)_ARCH) # sanity check ifeq ($(ARCH),) die:=$(error $(err_no_arch)) endif ifneq ($(VLDIR),$(shell echo "$(VLDIR)" | sed 's/ //g')) die:=$(error $(err_spaces)) endif # -------------------------------------------------------------------- # Configuration # -------------------------------------------------------------------- VLDIR ?= . CC ?= cc LIBTOOL ?= libtool STD_CLFAGS = $(CFLAGS) STD_CFLAGS += -std=c99 STD_CFLAGS += -Wall -Wextra STD_CFLAGS += -Wno-unused-function -Wno-long-long -Wno-variadic-macros STD_CFLAGS += $(ifeq ($(DISABLE_THREADS),yes),-DVL_DISABLE_THREADS) STD_CFLAGS += $(ifeq ($(DISABLE_SSE2),yes),-DVL_DISABLE_SSE2) STD_CFLAGS += $(if $(DEBUG), -DDEBUG -O0 -g, -DNDEBUG -O3) STD_CFLAGS += $(if $(PROFILE), -g,) STD_LDFLAGS = $(LDFLAGS) # Architecture specific ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # Mac OS X Intel 32 ifeq ($(ARCH),maci) SDKROOT ?= /Developer/SDKs/MacOSX10.7.sdk MACOSX_DEPLOYMENT_TARGET ?= 10.4 STD_CFLAGS += -m32 -isysroot $(SDKROOT) -mmacosx-version-min=$(MACOSX_DEPLOYMENT_TARGET) STD_LDFLAGS += -mmacosx-version-min=$(MACOSX_DEPLOYMENT_TARGET) endif # Mac OS X Intel 64 ifeq ($(ARCH),maci64) SDKROOT ?= /Developer/SDKs/MacOSX10.7.sdk MACOSX_DEPLOYMENT_TARGET ?= 10.4 STD_CFLAGS += -m64 -isysroot $(SDKROOT) -mmacosx-version-min=$(MACOSX_DEPLOYMENT_TARGET) STD_LDFLAGS += -mmacosx-version-min=$(MACOSX_DEPLOYMENT_TARGET) endif # Linux-32 ifeq ($(ARCH),glnx86) STD_CFLAGS += -march=i686 STD_LDFLAGS += -Wl,--rpath,\$$ORIGIN/ -Wl,--as-needed endif # Linux-64 ifeq ($(ARCH),glnxa64) STD_LDFLAGS += -Wl,--rpath,\$$ORIGIN/ -Wl,--as-needed endif # -------------------------------------------------------------------- # Functions # -------------------------------------------------------------------- # $(call if-like,FILTER,WHY,WHAT) define if-like $(if $(filter $(1),$(2)),$(3)) endef # $(call dump-var,VAR) pretty-prints the content of a variable VAR on # multiple columns ifdef VERB define dump-var @echo "$(1) =" @echo "$($(1))" | sed 's/\([^ ][^ ]* [^ ][^ ]*\) */\1#/g' | \ tr '#' '\n' | column -t | sed 's/\(.*\)/ \1/g' endef else define dump-var @printf "%15s = %s\n" "$(1)" \ "$$(echo '$($(1))' | sed -e 's/[^ ][^ ]* /\.\.\./3' -e 's/\.\.\..*$$/\.\.\./g')" endef endif # $(call echo-var,VAR) pretty-prints the content of a variable VAR on # one line define echo-var @printf "%15s = %s\n" "$(1)" "$($(1))" endef # $(call echo-title,TITLE) pretty-prints TITLE as a title define echo-title @printf "** %s\n" "$(1)" endef # $(call print-command, CMD, TGT) prints a message define print-command @printf "%15s %s\n" "$(strip $(1))" "$(strip $(2))" endef # $(call C, CMD) runs $(CMD) silently define C @$(call print-command, $(1), "$(@)") @quiet () \ { \ local cmd out err ; \ cmd="$($(1))"; \ out=$$($${cmd} "$${@}" 2>&1) ; \ err=$${?} ; \ if test $${err} -gt 0 ; then \ echo "******* Offending Command:"; \ printf "%s" "$${cmd}" ; \ for i in "$${@}" ; do printf " '%s'" "$$i" ; done ; \ echo ; \ echo "******* Error Code: $${err}"; \ echo "******* Command Output:"; \ echo "$${out}"; \ fi; \ echo "$${out}" | grep [Ww]arning ; \ return $${err}; \ } ; quiet endef # If verbose print everything ifdef VERB C = $($(1)) endif # rule to create a directory .PRECIOUS: %/.dirstamp %/.dirstamp : @printf "%15s %s\n" MK "$(dir $@)" @mkdir -p $(dir $@) @echo "Directory generated by make." > $@ # $(call gendir, TARGET, DIR1 DIR2 ...) creates a target TARGET-dir that # triggers the creation of the directories DIR1, DIR2 define gendir $(1)-dir=$(foreach x,$(2),$(x)/.dirstamp) endef # -------------------------------------------------------------------- # Build # -------------------------------------------------------------------- # Each Makefile submodule appends appropriate dependencies to the all, # clean, archclean, distclean, and info targets. In addition, it # appends to the deps and bins variables the list of .d files (to be # inclued by make as auto-dependencies) and the list of files to be # added to the binary distribution. include make/dll.mak include make/bin.mak include make/matlab.mak include make/octave.mak include make/doc.mak include make/dist.mak .PHONY: clean, archclean, distclean, info, help no_dep_targets += clean archclean distclean info help clean: rm -f `find . -name '*~'` rm -f `find . -name '.DS_Store'` rm -f `find . -name '.gdb_history'` rm -f `find . -name '._*'` rm -rf ./results archclean: clean distclean: info: $(call echo-title,General settings) $(call dump-var,deps) $(call echo-var,PROFILE) $(call echo-var,DEBUG) $(call echo-var,VER) $(call echo-var,ARCH) $(call echo-var,CC) $(call echo-var,STD_CFLAGS) $(call echo-var,STD_LDFLAGS) @printf "\nThere are %s lines of code.\n" \ `cat $(m_src) $(mex_src) $(dll_src) $(dll_hdr) $(bin_src) | wc -l` # Holw help works: cat this file, # skip the first block until an empty line is found (twice) # print the first block until an empty line, # remove the `# ' prefix from each remaining line help: @cat Makefile | \ sed -n '1,/^$$/!p' | \ sed -n '1,/^$$/!p' | \ sed -n '1,/^$$/p' | \ sed 's/^# \{0,1\}\(.*\)$$/\1/' # -------------------------------------------------------------------- # Include dependencies # -------------------------------------------------------------------- .PRECIOUS: $(deps) ifeq ($(filter $(no_dep_targets), $(MAKECMDGOALS)),) -include $(deps) endif vlfeat-0.9.14/Makefile.mak000066400000000000000000000357471167540017400153220ustar00rootroot00000000000000# file: Makefile.mak # descrption: Microsoft NMake makefile # authors: Andrea Vedaldi, Brian Fulkerson, Mircea Cimpoi # Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson. # All rights reserved. # # This file is part of the VLFeat library and is made available under # the terms of the BSD license (see the COPYING file). # -------------------------------------------------------------------- # Customization # -------------------------------------------------------------------- # To modify this script to run on your platform it is usually # sufficient to modify the following variables: # # ARCH: Either win32 or win64 [win64] # DEBUG: Set to yes to ativate debugging [no] # MATLABROOT: Path to MATLAB # MSVSVER: Visual Studio version (e.g. 80, 90, 100) [90 for VS 9.0] # MSVCROOT: Visual C++ location [$(VCInstallDir)]. # WINSDKROOT: Windows SDK location [$(WindowsSdkDir)] # # Note that some of these variables depend on the architecture # (either win32 or win64). VER = 0.9.14 ARCH = win64 DEBUG = no BRANCH = v$(VER)-$(ARCH) MSVSVER = 90 MSVCROOT = $(VCInstallDir) WINSDKROOT = $(WindowsSdkDir) GIT = git !if "$(MSVCROOT)" == "" MSVCROOT = C:\Program Files (x86)\Microsoft Visual Studio 9.0\VC !endif !if "$(WINSDKROOT)" == "" WINSDKROOT = C:\Program Files\Microsoft SDKs\Windows\v6.0A !endif # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 32-bit !if "$(ARCH)" == "win32" !message === COMPILING FOR 32-BIT MATLABROOT = C:\Program Files (x86)\MATLAB\R2009b MEX = "$(MATLABROOT)\bin\mex.bat" MEXOPT = "$(MATLABROOT)\bin\win32\mexopts\msvc$(MSVSVER)opts.bat" MEXEXT = mexw32 CC = "$(MSVCROOT)\bin\cl.exe" LINK = "$(MSVCROOT)\bin\link.exe" MSVCR_PATH = $(MSVCROOT)\redist\x86\Microsoft.VC$(MSVSVER).CRT LFLAGS = /MACHINE:X86 \ /LIBPATH:"$(MSVCROOT)\lib" \ /LIBPATH:"$(WINSDKROOT)\lib" # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 64-bit !elseif "$(ARCH)" == "win64" !message === COMPILING FOR 64-BIT MATLABROOT = C:\Program Files\MATLAB\R2009b MEX = "$(MATLABROOT)\bin\mex.bat" MEXOPT = "$(MATLABROOT)\bin\win64\mexopts\msvc$(MSVSVER)opts.bat" MEXEXT = mexw64 CC = "$(MSVCROOT)\bin\amd64\cl.exe" LINK = "$(MSVCROOT)\bin\amd64\link.exe" !if $(MSVSVER) >= 100 MSVCR_PATH = $(MSVCROOT)\redist\x64\Microsoft.VC$(MSVSVER).CRT !else MSVCR_PATH = $(MSVCROOT)\redist\amd64\Microsoft.VC$(MSVSVER).CRT !endif LFLAGS = /MACHINE:X64 \ /LIBPATH:"$(MSVCROOT)\lib\amd64" \ /LIBPATH:"$(WINSDKROOT)\lib\x64" !else !error ARCH = $(ARCH) is an unknown architecture. !endif # -------------------------------------------------------------------- # Flags # -------------------------------------------------------------------- # Debug info is embedded in .obj and .lib files (CodeView /Z7 option) # but in .pdb files for .exe and .dll (since the linker does not # produce CodeView output anymore). # # CFLAGS # /nologo : CL does not display splash # _CRT_NO_DEPRECATE : Do not deprecate `insecure' fscanf, snprintf, ... # __LITTLE_ENDIAN__ : Signal little endian architecture # /I. : Add VLROOT to include search path # /MD : Multi-thread run-time library dynamically linked # /TC : Source code is C (not C++) # /W3 : Usa all warnings # /Zp8 : Align structures to 8 bytes # /Ox : Turn on optimizations # /D"DEBUG" : [DEBUG] Turn on debugging in VLFeat # /Z7 : [DEBUG] Embedded CodeView debug info in .obj # /D"NDEBUG" : [NO DEBUG] Switches off asserts # # LFLAGS # /NOLOGO : LINK does not display splash # /INCREMENTAL:NO : No incremental linking # /MANIFEST : See DLL HELL below # /DEBUG : [DEBUG] Generate debug info (.pdb files) # # MEX_FLAGS # -I : Include VLFeat # -L : Add a library search path # -l : Link a dll # # ======================= ABOUT THE DLL HELL ========================= # # This makefile compiles VLFeat to make use of the side-by-side # deployment model, redestribtin the appropraite Visual C runtime # library with the library and executables. In Visual Studio < 10.0 # this meant including a manifest file, while in version >= 10.0 this # requirement has been relaxed. # # References: # http://www.codeguru.com/forum/showthread.php?t=408061 # http://mariusbancila.ro/blog/2010/03/24/visual-studio-2010-changes-for-vc-part-5 # http://social.msdn.microsoft.com/Forums/is/vcgeneral/thread/ca9177b2-2d02-42d8-8892-c6a25e6cfadb # bindir = bin\$(ARCH) mexdir = toolbox\mex\$(MEXEXT) objdir = $(bindir)\objs CFLAGS = /nologo /TC /MD \ /D"_CRT_SECURE_NO_DEPRECATE" \ /D"__LITTLE_ENDIAN__" \ /I. \ /W1 /Zp8 /Ox LFLAGS = $(LFLAGS) /NOLOGO \ /INCREMENTAL:NO \ /MANIFEST !if "$(DEBUG)" != "no" !message === DEBUGGING ON CFLAGS = $(CFLAGS) /Z7 /D"DEBUG" LFLAGS = $(LFLAGS) /DEBUG !else !message === DEBUGGING OFF CFLAGS = $(CFLAGS) /D"NDEBUG" !endif DLL_CFLAGS = /D"VL_BUILD_DLL" EXE_LFLAGS = $(LFLAGS) /LIBPATH:"$(bindir)" vl.lib MEX_FLAGS = -f $(MEXOPT) -I. -Itoolbox -L"$(bindir)" -lvl libsrc = \ vl\aib.c \ vl\array.c \ vl\dsift.c \ vl\generic.c \ vl\getopt_long.c \ vl\hikmeans.c \ vl\homkermap.c \ vl\host.c \ vl\ikmeans.c \ vl\imopv.c \ vl\imopv_sse2.c \ vl\kdtree.c \ vl\kmeans.c \ vl\lbp.c \ vl\mathop.c \ vl\mathop_sse2.c \ vl\mser.c \ vl\pegasos.c \ vl\pgm.c \ vl\quickshift.c \ vl\random.c \ vl\rodrigues.c \ vl\sift.c \ vl\slic.c \ vl\stringop.c cmdsrc = \ src\aib.c \ src\mser.c \ src\sift.c \ src\test_getopt_long.c \ src\test_heap-def.c \ src\test_host.c \ src\test_imopv.c \ src\test_mathop.c \ src\test_mathop_abs.c \ src\test_nan.c \ src\test_qsort-def.c \ src\test_rand.c \ src\test_stringop.c \ src\test_threads.c \ src\test_vec_comp.c mexsrc = \ toolbox\aib\vl_aib.c \ toolbox\aib\vl_aibhist.c \ toolbox\geometry\vl_irodr.c \ toolbox\geometry\vl_rodr.c \ toolbox\imop\vl_imdisttf.c \ toolbox\imop\vl_imintegral.c \ toolbox\imop\vl_imsmooth.c \ toolbox\imop\vl_imwbackwardmx.c \ toolbox\imop\vl_tpsumx.c \ toolbox\kmeans\vl_hikmeans.c \ toolbox\kmeans\vl_hikmeanspush.c \ toolbox\kmeans\vl_ikmeans.c \ toolbox\kmeans\vl_ikmeanspush.c \ toolbox\kmeans\vl_kmeans.c \ toolbox\misc\vl_alldist.c \ toolbox\misc\vl_alldist2.c \ toolbox\misc\vl_binsearch.c \ toolbox\misc\vl_binsum.c \ toolbox\misc\vl_getpid.c \ toolbox\misc\vl_homkermap.c \ toolbox\misc\vl_ihashfind.c \ toolbox\misc\vl_ihashsum.c \ toolbox\misc\vl_inthist.c \ toolbox\misc\vl_kdtreebuild.c \ toolbox\misc\vl_kdtreequery.c \ toolbox\misc\vl_lbp.c \ toolbox\misc\vl_localmax.c \ toolbox\misc\vl_pegasos.c \ toolbox\misc\vl_samplinthist.c \ toolbox\misc\vl_simdctrl.c \ toolbox\misc\vl_twister.c \ toolbox\misc\vl_version.c \ toolbox\mser\vl_erfill.c \ toolbox\mser\vl_mser.c \ toolbox\quickshift\vl_quickshift.c \ toolbox\sift\vl_dsift.c \ toolbox\sift\vl_sift.c \ toolbox\sift\vl_siftdescriptor.c \ toolbox\sift\vl_ubcmatch.c \ toolbox\slic\vl_slic.c !if "$(ARCH)" == "win32" libobj = $(libsrc:vl\=bin\win32\objs\) cmdexe = $(cmdsrc:src\=bin\win32\) mexdll = $(mexsrc:.c=.mexw32) mexdll = $(mexdll:toolbox\sift=toolbox\mex\mexw32) mexdll = $(mexdll:toolbox\mser=toolbox\mex\mexw32) mexdll = $(mexdll:toolbox\imop=toolbox\mex\mexw32) mexdll = $(mexdll:toolbox\geometry=toolbox\mex\mexw32) mexdll = $(mexdll:toolbox\kmeans=toolbox\mex\mexw32) mexdll = $(mexdll:toolbox\misc=toolbox\mex\mexw32) mexdll = $(mexdll:toolbox\aib=toolbox\mex\mexw32) mexdll = $(mexdll:toolbox\quickshift=toolbox\mex\mexw32) mexdll = $(mexdll:toolbox\slic=toolbox\mex\mexw32) mexpdb = $(mexdll:.dll=.pdb) !elseif "$(ARCH)" == "win64" libobj = $(libsrc:vl\=bin\win64\objs\) cmdexe = $(cmdsrc:src\=bin\win64\) mexdll = $(mexsrc:.c=.mexw64) mexdll = $(mexdll:toolbox\sift=toolbox\mex\mexw64) mexdll = $(mexdll:toolbox\mser=toolbox\mex\mexw64) mexdll = $(mexdll:toolbox\imop=toolbox\mex\mexw64) mexdll = $(mexdll:toolbox\geometry=toolbox\mex\mexw64) mexdll = $(mexdll:toolbox\kmeans=toolbox\mex\mexw64) mexdll = $(mexdll:toolbox\misc=toolbox\mex\mexw64) mexdll = $(mexdll:toolbox\aib=toolbox\mex\mexw64) mexdll = $(mexdll:toolbox\quickshift=toolbox\mex\mexw64) mexdll = $(mexdll:toolbox\slic=toolbox\mex\mexw64) mexpdb = $(mexdll:.mexw64=.pdb) !endif libobj = $(libobj:.c=.obj) cmdexe = $(cmdexe:.c=.exe) cmdpdb = $(cmdexe:.exe=.pdb) # Visual Studio redistributable files MSVCR = Microsoft.VC$(MSVSVER).CRT !if $(MSVSVER) <= 90 # VS <= 2008 needs a manifest too bincrt = $(bindir)\msvcr$(MSVSVER).dll $(bindir)\$(MSVCR).manifest mexcrt = $(mexdir)\msvcr$(MSVSVER).dll $(mexdir)\$(MSVCR).manifest !else bincrt = $(libdir)\msvcr$(MSVSVER).dll mexcrt = $(mexdir)\msvcr$(MSVSVER).dll !endif !ifdef MATLABROOT all: $(bindir) $(objdir) $(mexdir) \ $(bincrt) $(bindir)\vl.lib $(bindir)\vl.dll \ $(cmdexe) \ $(mexcrt) $(mexdir)\vl.dll $(mexdll) !else all: $(bindir) $(objdir) \ $(bincrt) $(bindir)\vl.lib $(bindir)\vl.dll \ $(cmdexe) !endif BUILD_MEX=@echo .... CC [MEX] $(@) && \ $(MEX) $(MEX_FLAGS) "$(<)" -output $(@) # -------------------------------------------------------------------- # Maintenance rules # -------------------------------------------------------------------- clean: del /f /Q $(libobj) del /f /Q $(objdir) del /f /Q $(cmdpdb) del /f /Q $(mexpdb) archclean: if exist bin\$(ARCH) rmdir /S /Q bin\$(ARCH) if exist $(mexdir) rmdir /S /Q $(mexdir) distclean: if exist bin rmdir /S /Q bin if exist toolbox\mex rmdir /S /Q toolbox\mex info: @echo $(mexx) @echo ** bindir = $(bindir) @echo ** mexdir = $(mexdir) @echo ** objdir = $(objdir) @echo ** libsrc = $(libsrc) @echo ** libobj = $(libobj) @echo ** cmdsrc = $(cmdsrc) @echo ** cmdexe = $(cmdexe) @echo ** mexsrc = $(mexsrc) @echo ** mexdll = $(mexdll) @echo ** CC = $(CC) @echo ** CFLAGS = $(CFLAGS) @echo ** DLL_CFLAGS = $(DLL_CFLAGS) @echo ** MEX_CFLAGS = $(MEX_CFLAGS) @echo ** BUILD_MEX = "$(BUILD_MEX)" @echo ** MATLABROOT = $(MATLABROOT) @echo ** MEX_LFLAGS = $(MEX_LFLAGS) @echo ** MEX = $(MEX) @echo ** MEXEXT = $(MEXEXT) @echo ** MEXOPT = $(MEXOPT) @echo ** MSVSVER = $(MSVSVER) @echo ** MSVCROOT = $(MSVCROOT) @echo ** MSVCR = $(MSVCR) @echo ** MSVCR_PATH = $(MSVCR_PATH) @echo ** bincrt = $(bincrt) @echo ** mexcrt = $(mexcrt) @echo ** WINSDKROOT = $(WINSDKROOT) @echo ** DEBUG = $(DEBUG) # -------------------------------------------------------------------- # Build rules # -------------------------------------------------------------------- # create directory if missing $(bindir) : mkdir $(bindir) $(objdir) : mkdir $(objdir) $(mexdir) : mkdir $(mexdir) # -------------------------------------------------------------------- # Rules to compile the VLFeat DLL # -------------------------------------------------------------------- # special sources with SSE2 support $(objdir)\mathop_sse2.obj : vl\mathop_sse2.c @echo .... CC [+SSE2] $(@) @$(CC) $(CFLAGS) $(DLL_CFLAGS) /arch:SSE2 /D"__SSE2__" /c /Fo"$(@)" "vl\$(@B).c" $(objdir)\imopv_sse2.obj : vl\imopv_sse2.c @echo .... CC [+SSE2] $(@) @$(CC) $(CFLAGS) $(DLL_CFLAGS) /arch:SSE2 /D"__SSE2__" /c /Fo"$(@)" "vl\$(@B).c" # vl\*.c -> $objdir\*.obj {vl}.c{$(objdir)}.obj: @echo .... CC $(@) @$(CC) $(CFLAGS) $(DLL_CFLAGS) /c /Fo"$(@)" "$(<)" # Link VLFeat DLL $(bindir)\vl.dll : $(libobj) @echo .. LINK [DLL] $(@R).dll $(LINK) /DLL $(LFLAGS) $(**) /OUT:"$(@)" @mt /nologo /outputresource:"$(@);#2" /manifest "$(@R).dll.manifest" @-del "$(@R).dll.manifest" # *.obj -> *.lib $(bindir)\vl.lib : $(libobj) @echo ... LIB $(@R).lib @lib $(**) /OUT:"$(@)" /NOLOGO # redistributable: msvcr__.dll => bin/win{32,64}/msvcr__.dll $(bindir)\$(MSVCR).manifest : "$(MSVCR_PATH)\$(MSVCR).manifest" copy $(**) "$(@)" $(bindir)\msvcr$(MSVSVER).dll: "$(MSVCR_PATH)\msvcr$(MSVSVER).dll" copy $(**) "$(@)" # -------------------------------------------------------------------- # Rules to compile the VLFeat EXE files # -------------------------------------------------------------------- # src\*.c -> $bindir\*.exe {src}.c{$(bindir)}.exe: @echo .... CC [EXE] $(@) @$(CC) $(CFLAGS) /Fe"$(@)" /Fo"$(@R).obj" "$(<)" /link $(EXE_LFLAGS) @MT /nologo /outputresource:"$(@);#1" /manifest "$(@).manifest" @-del "$(@).manifest" @-del "$(@R).obj" # -------------------------------------------------------------------- # Rules to compile the VLFeat MEX files # -------------------------------------------------------------------- startmatlab: "$(MATLABROOT)/bin/matlab.exe" -nodesktop # toolbox\*.c -> toolbox\*.dll {toolbox\sift}.c{$(mexdir)}.$(MEXEXT): $(BUILD_MEX) {toolbox\mser}.c{$(mexdir)}.$(MEXEXT): $(BUILD_MEX) {toolbox\imop}.c{$(mexdir)}.$(MEXEXT): $(BUILD_MEX) {toolbox\geometry}.c{$(mexdir)}.$(MEXEXT): $(BUILD_MEX) {toolbox\kmeans}.c{$(mexdir)}.$(MEXEXT): $(BUILD_MEX) {toolbox\aib}.c{$(mexdir)}.$(MEXEXT): $(BUILD_MEX) {toolbox\quickshift}.c{$(mexdir)}.$(MEXEXT): $(BUILD_MEX) {toolbox\misc}.c{$(mexdir)}.$(MEXEXT): $(BUILD_MEX) {toolbox\slic}.c{$(mexdir)}.$(MEXEXT): $(BUILD_MEX) # vl.dll => mexw{32,64}\vl.dll $(mexdir)\vl.dll : $(bindir)\vl.dll copy "$(**)" "$(@)" # redistributable: msvcr__.dll => bin/win{32,64}/msvcr__.dll $(mexdir)\$(MSVCR).manifest : "$(MSVCR_PATH)\$(MSVCR).manifest" copy $(**) "$(@)" $(mexdir)\msvcr$(MSVSVER).dll: "$(MSVCR_PATH)\msvcr$(MSVSVER).dll" copy $(**) "$(@)" # -------------------------------------------------------------------- # Rules to post the binary files # -------------------------------------------------------------------- bin-release: echo Fetching remote tags && \ $(GIT) fetch --tags && \ echo Checking out v$(VER) && \ $(GIT) checkout v$(VER) echo Rebuilding binaries for release if exist "bin\$(ARCH)" del /f /Q "bin\$(ARCH)" if exist "bin\mex\$(ARCH)" del /f /Q "toolbox\mex$(ARCH)" nmake /f Makefile.mak ARCH=$(ARCH) bin-commit: bin-release @echo Fetching remote tags && \ $(GIT) fetch --tags @echo Crearing/resetting and checking out branch $(BRANCH) to v$(VER) && \ $(GIT) branch -f $(BRANCH) v$(VER) && \ $(GIT) checkout $(BRANCH) @echo Adding binaries && \ $(GIT) add -f $(bincrt) && \ $(GIT) add -f "$(bindir)\vl.lib" && \ $(GIT) add -f "$(bindir)\vl.dll" && \ $(GIT) add -f $(cmdexe) && \ @echo Adding MEX files && \ $(GIT) add -f $(mexcrt) && \ $(GIT) add -f "$(mexdir)\vl.dll" && \ $(GIT) add -f $(mexdll) && \ @echo Commiting changes && \ $(GIT) commit -m "$(ARCH) binaries for version $(VER)" @echo Commiting and pushing to server the binaries && \ $(GIT) push -v --force bin $(BRANCH):refs/heads/$(BRANCH) && \ $(GIT) checkout v$(VER) && \ $(GIT) branch -D $(BRANCH) vlfeat-0.9.14/README000066400000000000000000000135431167540017400137610ustar00rootroot00000000000000 VisionLab Features Library Andrea Vedaldi and Brian Fulkerson ABOUT The VLFeat open source library implements popular computer vision algorithms including SIFT, MSER, k-means, hierarchical k-means, agglomerative information bottleneck, and quick shift. It is written in C for efficiency and compatibility, with interfaces in MATLAB for ease of use, and detailed documentation throughout. It supports Windows, Mac OS X, and Linux. VLFeat is distributed under the BSD license (see the COPYING file). Most of the documentation is available online at http://www.vlfeat.org/index.html or compiled in HTML format in `doc/index.html'. INSTALLING If you downloaded VLFeat binary package, installation is trivial. The binaries are found in the `bin/ARCH' directory, where ARCH depends on the architecture of your machine. VLFeat supports the following ones out of the box: ARCH MEXEXT DESCRIPTION ----------------------------------------------- maci mexmaci Mac OS X Intel maci64 mexmaci64 Mac OS X Intel (64 bit) glnx86 mexglx GNU Linux glnxa64 mexa64 GNU Linux (64 bit) win32 mexw32 Windows win64 mexw64 Windows (64 bit) The `bin' directory contains also a copy of the VLFeat DLL (and of the appropriate C runtime DLL in the case of Windows). The VLFeat MATLAB toolbox can be found in the `toolbox' directory. To use it with MATLAB, run the VL_SETUP command as > cd VLFEATROOT/toolbox ; vl_setup or > run VLFEATROOT/toolbox/vl_setup You can make this permanent by adding the line to MATLAB startup.m file. An HTML copy of the documentation can be found in `doc/index.html'. The same documentation is available online at http://www.vlfeat.org/index.html. COMPILING If you downloaded VLFeat source package or just want to compile your own version, all you need is a standard compiler setup. UNIX. In Mac OS X and Linux you will need GNU GCC and GNU Make. In fact, most C-90 compilers will do, although a few C-99 features are used. To compile MATLAB support, MATLAB should also be installed and the MATLAB MEX command should be available from the command line. In general, issuing > make should be enough to compile VLFeat. Type > make help or refer to http://www.vlfeat.org for further instructions. WINDOWS. In Windows you will need Visual Studio 2008 or 2010. VLFeat includes a Microsoft NMAKE Makefile (Makefile.mak). Open the Visual C command prompty (make sure you run the 64 bit version if your host is 64 bit), into the VLFeat directory and issue. > nmake /f Makefile.mak ARCH=win32 or > nmake /f Makefile.mak ARCH=win64 depending on your architecture. CREATING THE DOCUMENTATION Creating the documentation requires a UNIX platform (either Linux or Mac OS X) and the following tools - fig2dev (part of transfig) - a modern LaTeX with + pdflatex + dvips + htlatex (possibily part of TeX4ht) + dvipng (possibily a separated package) - doxygen (a recent version) FIGURES. Figures are preprocessed by typing > make -C doc/figures However, you need to run MATLAB programs to generate most of the figures to start with. To this end, load MATLAB and (provided that everything is compiled and installed correctly) type > vl_demo TUTORIALS. To create the figures for the tutorials, issue > make doc-deep > make doc SOURCE CODE DOCUMENTATION. To compile the source code documentation issue > make doc-api The start of the documentation is the file `doc/index.html'. APPENDIX CODE COMPATIBILITY. In addition to the C-90 standard, the C compiler is supposed to support the following common features: - long int (64 bit integer) support - variadic macro support The SSE accelerated code requires the compiler to support Intel intrisic. GCC and Visual C satisfy all the requirements. CHANGES 0.9.14 Added SLIC superpixels and VL_ALPHANUM(). Improved Windows binary package and added support for Visual Studio 2010. Improved the documentation layout and added a proper bibliography. Bugfixes and other minor improvements. Moved from the GPL to the less restrictive BSD license. 0.9.13 Fixes Windows binary package. 0.9.12 Fixes vl_compile and the architecture string on Linux 32 bit. 0.9.11 Fixes a compatibility problem on older Mac OS X versions. A few bugfixes are included too. 0.9.10 Improves the homogeneous kernel map. Plenty of small tweaks and improvements. Make maci64 the default architecture on the Mac. 0.9.9 Added: sift matching example. Extended Caltech-101 classification example to use kd-trees. 0.9.8 Added: image distance transform, PEGASOS, floating point K-means, homogeneous kernel maps, a Caltech-101 classification example. Improved documentation. 0.9.7 Changed the Mac OS X binary distribution to require a less recent version of Mac OS X (10.5). 0.9.6 Changed the GNU/Linux binary distribution to require a less recent version of the C library. 0.9.5 Added kd-tree and new SSE-accelerated vector/histogram comparison code. Improved dense SIFT (dsift) implementation. Added Snow Leopard and MATLAB R2009b support. 0.9.4 Added quick shift. Renamed dhog to dsift and improved implementation and documentation. Improved tutorials. Added 64 bit Windows binaries. Many other small changes. 0.9.3 Namespace change (everything begins with a vl_ prefix now). Many other changes to provide compilation support on Windows with MATLAB 7. beta-3 Completions to the ikmeans code. beta-2 Many completions. beta-1 Initial public release. vlfeat-0.9.14/apps/000077500000000000000000000000001167540017400140365ustar00rootroot00000000000000vlfeat-0.9.14/apps/phow_caltech101.m000066400000000000000000000260051167540017400171010ustar00rootroot00000000000000function phow_caltech101 % PHOW_CALTECH101 Image classification in the Caltech-101 dataset % This program demonstrates how to use VLFeat to construct an image % classifier on the Caltech-101 data. The classifier uses PHOW % features (dense SIFT), spatial histograms of visual words, and a % Chi2 SVM. To speedup computation it uses VLFeat fast dense SIFT, % kd-trees, and homogeneous kernel map. The program also % demonstrates VLFeat PEGASOS SVM solver, although for this small % dataset other solvers such as LIBLINEAR can be more efficient. % % By default 15 training images are used, which should result in % about 64% performance (a good performance considering that only a % single feature type is being used). % % Call PHOW_CALTECH101 to train and test a classifier on a small % subset of the Caltech-101 data. Note that the program % automatically downloads a copy of the Caltech-101 data from the % Internet if it cannot find a local copy. % % Edit the PHOW_CALTECH101 file to change the program configuration. % % To run on the entire dataset change CONF.TINYPROBLEM to FALSE. % % The Caltech-101 data is saved into CONF.CALDIR, which defaults to % 'data/caltech-101'. Change this path to the desired location, for % instance to point to an existing copy of the Caltech-101 data. % % The program can also be used to train a model on custom data by % pointing CONF.CALDIR to it. Just create a subdirectory for each % class and put the training images there. Make sure to adjust % CONF.NUMTRAIN accordingly. % % Intermediate files are stored in the directory CONF.DATADIR. All % such files begin with the prefix CONF.PREFIX, which can be changed % to test different parameter settings without overriding previous % results. % % The program saves the trained model in % /-model.mat. This model can be used to % test novel images independently of the Caltech data. % % load('data/baseline-model.mat') ; # change to the model path % label = model.classify(model, im) ; % % AUTORIGHTS conf.calDir = 'data/caltech-101' ; conf.dataDir = 'data/' ; conf.autoDownloadData = true ; conf.numTrain = 15 ; conf.numTest = 15 ; conf.numClasses = 102 ; conf.numWords = 600 ; conf.numSpatialX = [2 4] ; conf.numSpatialY = [2 4] ; conf.quantizer = 'kdtree' ; conf.svm.C = 10 ; conf.svm.solver = 'pegasos' ; conf.svm.biasMultiplier = 1 ; conf.phowOpts = {'Step', 3} ; conf.clobber = false ; conf.tinyProblem = true ; conf.prefix = 'baseline' ; conf.randSeed = 1 ; if conf.tinyProblem conf.prefix = 'tiny' ; conf.numClasses = 5 ; conf.numSpatialX = 2 ; conf.numSpatialY = 2 ; conf.numWords = 300 ; conf.phowOpts = {'Verbose', 2, 'Sizes', 7, 'Step', 5} ; end conf.vocabPath = fullfile(conf.dataDir, [conf.prefix '-vocab.mat']) ; conf.histPath = fullfile(conf.dataDir, [conf.prefix '-hists.mat']) ; conf.modelPath = fullfile(conf.dataDir, [conf.prefix '-model.mat']) ; conf.resultPath = fullfile(conf.dataDir, [conf.prefix '-result']) ; randn('state',conf.randSeed) ; rand('state',conf.randSeed) ; vl_twister('state',conf.randSeed) ; % -------------------------------------------------------------------- % Download Caltech-101 data % -------------------------------------------------------------------- if ~exist(conf.calDir, 'dir') || ... (~exist(fullfile(conf.calDir, 'airplanes'),'dir') && ... ~exist(fullfile(conf.calDir, '101_ObjectCategories', 'airplanes'))) if ~conf.autoDownloadData error(... ['Caltech-101 data not found. ' ... 'Set conf.autoDownloadData=true to download the required data.']) ; end vl_xmkdir(conf.calDir) ; calUrl = ['http://www.vision.caltech.edu/Image_Datasets/' ... 'Caltech101/101_ObjectCategories.tar.gz'] ; fprintf('Downloading Caltech-101 data to ''%s''. This will take a while.', conf.calDir) ; untar(calUrl, conf.calDir) ; end if ~exist(fullfile(conf.calDir, 'airplanes'),'dir') conf.calDir = fullfile(conf.calDir, '101_ObjectCategories') ; end % -------------------------------------------------------------------- % Setup data % -------------------------------------------------------------------- classes = dir(conf.calDir) ; classes = classes([classes.isdir]) ; classes = {classes(3:conf.numClasses+2).name} ; images = {} ; imageClass = {} ; for ci = 1:length(classes) ims = dir(fullfile(conf.calDir, classes{ci}, '*.jpg'))' ; ims = vl_colsubset(ims, conf.numTrain + conf.numTest) ; ims = cellfun(@(x)fullfile(classes{ci},x),{ims.name},'UniformOutput',false) ; images = {images{:}, ims{:}} ; imageClass{end+1} = ci * ones(1,length(ims)) ; end selTrain = find(mod(0:length(images)-1, conf.numTrain+conf.numTest) < conf.numTrain) ; selTest = setdiff(1:length(images), selTrain) ; imageClass = cat(2, imageClass{:}) ; model.classes = classes ; model.phowOpts = conf.phowOpts ; model.numSpatialX = conf.numSpatialX ; model.numSpatialY = conf.numSpatialY ; model.quantizer = conf.quantizer ; model.vocab = [] ; model.w = [] ; model.b = [] ; model.classify = @classify ; % -------------------------------------------------------------------- % Train vocabulary % -------------------------------------------------------------------- if ~exist(conf.vocabPath) || conf.clobber % Get some PHOW descriptors to train the dictionary selTrainFeats = vl_colsubset(selTrain, 30) ; descrs = {} ; %for ii = 1:length(selTrainFeats) parfor ii = 1:length(selTrainFeats) im = imread(fullfile(conf.calDir, images{selTrainFeats(ii)})) ; im = standarizeImage(im) ; [drop, descrs{ii}] = vl_phow(im, model.phowOpts{:}) ; end descrs = vl_colsubset(cat(2, descrs{:}), 10e4) ; descrs = single(descrs) ; % Quantize the descriptors to get the visual words vocab = vl_kmeans(descrs, conf.numWords, 'verbose', 'algorithm', 'elkan') ; save(conf.vocabPath, 'vocab') ; else load(conf.vocabPath) ; end model.vocab = vocab ; if strcmp(model.quantizer, 'kdtree') model.kdtree = vl_kdtreebuild(vocab) ; end % -------------------------------------------------------------------- % Compute spatial histograms % -------------------------------------------------------------------- if ~exist(conf.histPath) || conf.clobber hists = {} ; parfor ii = 1:length(images) % for ii = 1:length(images) fprintf('Processing %s (%.2f %%)\n', images{ii}, 100 * ii / length(images)) ; im = imread(fullfile(conf.calDir, images{ii})) ; hists{ii} = getImageDescriptor(model, im); end hists = cat(2, hists{:}) ; save(conf.histPath, 'hists') ; else load(conf.histPath) ; end % -------------------------------------------------------------------- % Compute feature map % -------------------------------------------------------------------- psix = vl_homkermap(hists, 1, 'kchi2', 'gamma', .5) ; % -------------------------------------------------------------------- % Train SVM % -------------------------------------------------------------------- if ~exist(conf.modelPath) || conf.clobber switch conf.svm.solver case 'pegasos' lambda = 1 / (conf.svm.C * length(selTrain)) ; w = [] ; % for ci = 1:length(classes) parfor ci = 1:length(classes) perm = randperm(length(selTrain)) ; fprintf('Training model for class %s\n', classes{ci}) ; y = 2 * (imageClass(selTrain) == ci) - 1 ; w(:,ci) = vl_pegasos(psix(:,selTrain(perm)), ... int8(y(perm)), lambda, ... 'NumIterations', 50/lambda, ... 'BiasMultiplier', conf.svm.biasMultiplier) ; end case 'liblinear' svm = train(imageClass(selTrain)', ... sparse(double(psix(:,selTrain))), ... sprintf(' -s 3 -B %f -c %f', ... conf.svm.biasMultiplier, conf.svm.C), ... 'col') ; w = svm.w' ; end model.b = conf.svm.biasMultiplier * w(end, :) ; model.w = w(1:end-1, :) ; save(conf.modelPath, 'model') ; else load(conf.modelPath) ; end % -------------------------------------------------------------------- % Test SVM and evaluate % -------------------------------------------------------------------- % Estimate the class of the test images scores = model.w' * psix + model.b' * ones(1,size(psix,2)) ; [drop, imageEstClass] = max(scores, [], 1) ; % Compute the confusion matrix idx = sub2ind([length(classes), length(classes)], ... imageClass(selTest), imageEstClass(selTest)) ; confus = zeros(length(classes)) ; confus = vl_binsum(confus, ones(size(idx)), idx) ; % Plots figure(1) ; clf; subplot(1,2,1) ; imagesc(scores(:,[selTrain selTest])) ; title('Scores') ; set(gca, 'ytick', 1:length(classes), 'yticklabel', classes) ; subplot(1,2,2) ; imagesc(confus) ; title(sprintf('Confusion matrix (%.2f %% accuracy)', ... 100 * mean(diag(confus)/conf.numTest) )) ; print('-depsc2', [conf.resultPath '.ps']) ; save([conf.resultPath '.mat'], 'confus', 'conf') ; % ------------------------------------------------------------------------- function im = standarizeImage(im) % ------------------------------------------------------------------------- im = im2single(im) ; if size(im,1) > 480, im = imresize(im, [480 NaN]) ; end % ------------------------------------------------------------------------- function hist = getImageDescriptor(model, im) % ------------------------------------------------------------------------- im = standarizeImage(im) ; width = size(im,2) ; height = size(im,1) ; numWords = size(model.vocab, 2) ; % get PHOW features [frames, descrs] = vl_phow(im, model.phowOpts{:}) ; % quantize appearance switch model.quantizer case 'vq' [drop, binsa] = min(vl_alldist(model.vocab, single(descrs)), [], 1) ; case 'kdtree' binsa = double(vl_kdtreequery(model.kdtree, model.vocab, ... single(descrs), ... 'MaxComparisons', 15)) ; end for i = 1:length(model.numSpatialX) binsx = vl_binsearch(linspace(1,width,model.numSpatialX(i)+1), frames(1,:)) ; binsy = vl_binsearch(linspace(1,height,model.numSpatialY(i)+1), frames(2,:)) ; % combined quantization bins = sub2ind([model.numSpatialY(i), model.numSpatialX(i), numWords], ... binsy,binsx,binsa) ; hist = zeros(model.numSpatialY(i) * model.numSpatialX(i) * numWords, 1) ; hist = vl_binsum(hist, ones(size(bins)), bins) ; hists{i} = single(hist / sum(hist)) ; end hist = cat(1,hists{:}) ; hist = hist / sum(hist) ; % ------------------------------------------------------------------------- function [className, score] = classify(model, im) % ------------------------------------------------------------------------- hist = getImageDescriptor(model, im) ; psix = vl_homkermap(hist, 1, .7, 'kchi2') ; scores = model.w' * psix + model.b' ; [score, best] = max(scores) ; className = model.classes{best} ; vlfeat-0.9.14/apps/sift_mosaic.m000066400000000000000000000110151167540017400165120ustar00rootroot00000000000000function mosaic = sift_mosaic(im1, im2) % SIFT_MOSAIC Demonstrates matching two images using SIFT and RANSAC % % SIFT_MOSAIC demonstrates matching two images based on SIFT % features and RANSAC and computing their mosaic. % % SIFT_MOSAIC by itself runs the algorithm on two standard test % images. Use SIFT_MOSAIC(IM1,IM2) to compute the mosaic of two % custom images IM1 and IM2. % AUTORIGHTS if nargin == 0 im1 = imread(fullfile(vl_root, 'data', 'river1.jpg')) ; im2 = imread(fullfile(vl_root, 'data', 'river2.jpg')) ; end % make single im1 = im2single(im1) ; im2 = im2single(im2) ; % make grayscale if size(im1,3) > 1, im1g = rgb2gray(im1) ; else im1g = im1 ; end if size(im2,3) > 1, im2g = rgb2gray(im2) ; else im2g = im2 ; end % -------------------------------------------------------------------- % SIFT matches % -------------------------------------------------------------------- [f1,d1] = vl_sift(im1g) ; [f2,d2] = vl_sift(im2g) ; [matches, scores] = vl_ubcmatch(d1,d2) ; numMatches = size(matches,2) ; X1 = f1(1:2,matches(1,:)) ; X1(3,:) = 1 ; X2 = f2(1:2,matches(2,:)) ; X2(3,:) = 1 ; % -------------------------------------------------------------------- % RANSAC with homography model % -------------------------------------------------------------------- clear H score ok ; for t = 1:100 % estimate homograpyh subset = vl_colsubset(1:numMatches, 4) ; A = [] ; for i = subset A = cat(1, A, kron(X1(:,i)', vl_hat(X2(:,i)))) ; end [U,S,V] = svd(A) ; H{t} = reshape(V(:,9),3,3) ; % score homography X2_ = H{t} * X1 ; du = X2_(1,:)./X2_(3,:) - X2(1,:)./X2(3,:) ; dv = X2_(2,:)./X2_(3,:) - X2(2,:)./X2(3,:) ; ok{t} = (du.*du + dv.*dv) < 6*6 ; score(t) = sum(ok{t}) ; end [score, best] = max(score) ; H = H{best} ; ok = ok{best} ; % -------------------------------------------------------------------- % Optional refinement % -------------------------------------------------------------------- function err = residual(H) u = H(1) * X1(1,ok) + H(4) * X1(2,ok) + H(7) ; v = H(2) * X1(1,ok) + H(5) * X1(2,ok) + H(8) ; d = H(3) * X1(1,ok) + H(6) * X1(2,ok) + 1 ; du = X2(1,ok) - u ./ d ; dv = X2(2,ok) - v ./ d ; err = sum(du.*du + dv.*dv) ; end if exist('fminsearch') == 2 H = H / H(3,3) ; opts = optimset('Display', 'none', 'TolFun', 1e-8, 'TolX', 1e-8) ; H(1:8) = fminsearch(@residual, H(1:8)', opts) ; else warning('Refinement disabled as fminsearch was not found.') ; end % -------------------------------------------------------------------- % Show matches % -------------------------------------------------------------------- dh1 = max(size(im2,1)-size(im1,1),0) ; dh2 = max(size(im1,1)-size(im2,1),0) ; figure(1) ; clf ; subplot(2,1,1) ; imagesc([padarray(im1,dh1,'post') padarray(im2,dh2,'post')]) ; o = size(im1,2) ; line([f1(1,matches(1,:));f2(1,matches(2,:))+o], ... [f1(2,matches(1,:));f2(2,matches(2,:))]) ; title(sprintf('%d tentative matches', numMatches)) ; axis image off ; subplot(2,1,2) ; imagesc([padarray(im1,dh1,'post') padarray(im2,dh2,'post')]) ; o = size(im1,2) ; line([f1(1,matches(1,ok));f2(1,matches(2,ok))+o], ... [f1(2,matches(1,ok));f2(2,matches(2,ok))]) ; title(sprintf('%d (%.2f%%) inliner matches out of %d', ... sum(ok), ... 100*sum(ok)/numMatches, ... numMatches)) ; axis image off ; drawnow ; % -------------------------------------------------------------------- % Mosaic % -------------------------------------------------------------------- box2 = [1 size(im2,2) size(im2,2) 1 ; 1 1 size(im2,1) size(im2,1) ; 1 1 1 1 ] ; box2_ = inv(H) * box2 ; box2_(1,:) = box2_(1,:) ./ box2_(3,:) ; box2_(2,:) = box2_(2,:) ./ box2_(3,:) ; ur = min([1 box2_(1,:)]):max([size(im1,2) box2_(1,:)]) ; vr = min([1 box2_(2,:)]):max([size(im1,1) box2_(2,:)]) ; [u,v] = meshgrid(ur,vr) ; im1_ = vl_imwbackward(im2double(im1),u,v) ; z_ = H(3,1) * u + H(3,2) * v + H(3,3) ; u_ = (H(1,1) * u + H(1,2) * v + H(1,3)) ./ z_ ; v_ = (H(2,1) * u + H(2,2) * v + H(2,3)) ./ z_ ; im2_ = vl_imwbackward(im2double(im2),u_,v_) ; mass = ~isnan(im1_) + ~isnan(im2_) ; im1_(isnan(im1_)) = 0 ; im2_(isnan(im2_)) = 0 ; mosaic = (im1_ + im2_) ./ mass ; figure(2) ; clf ; imagesc(mosaic) ; axis image off ; title('Mosaic') ; if nargout == 0, clear mosaic ; end end vlfeat-0.9.14/bin/000077500000000000000000000000001167540017400136435ustar00rootroot00000000000000vlfeat-0.9.14/bin/glnx86/000077500000000000000000000000001167540017400147715ustar00rootroot00000000000000vlfeat-0.9.14/bin/glnx86/aib000077500000000000000000000176401167540017400154620ustar00rootroot00000000000000ELF44 ("444hhhhh$(|||((( Qtd/lib/ld-linux.so.2GNU   yz  k6) rF~$/ CR libvl.so_initvl_aib_deletevl_aib_new_fini_Jv_RegisterClassesvl_aib_process__gmon_start__libc.so.6printfmemcpyputs_IO_stdin_used__libc_start_main_edata__bss_start_end$ORIGIN/GLIBC_2.0aii Tdhlptx |U5\%`%dh%hh%lh%ph%th %xh(%|h01^PTRh}h,QVh USëtЃ[]Ë$U=t ҡuUxtt $xÐU@W1VS L$\$$ $0t&$8\$Nu$<yu$B1f$EZ D$T$$Zlj$w,'\$C$RD$Hu<$ $\e1[^_]ÐUWV1S E)9sE)9r [^_]U]=u})u:]u}]ÐUShthЋCu[]ÐUS[[]>yX5?N@a?_LJ?}?5^I?K=U?&S?u?{P?V-??|a2U?6[ ?NbX9?37?S?HPs?Q?Ciq?t$~? 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t.EGFʋ MM;Et݋U [^)_]à 1[^_]ÐUVSOÚ\P@tЋFu[^]USNgN[]aib: (%5d,%5d)=%5d dE: %10.3g I: %6.4g H: %6.4g updt: %5d XAAAmAmAAAAAAA?4lx:>w??-DT!@0.9.14no$tIVLFeat version %s Static config: %s %d CPU(s): %s Debug: %s %s: illegal option -- %s %s: illegal option -- %c =%s: option requires an argument -- %s %s: option requires an argument -- %c hikmeans: branch at depth %d: %6.1f %% completed B?@E@q= ףp@333333 @(\@(\@? ףp= @ffffff@(\@!@333333)@(\B#@q= ףp@Gz?)\( @{Gz?m0_?-DT! @-DT! @@GNU C %d ILP32POSIX_threads, SSE2IX86, little_endian%s, %s, %s SSE42 SSE41 SSE3 SSE MMX%s%s%s%s%s%s%sikm: training with %d data ikm: %d clusters ikm: Elkan algorithm: total iterations: %d ikm: Elkan algorithm: distance calculations: %d (speedup: %.2f) %ss)J+Q+!+!+C+C+J+J+Q+Q+=kmeans: Lloyd iter %d: energy = %g kmeans: Lloyd terminating because maximum number of iterations reached kmeans: Lloyd terminating because the algorithm fully converged kmeans: Lloyd iter %d: restarted %d centers kmeans: Elkan iter 0: energy = %g, dist. calc. = %d kmeans: Elkan iter %d: energy <= %g, dist. calc. = %d kmeans: Elkan iter %d: restarted %d centers kmeans: Elkan iter %d: total dist. calc. per type: UB: %.1f%% (%d), LB: %.1f%% (%d), intra_center: %.1f%% (%d), new_center: %.1f%% (%d) kmeans: Elkan terminating because maximum number of iterations reached kmeans: Elkan terminating because the algorithm fully converged kmeans: Elkan: total dist. calc.: %d (%.2f %% of Lloyd) kmeans: Elkan: there have been %d restarts kmeans: Elkan: total dist. calc. per type: init: %.1f%% (%d), UB: %.1f%% (%d), LB: %.1f%% (%d), intra_center: %.1f%% (%d), new_center: %.1f%% (%d), finalize: %.1f%% (%d) kmeans: K-means initialized in %.2f s kmeans: K-means termineted in %.2f s with energy %g kmeans: repetition %d of %d .:!;;; ;;::::>;F;N;8;8;);V;^;;<;;;;;;;;<&<.<<< <6<><@@ A'dddddddddd*f*fdd*fdddddddddddddddddd*fdd0fddddddddddeeddeddddddddddddddddddeddeddddddddddeeddeddddddddddddddddddedde%d %udInvalid PGM dataP5 %d %d %d Error writing PGM datawbrbError opening PGM file for writingError opening PGM file `%s' for readingvl_pgm_read(): PGM with BPP > 1 not supportedCư>Ƚ@@AI?@B9B.?333333?333333??|=?@;f?{Gz$@:0yE>?9@B@h㈵>://asciibin c` ' -   8*(oH(oo`&o..&.6.F.V.f.v.........//&/6/F/V/f/v/////////00&060F0V0f0v00000000011&161F1V1f1v11111111122&262F2V2f2v22222222233&363F3V3f3v33333333344&464F4V4f4|߰GCC: (GNU) 3.4.6 (Ubuntu 3.4.6-1ubuntu2)GCC: (GNU) 4.0.3 (Ubuntu 4.0.3-1ubuntu5)GCC: (GNU) 4.0.3 (Ubuntu 4.0.3-1ubuntu5)GCC: (GNU) 4.0.3 (Ubuntu 4.0.3-1ubuntu5)GCC: (GNU) 4.0.3 (Ubuntu 4.0.3-1ubuntu5)GCC: (GNU) 4.0.3 (Ubuntu 4.0.3-1ubuntu5)GCC: (GNU) 4.0.3 (Ubuntu 4.0.3-1ubuntu5)GCC: (GNU) 4.0.3 (Ubuntu 4.0.3-1ubuntu5)GCC: (GNU) 4.0.3 (Ubuntu 4.0.3-1ubuntu5)GCC: (GNU) 4.0.3 (Ubuntu 4.0.3-1ubuntu5)GCC: (GNU) 4.0.3 (Ubuntu 4.0.3-1ubuntu5)GCC: (GNU) 4.0.3 (Ubuntu 4.0.3-1ubuntu5)GCC: (GNU) 4.0.3 (Ubuntu 4.0.3-1ubuntu5)GCC: (GNU) 4.0.3 (Ubuntu 4.0.3-1ubuntu5)GCC: (GNU) 4.0.3 (Ubuntu 4.0.3-1ubuntu5)GCC: (GNU) 4.0.3 (Ubuntu 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initfini.c/build/buildd/glibc-2.3.6/build-tree/i386-libc/csu/crti.Scall_gmon_startcrtstuff.c__CTOR_LIST____DTOR_LIST____JCR_LIST__completed.4463p.4462__do_global_dtors_auxframe_dummy__CTOR_END____DTOR_END____FRAME_END____JCR_END____do_global_ctors_aux/build/buildd/glibc-2.3.6/build-tree/i386-libc/csu/crtn.Saib.cvl_nan_darray.c.L34.L35.L36.L37.L38dsift.c_vl_dsift_free_buffersgeneric.cdo_nothing_printfvl_destructorvl_constructorgetopt_long.coptend.2735optbegin.2734place.2733hikmeans.cxdeletexmeanshomkermap.chost.cikmeans.ccmp_pairimopv.cvl_infinity_dvl_infinity_fimopv_sse2.ckdtree.cvl_nan_fvl_kdtree_compare_index_entriesvl_kdforest_neighbor_heap_upvl_kdforest_search_heap_upvl_kdtree_split_heap_upvl_kdforest_search_heap_downvl_kdforest_neighbor_heap_downvl_kdtree_split_heap_downvl_kdtree_build_recursivelyvl_kdtree_calc_bounds_recursivelykmeans.c_vl_kmeans_quantize_d_vl_kmeans_quantize_f_vl_kmeans_d_qsort_sort_recursive_vl_kmeans_f_qsort_sort_recursive_vl_kmeans_refine_centers_lloyd_d.L24.L25.L26.L27.L28.L16.L17.L18.L19.L1241.L1242.L1243.L1244.L1245lbp.cmathop.c.L229.L230.L231.L232.L233.L234.L235.L236.L237.L238.L244.L245.L246.L241.L247.L248.L249.L254.L255.L256.L257.L258.L259.L260.L261.L262.L263.L269.L270.L271.L266.L272.L273.L274mathop_sse2.cmser.cpegasos.cpgm.c.L21.L20.L22.L23.L31.L30.L32.L33.L41.L40.L42.L43quickshift.crandom.cmag01.2050rodrigues.csift.c_vl_sift_smoothupdate_gradientslic.cdy.3589dx.3588stringop.c__i686.get_pc_thunk.cxvl_aib_merge_nodes_DYNAMIC_vl_dsift_new_kernel__dso_handlevl_hikm_copy_subsetexpn_tabvl_aib_update_betavl_aib_new_Pc_vl_x86cpu_info_to_string_copyvl_aib_new_Pxvl_aib_new_nodelistvl_aib_normalize_P_vl_x86cpu_info_init__i686.get_pc_thunk.bx_GLOBAL_OFFSET_TABLE_vl_aib_calculate_informationvl_aib_min_betavl_set_printf_funcpthread_cond_wait@@GLIBC_2.3.2vl_homogeneouskernelmap_deletevl_imconvcol_vdpthread_getspecific@@GLIBC_2.0cos@@GLIBC_2.0vl_kmeans_refine_centersfgetc@@GLIBC_2.0_vl_kernel_l2_d_vl_dsift_update_buffersvl_homogeneouskernelmap_newvl_lbp_process_vl_distance_l1_dvl_pgm_insertstrchr@@GLIBC_2.0getopt_longvl_array_get_num_elementsvl_pegasos_train_binary_svm_dungetc@@GLIBC_2.0vl_dsift_new_basicvl_eval_vector_comparison_on_all_pairs_f__ctype_tolower_loc@@GLIBC_2.3lround@@GLIBC_2.1vl_array_newvl_array_init_vl_kernel_l1_sse2_dvl_image_distance_transform_f_vl_kernel_l2_sse2_fvl_slic_segmentvl_ikm_deletevl_array_new_matrix_envelopevl_array_init_matrix_vl_distance_js_dfprintf@@GLIBC_2.0vl_pgm_writevl_dsift_deletepowf@@GLIBC_2.0clock@@GLIBC_2.0frexp@@GLIBC_2.0vl_kmeans_deletevl_sift_detectvl_string_basename_vl_distance_l2_sse2_dvl_sift_newoptargvl_array_new_matrix_vl_kernel_chi2_fvl_string_protocol_namevsnprintf@@GLIBC_2.0vl_ikm_trainvl_set_alloc_funcvl_rand_uint32vl_ikm_new_vl_stateopterrvl_hikm_deletevl_mser_deletevl_kdforest_delete_vl_kernel_chi2_sse2_dpow@@GLIBC_2.0_vl_kernel_l1_dvl_pgm_read_newvl_static_configuration_to_string_copy_vl_imconvcol_vd_sse2expf@@GLIBC_2.0vl_pgm_write_fvl_kmeans_cluster_initvl_thread_specific_state_newvl_kdforest_querymalloc@@GLIBC_2.0vl_mser_newpthread_mutex_init@@GLIBC_2.0fscanf@@GLIBC_2.0vl_ikm_initpthread_mutex_destroy@@GLIBC_2.0vl_sift_calc_keypoint_descriptorvl_hikm_pushvl_aib_deletefread@@GLIBC_2.0_vl_kernel_hellinger_dvl_tocvl_array_init_matrix_envelopevl_rand_initlog2@@GLIBC_2.1_vl_distance_js_fstderr@@GLIBC_2.0abort@@GLIBC_2.0vl_get_rand_vl_distance_l2_sse2_f_vl_kernel_chi2_sse2_fsysconf@@GLIBC_2.0vl_rand_seed_by_array_vl_distance_chi2_sse2_doptind_vl_kernel_l1_sse2_fvl_string_replace_wildcardvl_aib_new_vl_distance_l2_d_vl_distance_chi2_fvl_pgm_read_new_fvl_sift_calc_raw_descriptorvl_rodriguesvl_dsift_newvl_irodriguespthread_mutex_lock@@GLIBC_2.0vl_imintegral_dvl_sift_process_next_octave_vl_kernel_l2_sse2_dvl_kdforest_new_vl_distance_l1_fstrstr@@GLIBC_2.0strlen@@GLIBC_2.0vl_imintegral_ui32vl_pegasos_train_binary_svm_fvl_imintegral_fvl_mser_process_vl_kernel_chi2_dqsort@@GLIBC_2.0pthread_setspecific@@GLIBC_2.0pthread_key_create@@GLIBC_2.0_vl_kernel_hellinger_fpthread_cond_signal@@GLIBC_2.3.2log2f@@GLIBC_2.1_vl_kernel_l2_fvl_sift_deletestrncmp@@GLIBC_2.0_vl_distance_l1_sse2_dvl_quickshift_deletefloor@@GLIBC_2.0vl_kdforest_query_recursivelyvl_kmeans_seed_centers_with_rand_data__bss_startpthread_self@@GLIBC_2.0vl_get_cpu_timevl_set_last_errorvl_array_new_envelopevl_pgm_extract_datapthread_cond_init@@GLIBC_2.3.2vl_kdforest_buildexp@@GLIBC_2.0vl_lbp_deletevl_string_casei_cmpsqrtf@@GLIBC_2.0vl_kmeans_set_centerspthread_key_delete@@GLIBC_2.0realloc@@GLIBC_2.0vl_kmeans_seed_centers_plus_plusprintf@@GLIBC_2.0_finimemcpy@@GLIBC_2.0sqrt@@GLIBC_2.0_vl_distance_hellinger_fvl_homogeneouskernelmap_evaluate_fvl_kmeans_quantizefclose@@GLIBC_2.1optreset__cxa_finalize@@GLIBC_2.1.3vl_hikm_trainsnprintf@@GLIBC_2.0_vl_distance_l1_sse2_fvl_quickshift_processvl_ikm_pushvl_kmeans_new_copyvl_sift_calc_keypoint_orientationsvl_eval_vector_comparison_on_all_pairs_dvl_imconvcoltri_dvl_pgm_get_bppvl_string_find_char_revvl_pgm_extract_headvl_lock_state_vl_kernel_js_fexit@@GLIBC_2.0_vl_distance_chi2_sse2_fcalloc@@GLIBC_2.0vl_sift_keypoint_initvl_hikm_newvl_pgm_get_npixels_vl_distance_chi2_d_edatafree@@GLIBC_2.0_endvl_imconvcoltri_fvl_string_parse_protocolvl_configuration_to_string_copyvl_lbp_newvl_string_lengthacos@@GLIBC_2.0memset@@GLIBC_2.0vl_sift_process_first_octavevl_homogeneouskernelmap_evaluate_dpthread_cond_destroy@@GLIBC_2.3.2vl_array_deletevl_kmeans_resetvl_image_distance_transform_dfopen@@GLIBC_2.1vl_get_vector_comparison_function_fvl_unlock_statevl_hikm_initpthread_mutex_unlock@@GLIBC_2.0vl_string_copyvl_thread_specific_state_deletevl_quickshift_newvl_rand_seedvl_imintegral_i32pthread_equal@@GLIBC_2.0_vl_kernel_js_dfwrite@@GLIBC_2.0vl_mser_ell_fitsin@@GLIBC_2.0vl_dsift_process_Jv_RegisterClassesvl_string_copy_subvl_aib_processvl_ikm_init_randvl_get_version_stringlog@@GLIBC_2.0vl_kmeans_new_vl_distance_hellinger_dvl_array_init_envelopevl_ikm_push_oneoptopt@@GLIBC_2.0vl_ticvl_get_vector_comparison_function_d_vl_kernel_l1_f_vl_imconvcol_vf_sse2_vl_distance_l2_fvl_array_dealloc__gmon_start__ceil@@GLIBC_2.0ldexp@@GLIBC_2.0vl_ikm_init_rand_datavl_imconvcol_vfvlfeat-0.9.14/bin/glnx86/mser000077500000000000000000000604361167540017400156760ustar00rootroot00000000000000ELF4N4 ("444XDXDXDXX`lDll((( Qtd/lib/ld-linux.so.2GNU%1.&0'(,+#-*)/ %!  $ " I B!)07HRzaX gQ ]x"6, F8@ 1q4 9 2JVdZiy  libvl.sogetopt_longvl_string_basenameoptargvl_string_protocol_name_vl_statevl_mser_delete_initvl_thread_specific_state_newvl_mser_newoptindvl_string_replace_wildcardvl_mser_processvl_pgm_extract_data_finivl_pgm_get_bppvl_pgm_extract_headvl_lock_statevl_pgm_get_npixelsvl_string_parse_protocolvl_string_lengthvl_unlock_statevl_string_copyvl_mser_ell_fit_Jv_RegisterClassesvl_get_version_string__gmon_start__libc.so.6snprintfmemcpyputsmallocabortfprintfsscanffclosestderrfputcfwriteexitfopen_IO_stdin_used__libc_start_mainfreelibpthread.so.0pthread_getspecificpthread_setspecific_edata__bss_start_end$ORIGIN/GLIBC_2.1GLIBC_2.00ii ii 9ii L0\`dhlpt x |   !"$&'()+,-/U5T%X%\h%`h%dh%hh%lh %ph(%th0%xh8p%|h@`%hHP%hP@%hX0%h` %hh%hp%hx%h%h%h%h%h%h%h%hp%h`%hP%h@%h0%h %h%h%h%h%h%h%h1^PTRhhdQVhpUScHtЃ[]Ë$U=t ҡuUhtt $hÐU(]É}ωuu1]u}]1%T$D$CL$ D$T$4$w=wZt|$4$u$tn\$$Kfa$Tt&\$$ (#Ð뉐t&U WVS P}D$u UUUU]EE\$$` T$L$$ D$@D$|$1('1D$D$ PD$t$<$L=;dvv=t&hx=0[=t&ED$UD$$GtE (|\$|$$vE$D$UtM|BF‰$\$T$$=DD$ D$D$$D$\$ D$eD$$$=t&|tDE$D$UW|BF‰$aw:?٢D\$L$$D$ ED$UD$$3tE LD$E؉D$UD$$tEDD$ D$Zt&=%ED$YD$$t }D$D$$ t&EȺUD$T$$CtE $D$똋$`D$wD$$aD$$KD$$5P$7D$3D$${EYD$L$$~t }pT$EЉD$UD$$CtE $\$L$$DT$D$ y(E$D$U"|BF‰$|D$\$$=VD$\$$=yt&9)Ǎ`duE‰($\$pD$#D$$ $/$D$$3$pD$D$$$$D$$ţ|$pD$wD$$a$$D$C11$P+((~\$$ &D$<$L)ڍ8)ڍ{ )ڍ|Pp(~*|LED$$uv(E$U$7؉$\ D$LUT$$ \|$\$$1ɉ4 $%4t4$ L$t1$kt1ۉ$Qt$91ɉPP9d<`<\$t$ $|$=ף|$ L$T$$,D$ `|$\$$LQD$t$ T$$:$|$$ +D$D$$낸<D$D$|$ $V$e[^_]øF뛋E]\$$EEE\$$,,҉0@8EVٽn,nf fl٭lߝj٭njBXE,X\E,ZdE,XlE,ZtEٽn0nf fl٭lߝj٭njBXE0X\E<0ZdE0XlE0Zt([UE1҉4|1=,t,$00$ D$$JD$$n!D$$SE$D$ED$4  ~,D$1ۉ|$$ 4 ؐt&r؉U؉$p,BX$D$,@\$\$l,Bd$\$S,@l$֤\$:,Bt$\$!؍vU1$wÍE$*؉$O4& D24ЈD2E$F+9׋440T$$10B,r4 mser: err: %s (%d) %s: error: %s (%d) hvphud{ʢvhd:Usage: %s [options] files ... Options include: --verbose -v Be verbose --help -h Print this help message --seeds Specify seeds file --frames Specify frames file --meta Specify meta file --delta -d Specify MSER delta paramter --epsilon -e Specify MSER epsilon parameter --no-dups Remove duplicate --dups Keep duplicates --max-area Specify maximum region (relative) area --min-area Specify minimum region (relative) area --max-variation Specify maximum absolute region stability --bright-on-dark Enable or disable bright-on-dark regions (default 1) --dark-on-bright Enable or disable dark-on-bright regions (default 1) %.meta%.mser%.frameMissing mandatory argument for option '%s'.The argument of '%s' must be a non-negative number.max-area argument must be in the [0,1] range.min-area argument must be in the [0,1] range.max-variation argument must be non-negative.min-diversity argument must be in the [0,1] range.The arguments of '%s' is invalid.meta file supports only ASCII protocolbright_on_dark must be 0 or 1.dark_on_bright must be 0 or 1.Could not open '%s' for reading.Could not open '%s' for writing.mser: writing seeds to '%s' mser: writing frames to '%s' mser: writing meta to '%s' mser: image is %d by %d pixels Could not allocate enough memory.Could not create an MSER filter.9 X ,H  P 8(ooo^lƊ֊&6FVfvƋ֋&6FVfvdGCC: (GNU) 3.4.6 (Ubuntu 3.4.6-1ubuntu2)GCC: (GNU) 3.4.6 (Ubuntu 3.4.6-1ubuntu2)GCC: (GNU) 4.0.3 (Ubuntu 4.0.3-1ubuntu5)GCC: (GNU) 4.0.3 (Ubuntu 4.0.3-1ubuntu5)GCC: (GNU) 3.4.6 (Ubuntu 3.4.6-1ubuntu2)GCC: (GNU) 4.0.3 (Ubuntu 4.0.3-1ubuntu5)GCC: (GNU) 3.4.6 (Ubuntu 3.4.6-1ubuntu2)"4 ,X $,Cm!zy_IO_stdin_usedv../sysdeps/i386/elf/start.S/build/buildd/glibc-2.3.6/build-tree/glibc-2.3.6/csuGNU AS 2.16.91[bTVintFK]dOV/build/buildd/glibc-2.3.6/build-tree/i386-libc/csu/crti.S/build/buildd/glibc-2.3.6/build-tree/glibc-2.3.6/csuGNU AS 2.16.91f//build/buildd/glibc-2.3.6/build-tree/i386-libc/csu/crtn.S/build/buildd/glibc-2.3.6/build-tree/glibc-2.3.6/csuGNU AS 2.16.91%% $ > $ > 4: ; I?  &I%%W2 ../sysdeps/i386/elfstart.S3!4=%" YZ!"\[# init.cP /build/buildd/glibc-2.3.6/build-tree/i386-libc/csucrti.S=,.!/!YgX!/= !/!Yg=g//0=!!P /build/buildd/glibc-2.3.6/build-tree/i386-libc/csucrtn.S!=C=!!m !init.cshort int/build/buildd/glibc-2.3.6/build-tree/glibc-2.3.6/csulong long intunsigned charGNU C 3.4.6 (Ubuntu 3.4.6-1ubuntu2)long long unsigned intshort unsigned int_IO_stdin_used.symtab.strtab.shstrtab.interp.note.ABI-tag.hash.dynsym.dynstr.gnu.version.gnu.version_r.rel.dyn.rel.plt.init.text.fini.rodata.eh_frame.ctors.dtors.jcr.dynamic.got.got.plt.data.bss.comment.debug_aranges.debug_pubnames.debug_info.debug_abbrev.debug_line.debug_str#(( 1HH`7 ?Go^^bToPc  (l 88  uXX ppp P{ l,,"``"! TTDXXD``DhhDllDLLEPPEE EE& GG%G+IvnJ0)LL'`S!C P[(H^8 X p  , `TX`hlLP ! (/:t X`h  ; \d Th$ /:t{`  @ `  lXXXPX1:BF`M!`sz7J `zX Q&8IPk r dQ p P  6&, ,F>P8ds@1 '28VHdWfx  abi-note.S../sysdeps/i386/elf/start.Sinit.cinitfini.c/build/buildd/glibc-2.3.6/build-tree/i386-libc/csu/crti.Scall_gmon_startcrtstuff.c__CTOR_LIST____DTOR_LIST____JCR_LIST__completed.4463p.4462__do_global_dtors_auxframe_dummy__CTOR_END____DTOR_END____FRAME_END____JCR_END____do_global_ctors_aux/build/buildd/glibc-2.3.6/build-tree/i386-libc/csu/crtn.Smser.cvl_file_meta_openC.15.4021C.14.4020C.13.4019_DYNAMIC__fini_array_end__fini_array_start__init_array_end_GLOBAL_OFFSET_TABLE___init_array_startpthread_getspecific@@GLIBC_2.0longoptsgetopt_long_fp_hwfprintf@@GLIBC_2.0vl_string_basenameoptargvl_string_protocol_name__dso_handle__libc_csu_fini_vl_statevl_mser_deletehelp_messageputs@@GLIBC_2.0_initvl_thread_specific_state_newmalloc@@GLIBC_2.0vl_mser_newstderr@@GLIBC_2.0abort@@GLIBC_2.0optindvl_string_replace_wildcard_startvl_mser_processpthread_setspecific@@GLIBC_2.0fputc@@GLIBC_2.0__libc_csu_init__bss_startmainvl_pgm_extract_data__libc_start_main@@GLIBC_2.0optsdata_startprintf@@GLIBC_2.0_finimemcpy@@GLIBC_2.0fclose@@GLIBC_2.1snprintf@@GLIBC_2.0vl_pgm_get_bppvl_pgm_extract_headvl_lock_stateexit@@GLIBC_2.0vl_pgm_get_npixelssscanf@@GLIBC_2.0_edata__i686.get_pc_thunk.bxfree@@GLIBC_2.0_endvl_string_parse_protocolvl_string_lengthfopen@@GLIBC_2.1vl_unlock_state_IO_stdin_usedvl_string_copyfwrite@@GLIBC_2.0vl_mser_ell_fit__data_start_Jv_RegisterClassesvl_get_version_string__gmon_start__vlfeat-0.9.14/bin/glnx86/sift000077500000000000000000001111321167540017400156630ustar00rootroot00000000000000ELF4}4 ("444ssss((( Qtd/lib/ld-linux.so.2GNU%:7!/90&265,)43 8(.%*  $# '-+1" }Bu!$3FRhY7qpcz  {QhU! o Od}Hv6 \FF8 +CR@ft}1d)(^2V - libvl.sovl_pgm_insertgetopt_longvl_sift_detectvl_string_basenamevl_sift_newoptargvl_string_protocol_name_vl_statevl_thread_specific_state_newvl_sift_calc_keypoint_descriptoroptindvl_string_replace_wildcardvl_sift_process_next_octavevl_sift_deletevl_pgm_extract_data_finivl_sift_calc_keypoint_orientationsvl_pgm_get_bppvl_pgm_extract_headvl_lock_statevl_sift_keypoint_initvl_pgm_get_npixelsvl_string_parse_protocolvl_string_lengthvl_sift_process_first_octavevl_unlock_statevl_string_copy_Jv_RegisterClassesvl_get_version_string__gmon_start__libc.so.6snprintfqsortfscanfmemcpyputsmallocabortfprintfreallocsscanffreadfclosestderrfputcfwriteexitfopen_IO_stdin_used__libc_start_mainfreelibpthread.so.0pthread_getspecificpthread_setspecific_edata__bss_start_end$ORIGIN/GLIBC_2.1GLIBC_2.0<0ii 7ii Aii A9hp     ! "#$%& '$((),*0+4-8/<0@1D2H3L5P6T8Uog*5%%h%h%h%h%h %h(%h0%h8p%h@`%hHP%hP@%hX0%h` %hh%hp%hx%h%h%h%h%h%h%h%hp%h`%hP% h@%h0%h %h%h% h%$h%(h%,h%0h%4h %8h(%<h0%@h8p%Dh@`%HhHP%LhP@%PhX0%Th` 1^PTRh=hQVhcUStЃ[]Ë$U=$t `ҡ`u$Utt $ÐUE @Ehɸw1&؍]Í&'U(]É}ωuu1]u}]1%T$D$CL$ D$T$4$=wZt|$4$Bup$tnp\$$aep$ht&p\$$q#Ð뉐t&U  WVS?}4u UU]ȉL$T$EEE$R (D$$D$2 D$ $D$ D$$ D$L$\$$ T$D$$1111D$D$ \$t$<$H=;Sv*?O&:o=@=t&vEЉD$MD$h${tE2 DD$ <h(E$D$GUtM|B,F‰,$.\$,\$$K= XDT$D$ @D$$#D$\$ D$D$$z$>=JhE$D$UUW|B F‰ $8 \$T$$U=o:&Zv&=t&htDE$D$U|BF‰$}|D$@D$$pE؉D$MD$h$tE DD$ D$ ED$JD$h$t Ed DD$ @D$|$$h$D$8D$$z,D$$m D$$WD$$ѷAD$$+D$$ P$,D$(D$$k=<=AEJD$h\$$X DD$ ^t& DD$ ,D$_hE$D$U|BF‰$\$L$$=bh4tDE$D$U|B8F‰8$pEȉD$MD$h$ E eDD$ @L$T$$@ED$JD$h$EWD$8\$$=&t&hE$D$LUN|BF‰$/D$\$$L= 9W)Ǎu(u t14D$Qjn4$qDƉD$8$m$vD$m8D$$|W$K($qDƉD$.,$$vD$,D$$|$$qDƉD$ $$vD$ D$$|$$qDƉD$t$V$vD$VD$$|@$θ4$qDƉD$$$vD$D$$|$$qDމ\$$$vD$D$$|119D8AUy ؃E\$]L$$EEEEEEEEEEEEEEEED$D$$_ t&e[^_]Í`\$D$$0 9K $(hL$$@jED$T$$C,0hD$$X@ED$D$$3SPL@P[ sift: err: %s (%d) %s: error: %s (%d) vhOSo Q -9ELXvhO:S:o:Usage: %s [options] files ... Options include: --verbose -v Be verbose --help -h Print this help message --output -o Specify output file --frames Specify frames file --descriptors Specify descriptors file --meta Specify meta file --gss Specify Gaussian scale space files --octaves -O Number of octaves --levels -S Number of levels per octave --first-octave Index of the first octave --edge-thresh Specify the edge threshold --peak-thresh Specift the peak threshold --magnif Specify the magnification factor --read-frames Specify a file from which to read frames --orientations Force the computation of the orientations %.frame%.pgm%.meta%.descr%.frame%.siftMissing mandatory argument for option '%s'.The arguments of '%s' is invalid.meta file supports only ASCII protocolThe argument of '%s' must be a non-negative integer.The argument of '%s' must be an integer.The argument of '%s' must be not smaller than 1.The argument of '%s' must be a non-negative float.sift: will compute orientationsCould not open '%s' for reading.'%s' contains a malformed PGM header.sift: image is %d by %d pixels Could not allocate enough memory.Could not open '%s' for readingsift: read %d keypoints from '%s' Could not open '%s' for writingsift: writing all ....... to . '%s' sift: writing frames .... to . '%s' sift: writing descriptors to . '%s' sift: writign meta ...... to . '%s' sift: octaves (O) = %d sift: levels (S) = %d sift: first octave (o_min) = %d sift: edge thresh = %g sift: peak thresh = %g sift: magnif = %g sift: will force orientations? %s sift: saved gss level to '%s' Could not write GSS to PGM file.sift: detected %d (unoriented) keypoints DC<.  Hl̂ K h|(o,oo:JZjzʌڌ *:JZjzʍڍ *:JZjzʎڎGCC: (GNU) 3.4.6 (Ubuntu 3.4.6-1ubuntu2)GCC: (GNU) 3.4.6 (Ubuntu 3.4.6-1ubuntu2)GCC: (GNU) 4.0.3 (Ubuntu 4.0.3-1ubuntu5)GCC: (GNU) 4.0.3 (Ubuntu 4.0.3-1ubuntu5)GCC: (GNU) 3.4.6 (Ubuntu 3.4.6-1ubuntu2)GCC: (GNU) 4.0.3 (Ubuntu 4.0.3-1ubuntu5)GCC: (GNU) 3.4.6 (Ubuntu 3.4.6-1ubuntu2)"4 X  4$,X˶!!zy_IO_stdin_usedv2../sysdeps/i386/elf/start.S/build/buildd/glibc-2.3.6/build-tree/glibc-2.3.6/csuGNU AS 2.16.91[44bTVintFK]OV/build/buildd/glibc-2.3.6/build-tree/i386-libc/csu/crti.S/build/buildd/glibc-2.3.6/build-tree/glibc-2.3.6/csuGNU AS 2.16.91f//build/buildd/glibc-2.3.6/build-tree/i386-libc/csu/crtn.S/build/buildd/glibc-2.3.6/build-tree/glibc-2.3.6/csuGNU AS 2.16.91%% $ > $ > 4: ; I?  &I%%W2 ../sysdeps/i386/elfstart.S3!4=%" YZ!"\[# init.cP /build/buildd/glibc-2.3.6/build-tree/i386-libc/csucrti.SX=.!/!Yg !/=4 !/!Yg=g//0=!!P /build/buildd/glibc-2.3.6/build-tree/i386-libc/csucrtn.SX!=˶=!!! !init.cshort int/build/buildd/glibc-2.3.6/build-tree/glibc-2.3.6/csulong long intunsigned charGNU C 3.4.6 (Ubuntu 3.4.6-1ubuntu2)long long unsigned intshort unsigned int_IO_stdin_used.symtab.strtab.shstrtab.interp.note.ABI-tag.hash.dynsym.dynstr.gnu.version.gnu.version_r.rel.dyn.rel.plt.init.text.fini.rodata.eh_frame.ctors.dtors.jcr.dynamic.got.got.plt.data.bss.comment.debug_aranges.debug_pubnames.debug_info.debug_abbrev.debug_line.debug_str#(( 1HH7 ̂?llKGo tTo,, Pc || (l  h u  p$$ {'66< sssssttXXu hdudu&vw%=w+hyvy0{X|'Ђ!F (Ĥl,|  $   Xh ! 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pqinitfini.c/build/buildd/glibc-2.3.6/build-tree/amd64-libc/csu/crti.Scall_gmon_startcrtstuff.c__CTOR_LIST____DTOR_LIST____JCR_LIST__completed.4801p.4800__do_global_dtors_auxframe_dummy__CTOR_END____DTOR_END____FRAME_END____JCR_END____do_global_ctors_aux/build/buildd/glibc-2.3.6/build-tree/amd64-libc/csu/crtn.Saib.cvl_nan_darray.cdsift.c_vl_dsift_free_buffersgeneric.cdo_nothing_printfvl_destructorvl_constructorgetopt_long.coptend.3083optbegin.3082place.3081hikmeans.cxdeletexmeanshomkermap.chost.cikmeans.ccmp_pairimopv.cvl_infinity_dvl_infinity_fimopv_sse2.ckdtree.cvl_nan_fvl_kdtree_compare_index_entriesvl_kdforest_neighbor_heap_upvl_kdtree_split_heap_upvl_kdforest_search_heap_upvl_kdforest_search_heap_downvl_kdforest_neighbor_heap_downvl_kdtree_split_heap_downvl_kdtree_build_recursivelyvl_kdtree_calc_bounds_recursivelykmeans.c_vl_kmeans_quantize_d_vl_kmeans_quantize_f_vl_kmeans_d_qsort_sort_recursive_vl_kmeans_f_qsort_sort_recursive_vl_kmeans_refine_centers_lloyd_dlbp.cmathop.cmathop_sse2.cmser.cpegasos.cpgm.cquickshift.crandom.cmag01.2387rodrigues.csift.c_vl_sift_smoothupdate_gradientslic.cdy.3937dx.3936stringop.cvl_aib_merge_nodes_DYNAMIC_vl_dsift_new_kernel__dso_handlevl_hikm_copy_subsetexpn_tabvl_aib_update_betavl_aib_new_Pc_vl_x86cpu_info_to_string_copyvl_aib_new_Pxvl_aib_new_nodelistvl_aib_normalize_P_vl_x86cpu_info_init_GLOBAL_OFFSET_TABLE_vl_aib_calculate_informationvl_aib_min_betapthread_key_create@@GLIBC_2.2.5pthread_self@@GLIBC_2.2.5vl_set_printf_funcpthread_cond_wait@@GLIBC_2.3.2vl_homogeneouskernelmap_deletevl_imconvcol_vdvl_kmeans_refine_centerspowf@@GLIBC_2.2.5_vl_kernel_l2_d_vl_dsift_update_buffersvl_homogeneouskernelmap_newvl_lbp_processexpf@@GLIBC_2.2.5pthread_mutex_destroy@@GLIBC_2.2.5_vl_distance_l1_dfloor@@GLIBC_2.2.5vl_pgm_insertgetopt_longvl_array_get_num_elementsvl_pegasos_train_binary_svm_dvl_dsift_new_basicvl_eval_vector_comparison_on_all_pairs_f__ctype_tolower_loc@@GLIBC_2.3sqrtf@@GLIBC_2.2.5vl_array_newvl_array_init_vl_kernel_l1_sse2_dvl_image_distance_transform_f_vl_kernel_l2_sse2_ffscanf@@GLIBC_2.2.5vl_slic_segmentvl_ikm_deletevl_array_new_matrix_envelopevl_array_init_matrix_vl_distance_js_dvl_pgm_writevl_dsift_deletemalloc@@GLIBC_2.2.5strncmp@@GLIBC_2.2.5vl_kmeans_deletevl_sift_detectvl_string_basename_vl_distance_l2_sse2_dvl_sift_newoptargvl_array_new_matrix_vl_kernel_chi2_flround@@GLIBC_2.2.5vl_string_protocol_namevl_ikm_trainvl_set_alloc_funcvl_rand_uint32vl_ikm_new_vl_stateopterrpthread_setspecific@@GLIBC_2.2.5vl_hikm_deleteceil@@GLIBC_2.2.5vl_mser_deletevl_kdforest_deleteclock@@GLIBC_2.2.5_vl_kernel_chi2_sse2_dfwrite@@GLIBC_2.2.5fclose@@GLIBC_2.2.5stderr@@GLIBC_2.2.5_vl_kernel_l1_dpthread_mutex_lock@@GLIBC_2.2.5vl_pgm_read_newvl_static_configuration_to_string_copy_vl_imconvcol_vd_sse2vl_pgm_write_fvl_kmeans_cluster_initqsort@@GLIBC_2.2.5log@@GLIBC_2.2.5vl_thread_specific_state_newvl_kdforest_querystrlen@@GLIBC_2.2.5vl_mser_newvl_ikm_initvl_sift_calc_keypoint_descriptorvl_hikm_pushacos@@GLIBC_2.2.5vl_aib_deletefgetc@@GLIBC_2.2.5_vl_kernel_hellinger_dfread@@GLIBC_2.2.5vl_tocvl_array_init_matrix_envelopevl_rand_initfprintf@@GLIBC_2.2.5_vl_distance_js_fmemcpy@@GLIBC_2.2.5snprintf@@GLIBC_2.2.5vl_get_rand_vl_distance_l2_sse2_f_vl_kernel_chi2_sse2_fvl_rand_seed_by_arrayabort@@GLIBC_2.2.5_vl_distance_chi2_sse2_doptind_vl_kernel_l1_sse2_fvl_string_replace_wildcardvl_aib_new_vl_distance_l2_d_vl_distance_chi2_fvl_pgm_read_new_fvl_sift_calc_raw_descriptorvl_rodriguesrealloc@@GLIBC_2.2.5vl_dsift_newvl_irodriguesvl_imintegral_dvl_sift_process_next_octave_vl_kernel_l2_sse2_dvl_kdforest_new_vl_distance_l1_fvl_imintegral_ui32vl_pegasos_train_binary_svm_fvl_imintegral_fvl_mser_process_vl_kernel_chi2_dsqrt@@GLIBC_2.2.5_vl_kernel_hellinger_fpthread_equal@@GLIBC_2.2.5pthread_cond_signal@@GLIBC_2.3.2printf@@GLIBC_2.2.5_vl_kernel_l2_fvl_sift_deleteoptopt@@GLIBC_2.2.5_vl_distance_l1_sse2_dvl_quickshift_deletevl_kdforest_query_recursivelyvl_kmeans_seed_centers_with_rand_datafrexp@@GLIBC_2.2.5__bss_startpthread_key_delete@@GLIBC_2.2.5vl_get_cpu_timevl_set_last_errorvl_array_new_envelopevl_pgm_extract_datapthread_cond_init@@GLIBC_2.3.2vl_kdforest_build__cxa_finalize@@GLIBC_2.2.5log2@@GLIBC_2.2.5vl_lbp_deletevl_string_casei_cmpstrchr@@GLIBC_2.2.5memset@@GLIBC_2.2.5vl_kmeans_set_centersvsnprintf@@GLIBC_2.2.5vl_kmeans_seed_centers_plus_plus_fini_vl_distance_hellinger_fvl_homogeneouskernelmap_evaluate_fvl_kmeans_quantizeoptresetpthread_mutex_init@@GLIBC_2.2.5log2f@@GLIBC_2.2.5exit@@GLIBC_2.2.5vl_hikm_train_vl_distance_l1_sse2_fvl_quickshift_processvl_ikm_pushldexp@@GLIBC_2.2.5vl_kmeans_new_copysin@@GLIBC_2.2.5ungetc@@GLIBC_2.2.5vl_sift_calc_keypoint_orientationsvl_eval_vector_comparison_on_all_pairs_dvl_imconvcoltri_dvl_pgm_get_bppvl_string_find_char_revvl_pgm_extract_headcalloc@@GLIBC_2.2.5vl_lock_state_vl_kernel_js_f_vl_distance_chi2_sse2_fvl_sift_keypoint_initvl_hikm_newvl_pgm_get_npixels_vl_distance_chi2_d_edata_endvl_imconvcoltri_fvl_string_parse_protocolvl_configuration_to_string_copyvl_lbp_newvl_string_lengthvl_sift_process_first_octavevl_homogeneouskernelmap_evaluate_dpthread_mutex_unlock@@GLIBC_2.2.5pthread_cond_destroy@@GLIBC_2.3.2vl_array_deletevl_kmeans_resetpthread_getspecific@@GLIBC_2.2.5pow@@GLIBC_2.2.5vl_image_distance_transform_dfree@@GLIBC_2.2.5cos@@GLIBC_2.2.5vl_get_vector_comparison_function_ffopen@@GLIBC_2.2.5vl_unlock_statevl_hikm_initsysconf@@GLIBC_2.2.5vl_string_copyvl_thread_specific_state_deletevl_quickshift_newexp@@GLIBC_2.2.5vl_rand_seedstrstr@@GLIBC_2.2.5vl_imintegral_i32_vl_kernel_js_dvl_mser_ell_fitvl_dsift_process_Jv_RegisterClassesvl_string_copy_subvl_aib_processvl_ikm_init_randvl_get_version_stringvl_kmeans_new_vl_distance_hellinger_dvl_array_init_envelopevl_ikm_push_onevl_ticvl_get_vector_comparison_function_d_vl_kernel_l1_f_vl_imconvcol_vf_sse2_vl_distance_l2_fvl_array_dealloc__gmon_start__vl_ikm_init_rand_datavl_imconvcol_vfvlfeat-0.9.14/bin/glnxa64/mser000077500000000000000000000703701167540017400160310ustar00rootroot00000000000000ELF>@@V@8@# 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"# < S)PNP0+H`NPNRr1@OPa (@g4faHOP%iHNP &@ bHNPuPOP 9:%  J4Z[iy  libvl.sogetopt_longvl_string_basenameoptargvl_string_protocol_name_vl_statevl_mser_delete_initvl_thread_specific_state_newvl_mser_newoptindvl_string_replace_wildcardvl_mser_processvl_pgm_extract_data_finivl_pgm_get_bppvl_pgm_extract_headvl_lock_statevl_pgm_get_npixelsvl_string_parse_protocolvl_string_lengthvl_unlock_statevl_string_copyvl_mser_ell_fit_Jv_RegisterClassesvl_get_version_string__gmon_start__libc.so.6snprintfmemcpyputsmallocfcloseabortfprintfsscanfstderrfputcfwriteexitfopen__libc_start_mainfreelibpthread.so.0pthread_getspecificpthread_setspecific_edata__bss_start_end$ORIGIN/GLIBC_2.2.5* ui ui LP/PNP`NP@OP HOPMPMP MP(MP0MP8MP@MP HMP PMP XMP`MPhMPpMPxMPMPMPMPMPMPMPMPMPMPMPMP MP!MP$MP%MP&MP'NP(NP)NP*NP+ NP,(NP.HuH5=%=%=h%=h%=h%=h%=h%=h%=h%=hp%z=h`%r=h P%j=h @%b=h 0%Z=h %R=h %J=h%B=h%:=h%2=h%*=h%"=h%=h%=h% =h%=hp%<h`%<hP%<h@%<h0%<h %<h%<h%<h%<h %<h!%<h"%<h#H1I^HHPTIp&@H&@Hp@HH);HtHÐ=e=UHt$HHE<H<<HHu9=ffffffffUH=8HtHt (KPIAÐH\$Ld$IHl$HHu1H$Hl$Ld$HHHWIE1%HH=wZtLHHHu=;DHHtp=v;H1Wh1=J;HHt%=7;Ho1)1H1HfffffffffffAWHAVI?AUATAUH?@SH!H$L$H$ H$ H$ L$ H$ H$ DŽ$ DŽ$ 6@LH$.@H$IGD$4HHD$(HT$ fffE1 *@+@HD=fffCdfff*vfffff'=fff hk=3J=fffH=]9H$ 1'@It(f$ fWf.wLt$fL$f.H$H@1HH=8DŽ$HLy$ HÃtH|B$F‰$HYHH|$ HھH=Hcs9H$@H@HHL1*H=C9HUAHپ*@1=H=8DŽ$HtEL$ HÃk|B$F‰$H|H$H$hH@1Hi:fff:?fffHck8H$u'@HHL1"H=F7H$ 1'@2tfWf.$ H$G@1HH=6H$ 1'@tfWf.$ Hc7H$@G@HHL1pA=H=6H$ 1'@ut$ cH$H@1HfH=96H$ 1'@%t(f$ fWf.wLt$fL$f.H$G@1H~Hu+@1Ht$('@1Ht$ '@1H$'@1H1'@H¾'@11H=e5H$ 1'@Qt$ ?H$H@1HH=5H$ 1'@t(f$ fWf.wLt$fL$f.H$xG@1H\ffffHc5H$G@HHL1X)D$4zH=s4DŽ$H_L$ HÃ|B$ F‰$ HHH|$(HھH=ufffffH$HھHH=Dffff4A9$A)HDd$tLlu$E‰$|$4'@*$ (@1wH$"(@1H_$s8(@H1DN(@$ (@1'Iw"(@1$ +8(@H1a(@$ (@1H$"(@1H$8(@H1D$0E1D9t$tMeH$IHLH=TD\$4El|$4~H޿(@1A(@LHHD$hm(@HL4Iu7H$(@H YH$HH$H@1`H|$h7H$HtHDŽ$H$HtHDŽ$H$HtHDŽ$uAH=2H$)@1AD$0pH$Ls(@1GL濐(@1H$(@1n H$LH@JD$0H![]A\A]A^A_H$(@HJt|$4~3D$EsD$EBD$EH|$hH$ _|$4H$ ^H$ HcHHD$H|$hH$ HtaH$(@1BHD$HH|$?H|$Ht H|$H-H|$hH$H$ H$ Hމ$ $ $ H޿HD$8H|$8HD$@HTf$ fWf.r,HT$8f$ f.r HL$8f$ f.r HD$8f$ f.r HT$8f$ f.r HL$8f$ f.r,HT$@f$ f.r HL$@f$ f.r HD$@f$ f.r HT$@f$ f.r HL$@t$4$ $ HD$H/D$1EH|$8t H|$8H|$@H|$@H$(@1$ $ I@1H$`I@1HH$@I@1HI I@1uH$I@LH$I@1.HD$HH$)@H$1L⾹)@D$E$pH$)@1O{ )@HD$8)@1HT$8:)@fHL$8V)@fHD$8r)@fHT$8)@fhH$ H$ Hc1:HD$HffHL$HcHL$HЈH$ {9|H|$H+Ht$HH|$@HD$@$HT$@@dۉD$XHBXtL$X1HŅD$EH|$@HL$@HD$@HT$@D$|HjxɉL$`D$d)H$ vD$|L$|9L$`DT$dE~1ZEH$)@H9\$du릋UH$1)@H9\$Xu)fffHt$H|$8HT$8HL$8HBX$Id҉L$Ttt$T1HÅ$H|$8HL$8HD$8HT$8D$xHjxɉL$\D$p)H$ ]D$xL$x9L$\D$p~1ZEH$)@H9\$pu먋H$1)@Hk9l$Tu0H$I@1b.H$H$)@1HhH$I)@19H\$Ld$L%$Hl$H1H$L)HH9sHAHH9rH$Hl$Ld$HfffffffUH$H-$H)SHHXHTHHuH[]?UHSHHH$HtKPfffHCHHuH[ÐHHverbosehelpdeltaseedsframesmetamax-areamin-areamax-variationmin-diversitybright-on-darkdark-on-brightInvalid option '%s'.MSERs filespec: `%s' Frames filespec: `%s' Meta filespec: `%s' 0.2Version: driver %s; libvl %s %lf%dmser: frames outputmser: active %d mser: pattern %s mser: protocol %s mser: seeds outputmser: meta outputBasename of '%s' is too longmser: processing '%s' mser: basename is '%s' rbwOutput file name too long.PGM header corrputed.PGM body corrputed.mser: parameters:mser: delta = %d mser: max_area = %g mser: min_area = %g mser: max_variation = %g mser: min_diversity = %g %d %f mser: err: %s (%d) %s: error: %s (%d) '@v '@h'@d'@'@$'@)'@2'@;'@I'@W'@f'@vhd:Usage: %s [options] files ... Options include: --verbose -v Be verbose --help -h Print this help message --seeds Specify seeds file --frames Specify frames file --meta Specify meta file --delta -d Specify MSER delta paramter --epsilon -e Specify MSER epsilon parameter --no-dups Remove duplicate --dups Keep duplicates --max-area Specify maximum region (relative) area --min-area Specify minimum region (relative) area --max-variation Specify maximum absolute region stability --bright-on-dark Enable or disable bright-on-dark regions (default 1) --dark-on-bright Enable or disable dark-on-bright regions (default 1) %.meta%.mser%.frameMissing mandatory argument for option '%s'.The argument of '%s' must be a non-negative number.max-area argument must be in the [0,1] range.min-area argument must be in the [0,1] range.max-variation argument must be non-negative.min-diversity argument must be in the [0,1] range.The arguments of '%s' is invalid.meta file supports only ASCII protocolbright_on_dark must be 0 or 1.dark_on_bright must be 0 or 1.Could not open '%s' for reading.Could not open '%s' for writing.mser: writing seeds to '%s' mser: writing frames to '%s' mser: writing meta to '%s' mser: image is %d by %d pixels Could not allocate enough memory.Could not create an MSER filter.;,XHxpxx $P@JL <Dp@BQL B(D0I8GBzRx HVJP <9ARL *z (@ &@@@ @@  LP` @P @x o @oo @0KPV@f@v@@@@@@@@@@@&@6@F@V@f@v@@@@@@@@@@@&@6@F@V@f@v@@ KPGCC: (GNU) 3.4.6 (Ubuntu 3.4.6-1ubuntu2)GCC: (GNU) 3.4.6 (Ubuntu 3.4.6-1ubuntu2)GCC: (GNU) 4.0.3 (Ubuntu 4.0.3-1ubuntu5)GCC: (GNU) 4.0.3 (Ubuntu 4.0.3-1ubuntu5)GCC: (GNU) 3.4.6 (Ubuntu 3.4.6-1ubuntu2)GCC: (GNU) 4.0.3 (Ubuntu 4.0.3-1ubuntu5)GCC: (GNU) 3.4.6 (Ubuntu 3.4.6-1ubuntu2),@+L&@(@ @<&@;@!s_IO_stdin_used@@../sysdeps/x86_64/elf/start.S/build/buildd/glibc-2.3.6/build-tree/glibc-2.3.6/csuGNU AS 2.16.91a@@]OQintFX '@WV/build/buildd/glibc-2.3.6/build-tree/amd64-libc/csu/crti.S/build/buildd/glibc-2.3.6/build-tree/glibc-2.3.6/csuGNU AS 2.16.91f#/build/buildd/glibc-2.3.6/build-tree/amd64-libc/csu/crtn.S/build/buildd/glibc-2.3.6/build-tree/glibc-2.3.6/csuGNU AS 2.16.91%% $ > $ > 4: ; I?  &I%%]4 ../sysdeps/x86_64/elfstart.S @<>!>L$ uvx[# init.cQ /build/buildd/glibc-2.3.6/build-tree/amd64-libc/csucrti.S &@$ (@K @ Ku=/0K}Q /build/buildd/glibc-2.3.6/build-tree/amd64-libc/csucrtn.S &@K ;@Kinit.cshort int/build/buildd/glibc-2.3.6/build-tree/glibc-2.3.6/csulong intunsigned charGNU C 3.4.6 (Ubuntu 3.4.6-1ubuntu2)short unsigned intlong unsigned int_IO_stdin_used.symtab.strtab.shstrtab.interp.note.ABI-tag.hash.dynsym.dynstr.gnu.version.gnu.version_r.rela.dyn.rela.plt.init.text.fini.rodata.eh_frame_hdr.eh_frame.ctors.dtors.jcr.dynamic.got.got.plt.data.bss.comment.debug_aranges.debug_pubnames.debug_info.debug_abbrev.debug_line.debug_str@#@ 1@@@\7 @? @ Go @ `To @ @cP @P xm @ ` w(@(r@@@P}@X&@&'@'" I@I,(J@(JKPKKPK(KP(K0KP0KLPLLPL80NP0NPNPHNHN&pO0P%UP5Rv S,0TYU7P_ "D  k@@@@@ @ @ @P @ @ (@ @@ @ &@'@I@(J@KPKP(KP0KPLPLP0NPPNP !" *1<w @KPKP(KPLOP@NP @ @KP KP K@(KP' &@1=x P@.@6@?@0KPKPKPKPLPKP<5 *@>SJ^qPNPx+8NP p&@9`NP+@r1@OP0 (@64S_tfaHOP @% &@V"HNP. p@3G+@L 0NPW &@]pHNPPOP/A T4d'@s[0NP  abi-note.S../sysdeps/x86_64/elf/start.Sinit.cinitfini.c/build/buildd/glibc-2.3.6/build-tree/amd64-libc/csu/crti.Scall_gmon_startcrtstuff.c__CTOR_LIST____DTOR_LIST____JCR_LIST__completed.4801p.4800__do_global_dtors_auxframe_dummy__CTOR_END____DTOR_END____FRAME_END____JCR_END____do_global_ctors_aux/build/buildd/glibc-2.3.6/build-tree/amd64-libc/csu/crtn.Smser.cvl_file_meta_openC.15.4369C.14.4368C.13.4367_DYNAMIC__fini_array_end__fini_array_start__init_array_end_GLOBAL_OFFSET_TABLE___init_array_start__libc_start_main@@GLIBC_2.2.5longoptsgetopt_longmalloc@@GLIBC_2.2.5vl_string_basenameoptargvl_string_protocol_name__dso_handle__libc_csu_fini_vl_statepthread_setspecific@@GLIBC_2.2.5vl_mser_deletehelp_messagefwrite@@GLIBC_2.2.5fclose@@GLIBC_2.2.5stderr@@GLIBC_2.2.5_initvl_thread_specific_state_newvl_mser_newfprintf@@GLIBC_2.2.5memcpy@@GLIBC_2.2.5snprintf@@GLIBC_2.2.5abort@@GLIBC_2.2.5optindvl_string_replace_wildcard_startsscanf@@GLIBC_2.2.5vl_mser_processprintf@@GLIBC_2.2.5__libc_csu_init__bss_startmainvl_pgm_extract_dataoptsdata_start_finifputc@@GLIBC_2.2.5exit@@GLIBC_2.2.5vl_pgm_get_bppvl_pgm_extract_headvl_lock_statevl_pgm_get_npixels_edata_endvl_string_parse_protocolvl_string_lengthputs@@GLIBC_2.2.5pthread_getspecific@@GLIBC_2.2.5free@@GLIBC_2.2.5fopen@@GLIBC_2.2.5vl_unlock_state_IO_stdin_usedvl_string_copyvl_mser_ell_fit__data_start_Jv_RegisterClassesvl_get_version_string__gmon_start__vlfeat-0.9.14/bin/glnxa64/sift000077500000000000000000001241641167540017400160310ustar00rootroot00000000000000ELF>@@Ћ@8@# @@@@@@@@@}}PP((P(P@@ PtdX|X|@X|@44Qtd/lib64/ld-linux-x86-64.so.2GNU%96-!8.'350)417$2&*    #% ( +",/< uSUh$p3FRPY+qPro1P @O{4B|\fFvaP}P (>@ CRftPP5c 4[ - libvl.sovl_pgm_insertgetopt_longvl_sift_detectvl_string_basenamevl_sift_newoptargvl_string_protocol_name_vl_statevl_thread_specific_state_newvl_sift_calc_keypoint_descriptoroptindvl_string_replace_wildcardvl_sift_process_next_octavevl_sift_deletevl_pgm_extract_data_finivl_sift_calc_keypoint_orientationsvl_pgm_get_bppvl_pgm_extract_headvl_lock_statevl_sift_keypoint_initvl_pgm_get_npixelsvl_string_parse_protocolvl_string_lengthvl_sift_process_first_octavevl_unlock_statevl_string_copy_Jv_RegisterClassesvl_get_version_string__gmon_start__libc.so.6snprintfqsortfscanfmemcpyputsmallocfcloseabortfprintfreallocsscanffreadstderrfputcfwriteexitfopen__libc_start_mainfreelibpthread.so.0pthread_getspecificpthread_setspecific_edata__bss_start_end$ORIGIN/GLIBC_2.2.5 ui (<ui (P8P P PPPPP P(P0P8P@PHP PP XP `PhPpPxPPPPPPPPPPȂPЂP؂P!P#P$P%P&P'P(P)P* P-(P.0P/8P0@P1HP2PP3XP4`P5hP7H#-,H5*p%,p%*ph%"ph%ph%ph% ph%ph%oh%ohp%oh`%oh P%oh @%oh 0%oh %oh %oh%oh%oh%oh%oh%oh%oh%oh%zoh%rohp%joh`%bohP%Zoh@%Roh0%Joh %Boh%:oh%2oh%*oh %"oh!%oh"%oh#% oh$%oh%%nh&%nh'p%nh(`%nh)P%nh*@%nh+0%nh, H1I^HHPTI=@HP=@H@HHmHtHÐ=oUHt$HHenH\nHHuYoffffffffUH=jHtHt PIAÐfG\Ff. bzr 1f.affffH\$Ld$IHl$HHu1H$Hl$Ld$HHHWIE1%H!H=wZtLHXHHu=ymHHtp=fmH&1/h1x=:mHHt%='mH1)1 H1HfffffffffffAWHAVAUE1ATIo@USH?L$)H$?H$?H$?DŽ$?L$LDŽ$?DŽ$?H$ `g@@_@LH$ W@jH$O@SH$F@@1H$ >@1HwIv?@1gH$-?@1HOH$J?@1H7H$g?@1H1X?@H¾?@11==O=D$LBH= gH$?1?@HcgH$9hx@HIL1/fHcgH$9w@HIL1qH=fDŽ$HH$?$?HÃ|B$ F‰$ HH^H$H=fDŽ$)HtJH$?u$?HÃ|B$-F‰$-HQHAH=eH$?1?@RfYf.$?tffffHcafH$9>@HIL1!H= sift: err: %s (%d) %s: error: %s (%d) D>@vL>@hQ>@OY>@S`>@og>@>@l>@x>@|>@>@>@>@>@>@vhO:S:o:Usage: %s [options] files ... Options include: --verbose -v Be verbose --help -h Print this help message --output -o Specify output file --frames Specify frames file --descriptors Specify descriptors file --meta Specify meta file --gss Specify Gaussian scale space files --octaves -O Number of octaves --levels -S Number of levels per octave --first-octave Index of the first octave --edge-thresh Specify the edge threshold --peak-thresh Specift the peak threshold --magnif Specify the magnification factor --read-frames Specify a file from which to read frames --orientations Force the computation of the orientations %.frame%.pgm%.meta%.descr%.frame%.sift?@o@Missing mandatory argument for option '%s'.The arguments of '%s' is invalid.meta file supports only ASCII protocolThe argument of '%s' must be a non-negative integer.The argument of '%s' must be an integer.The argument of '%s' must be not smaller than 1.The argument of '%s' must be a non-negative float.sift: will compute orientationsCould not open '%s' for reading.'%s' contains a malformed PGM header.sift: image is %d by %d pixels Could not allocate enough memory.Could not open '%s' for readingsift: read %d keypoints from '%s' Could not open '%s' for writingsift: writing all ....... to . '%s' sift: writing frames .... to . '%s' sift: writing descriptors to . '%s' sift: writign meta ...... to . '%s' sift: octaves (O) = %d sift: levels (S) = %d sift: first octave (o_min) = %d sift: edge thresh = %g sift: peak thresh = %g sift: magnif = %g sift: will force orientations? %s sift: saved gss level to '%s' Could not write GSS to PGM file.sift: detected %d (unoriented) keypoints ;4PHhhXx p@*$4@JL <\@&BQB E(I0C8GzRx VJP <H9ARL < @ (>@@@ @@ 4 P8x @ @x o @ooL @(P@@@@@.@>@N@^@n@~@@@@@@@@@@@.@>@N@^@n@~@@@@@@@@@@@.@>@N@^@n@~@@@PGCC: (GNU) 3.4.6 (Ubuntu 3.4.6-1ubuntu2)GCC: (GNU) 3.4.6 (Ubuntu 3.4.6-1ubuntu2)GCC: (GNU) 4.0.3 (Ubuntu 4.0.3-1ubuntu5)GCC: (GNU) 4.0.3 (Ubuntu 4.0.3-1ubuntu5)GCC: (GNU) 3.4.6 (Ubuntu 3.4.6-1ubuntu2)GCC: (GNU) 4.0.3 (Ubuntu 4.0.3-1ubuntu5)GCC: (GNU) 3.4.6 (Ubuntu 3.4.6-1ubuntu2),@+L(>@@ @<1>@@!s_IO_stdin_used@@../sysdeps/x86_64/elf/start.S/build/buildd/glibc-2.3.6/build-tree/glibc-2.3.6/csuGNU AS 2.16.91a@@]OQintFX @>@WV/build/buildd/glibc-2.3.6/build-tree/amd64-libc/csu/crti.S/build/buildd/glibc-2.3.6/build-tree/glibc-2.3.6/csuGNU AS 2.16.91f#/build/buildd/glibc-2.3.6/build-tree/amd64-libc/csu/crtn.S/build/buildd/glibc-2.3.6/build-tree/glibc-2.3.6/csuGNU AS 2.16.91%% $ > $ > 4: ; I? 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 &I%%]4 ../sysdeps/x86_64/elfstart.S @<>!>L$ uvx[# init.cQ /build/buildd/glibc-2.3.6/build-tree/amd64-libc/csucrti.S @$ @K ,@ Ku=/0K}Q /build/buildd/glibc-2.3.6/build-tree/amd64-libc/csucrtn.S !@K @Kinit.cshort int/build/buildd/glibc-2.3.6/build-tree/glibc-2.3.6/csulong intunsigned charGNU C 3.4.6 (Ubuntu 3.4.6-1ubuntu2)short unsigned intlong unsigned int_IO_stdin_used.symtab.strtab.shstrtab.interp.note.ABI-tag.hash.dynsym.dynstr.gnu.version.gnu.version_r.rela.dyn.rela.plt.init.text.fini.rodata.eh_frame_hdr.eh_frame.ctors.dtors.jcr.dynamic.got.got.plt.data.bss.comment.debug_aranges.debug_pubnames.debug_info.debug_abbrev.debug_line.debug_str@#@ 1@@@@7 @?@Go@To(@( cH@Hm`@`H w@r@@}@@(@(@p@p$PPPP`P`hPh0PP&%5!v p",0$$7."H 7g@@@@@@@(@H@ `@ @ @ @ @(@@p@PPPP`PhPPP !" *1<w ,@PPPPP P@ @PP @P' @1=x @ @ @ @ P @ 0 @X @ @PPP'P8hPNPa< @ P @9 @ @ @ @ @@VP @ P @ @ PP((@7PD X abi-note.S../sysdeps/x86_64/elf/start.Sinit.cinitfini.c/build/buildd/glibc-2.3.6/build-tree/amd64-libc/csu/crti.Scall_gmon_startcrtstuff.c__CTOR_LIST____DTOR_LIST____JCR_LIST__completed.4801p.4800__do_global_dtors_auxframe_dummy__CTOR_END____DTOR_END____FRAME_END____JCR_END____do_global_ctors_aux/build/buildd/glibc-2.3.6/build-tree/amd64-libc/csu/crtn.Stest_heap-def.ctrack_s_heap_upvl_heap_float_ups_heap_uph_heap_uph_heap_downs_heap_downtrack_s_heap_downvl_heap_float_down_DYNAMIC__fini_array_end__fini_array_start__init_array_end_GLOBAL_OFFSET_TABLE___init_array_start__libc_start_main@@GLIBC_2.2.5h_cmp__dso_handle__libc_csu_fini_inits_cmps_swap_startprintf@@GLIBC_2.2.5__libc_csu_init__bss_startmaindata_start_finih_swap_edata_endputs@@GLIBC_2.2.5_IO_stdin_used__data_start_Jv_RegisterClasses__gmon_start__vlfeat-0.9.14/bin/glnxa64/test_host000077500000000000000000000225531167540017400170770ustar00rootroot00000000000000ELF> @@ @8@# @@@@@@@@@PP PP@@ Ptd@@$$Qtd/lib64/ld-linux-x86-64.so.2GNU  m<  P @ P @ P P @ T libvl.so_vl_state_init_finivl_configuration_to_string_copy_Jv_RegisterClasses__gmon_start__libc.so.6__libc_start_main_edata__bss_start_end$ORIGIN/GLIBC_2.2.5cui  P P P PHsH5%%h%hH1I^HHPTIp@H@H@HH9HtHÐ=IUHt$HHEH<HHuffffffffUH=HtHt PIAÐS1 HH1ZHq[1ÐH\$Ld$L%Hl$H1H|L)HH9sHAHH9rH$Hl$Ld$HfffffffUH<H-5H)SHHXHTHHuH[]?UHSHHHtPfffHCHHuH[ÐHH; <ttx @#AzRx VJP <9ARL c @ @@@@@  P0@p@0 oP@oo4@P@@PGCC: (GNU) 3.4.6 (Ubuntu 3.4.6-1ubuntu2)GCC: (GNU) 3.4.6 (Ubuntu 3.4.6-1ubuntu2)GCC: (GNU) 4.0.3 (Ubuntu 4.0.3-1ubuntu5)GCC: (GNU) 4.0.3 (Ubuntu 4.0.3-1ubuntu5)GCC: (GNU) 3.4.6 (Ubuntu 3.4.6-1ubuntu2)GCC: (GNU) 4.0.3 (Ubuntu 4.0.3-1ubuntu5)GCC: (GNU) 3.4.6 (Ubuntu 3.4.6-1ubuntu2), @+L@@ L@<@@!s_IO_stdin_used @K@../sysdeps/x86_64/elf/start.S/build/buildd/glibc-2.3.6/build-tree/glibc-2.3.6/csuGNU AS 2.16.91aL@L@]OQintFX @WV/build/buildd/glibc-2.3.6/build-tree/amd64-libc/csu/crti.S/build/buildd/glibc-2.3.6/build-tree/glibc-2.3.6/csuGNU AS 2.16.91f#/build/buildd/glibc-2.3.6/build-tree/amd64-libc/csu/crtn.S/build/buildd/glibc-2.3.6/build-tree/glibc-2.3.6/csuGNU AS 2.16.91%% $ > $ > 4: ; I?  &I%%]4 ../sysdeps/x86_64/elfstart.S  @<>!>L$ uvx[# init.cQ /build/buildd/glibc-2.3.6/build-tree/amd64-libc/csucrti.S @$ @K L@ Ku=/0K}Q /build/buildd/glibc-2.3.6/build-tree/amd64-libc/csucrtn.S @K @Kinit.cshort int/build/buildd/glibc-2.3.6/build-tree/glibc-2.3.6/csulong intunsigned charGNU C 3.4.6 (Ubuntu 3.4.6-1ubuntu2)short unsigned intlong unsigned int_IO_stdin_used.symtab.strtab.shstrtab.interp.note.ABI-tag.hash.dynsym.dynstr.gnu.version.gnu.version_r.rela.dyn.rela.plt.init.text.fini.rodata.eh_frame_hdr.eh_frame.ctors.dtors.jcr.dynamic.got.got.plt.data.bss.comment.debug_aranges.debug_pubnames.debug_info.debug_abbrev.debug_line.debug_str@#@ 1@@@@7 @?@Go4@4ToP@P cp@p0m@0 w@r@0} @ @@@$ @ PPPP P  P ( P  P  &  % 5v ,047"@ "@@@@@@4@P@p@ @ @ @ @ @@@ @PPPP P P P P !" *1<w L@PPP P P p@ @PP @P' @1=xPPPP PP<  P p@9' P1 @7 @> @VN PZ @#_  Pj @p Pw P|@ P  abi-note.S../sysdeps/x86_64/elf/start.Sinit.cinitfini.c/build/buildd/glibc-2.3.6/build-tree/amd64-libc/csu/crti.Scall_gmon_startcrtstuff.c__CTOR_LIST____DTOR_LIST____JCR_LIST__completed.4801p.4800__do_global_dtors_auxframe_dummy__CTOR_END____DTOR_END____FRAME_END____JCR_END____do_global_ctors_aux/build/buildd/glibc-2.3.6/build-tree/amd64-libc/csu/crtn.Stest_host.c_DYNAMIC__fini_array_end__fini_array_start__init_array_end_GLOBAL_OFFSET_TABLE___init_array_start__libc_start_main@@GLIBC_2.2.5__dso_handle__libc_csu_fini_vl_state_init_start__libc_csu_init__bss_startmaindata_start_fini_edata_endvl_configuration_to_string_copy_IO_stdin_used__data_start_Jv_RegisterClasses__gmon_start__vlfeat-0.9.14/bin/glnxa64/test_imopv000077500000000000000000000276001167540017400172520ustar00rootroot00000000000000ELF>@@@8@# @@@@@@@@@PPxh((P(P@@ Ptd @ @$$Qtd/lib64/ld-linux-x86-64.so.2GNU  < P# @)4FrMxP_ 8 @exP$hPs4 l qlibvl.so_vl_statevl_pgm_write_f_initvl_thread_specific_state_newvl_tocvl_pgm_read_new_f_finivl_lock_statevl_unlock_state_Jv_RegisterClassesvl_tic__gmon_start__vl_imconvcol_vflibc.so.6__libc_start_mainlibpthread.so.0pthread_getspecificpthread_setspecific_edata__bss_start_end$ORIGIN/GLIBC_2.2.5 ui 2ui 2PPPPP P(P0P8P @PHPPPXPHbMH5: %< %: h%2 h%* h%" h% h% h% h% hp% h`% h P% h @H1I^HHPTI @H` @H@ @HH1 HtHÐ= UHt$HH H| HHu] ffffffffUH=HtHt PIAÐAWHAVAUATUSHXbHxHT$HHt$0Dd$0l$4IcLcI݉D濰 @1W HHR HIF <I8 I1AD=HHu1l kHT$HEADLD$ D$D$D$L,$?u1Hh @1HT$HEADLD$ D$D$D$L,$u1HJ] @Ht$HD⿆ @DL @ DL @H|$HL LL1HX[]A\A]A^A_1L%=FHHu 1HË=)Hy11+= HHu 1oHË=H>1HsL @1AԸ\2E1IHD$H DDE1A<)ljB Hcɍ)Ѓƒ1Ѓ*YAuIIuAA(H\$Ld$L%Hl$H1HxL)HH9sHAHH9rH$Hl$Ld$HfffffffUH8H-1H)SHHXHTHHuH[]?UHSHHHtPfffHCHHuH[ÐHHtest_imopv: error: %s (%d) Elapsed time no SIMD: %f [s] /tmp/src.pgm/tmp/test.pgm/tmp/test2.pgmtest_imopv: width: %d, height: %d Elapsed time with SIMD: %f [s] C; D<dx <@ @BEB B(A0A8DzRx VJP <9ARL ) @ 8 @@@@@ > P@h@0 o(@oo@(P@@@@@@.@>@N@^@n@PGCC: (GNU) 3.4.6 (Ubuntu 3.4.6-1ubuntu2)GCC: (GNU) 3.4.6 (Ubuntu 3.4.6-1ubuntu2)GCC: (GNU) 4.0.3 (Ubuntu 4.0.3-1ubuntu5)GCC: (GNU) 4.0.3 (Ubuntu 4.0.3-1ubuntu5)GCC: (GNU) 3.4.6 (Ubuntu 3.4.6-1ubuntu2)GCC: (GNU) 4.0.3 (Ubuntu 4.0.3-1ubuntu5)GCC: (GNU) 3.4.6 (Ubuntu 3.4.6-1ubuntu2),@+L8 @@ @<A @@!s_IO_stdin_used@@../sysdeps/x86_64/elf/start.S/build/buildd/glibc-2.3.6/build-tree/glibc-2.3.6/csuGNU AS 2.16.91a@@]OQintFX H @WV/build/buildd/glibc-2.3.6/build-tree/amd64-libc/csu/crti.S/build/buildd/glibc-2.3.6/build-tree/glibc-2.3.6/csuGNU AS 2.16.91f#/build/buildd/glibc-2.3.6/build-tree/amd64-libc/csu/crtn.S/build/buildd/glibc-2.3.6/build-tree/glibc-2.3.6/csuGNU AS 2.16.91%% $ > $ > 4: ; I?  &I%%]4 ../sysdeps/x86_64/elfstart.S @<>!>L$ uvx[# init.cQ /build/buildd/glibc-2.3.6/build-tree/amd64-libc/csucrti.S 8 @$ @K @ Ku=/0K}Q /build/buildd/glibc-2.3.6/build-tree/amd64-libc/csucrtn.S A @K @Kinit.cshort int/build/buildd/glibc-2.3.6/build-tree/glibc-2.3.6/csulong intunsigned charGNU C 3.4.6 (Ubuntu 3.4.6-1ubuntu2)short unsigned intlong unsigned int_IO_stdin_used.symtab.strtab.shstrtab.interp.note.ABI-tag.hash.dynsym.dynstr.gnu.version.gnu.version_r.rela.dyn.rela.plt.init.text.fini.rodata.eh_frame_hdr.eh_frame.ctors.dtors.jcr.dynamic.got.got.plt.data.bss.comment.debug_aranges.debug_pubnames.debug_info.debug_abbrev.debug_line.debug_str@#@ 1@@@7 @?@>Go@(To(@(@ch@h0m@ w@r@}@8 @8 H @H  @ $ @ PP P (P(PPp`P`Pxx&`%5v 0,07#"@ ,x@@@@@@@(@h@ @ @ @ @ 8 @H @ @ @PP P(PPP`PP !" *1<w @PP P`PpP @  @PP @ P' @1=x(PPPPPP< hP @9(P2Sb @h4r @ ` @VxP @ @ `P 8 @xPhP4"H @1`P> RlY hqabi-note.S../sysdeps/x86_64/elf/start.Sinit.cinitfini.c/build/buildd/glibc-2.3.6/build-tree/amd64-libc/csu/crti.Scall_gmon_startcrtstuff.c__CTOR_LIST____DTOR_LIST____JCR_LIST__completed.4801p.4800__do_global_dtors_auxframe_dummy__CTOR_END____DTOR_END____FRAME_END____JCR_END____do_global_ctors_aux/build/buildd/glibc-2.3.6/build-tree/amd64-libc/csu/crtn.Stest_imopv.c_DYNAMIC__fini_array_end__fini_array_start__init_array_end_GLOBAL_OFFSET_TABLE___init_array_start__libc_start_main@@GLIBC_2.2.5__dso_handle__libc_csu_fini_vl_statepthread_setspecific@@GLIBC_2.2.5vl_pgm_write_f_initvl_thread_specific_state_newvl_tocvl_pgm_read_new_f_start__libc_csu_init__bss_startmaindata_start_finivl_lock_state_edata_endpthread_getspecific@@GLIBC_2.2.5vl_unlock_state_IO_stdin_used__data_start_Jv_RegisterClassesvl_tic__gmon_start__vl_imconvcol_vfvlfeat-0.9.14/bin/glnxa64/test_mathop000077500000000000000000000312631167540017400174100ustar00rootroot00000000000000ELF>`@@@8@# @@@@@@@@@<<@@P@PP8hhPhP@@ Ptdhh@h@$$Qtd/lib64/ld-linux-x86-64.so.2GNU   [<|e P @rwP! @PxP' ;lB libvl.so_vl_state_initvl_toc_fini_Jv_RegisterClassesvl_tic__gmon_start__libc.so.6__libc_start_mainlibm.so.6sqrtfloorsqrtf_edata__bss_start_end$ORIGIN/GLIBC_2.2.5m ui Qui (PPHPPPXP`PhPpP H H5J%L%Jh%Bh%:h%2h%*h%"hH1I^HHPTIp@H@H @nHHHtHÐ=UHt$HHHHHuffffffffUH=g HtHt `PIAÐAVA@ @@@!@AU1ATUSY7_H W$ \$ (ӋT$ -} Yq Љ)‰T$ D$ ((YY\(Y B YY\YQ.zt,(L$@\$0d$ !d$ \$0L$@5 X$^(\T ^Y _Z$Y Z$ .Z6@@E1d$PofL$Pf. vA6@ @1J11f%i  ) % 1 XZY.Zw1+*˾6@G@1^ 1% T$d Q.zt(L$@L$@ZD$dXL$d- Y .ZD$dw1*˾[@G@H0_^M W5V fWt$h($H$f5p Y` HHHH)H$f$YY\Yf / YY\YZD$lQf.zt!d$ l$l$d$ f Xl$hf^Z\D$lZL$lZf.TZ^YZ_ZD$hYiZD$hZd@@d$X fl$Xf.-0vAd@ @1 11Wf%f ZXYZZf.w1*˾d@G@1^x 1WzfW$$Qf.zt sZ$$X$fVY>$ZZ$f.w1E1 *˾[@G@1^ Y@1 !@A@ @@@1 111D69r)ƃuAÃw11E1{u@D$p@1; *DfD$p@1^L$p 1WH*Qf.zt +ffH,Aăw1[@$@1 *Df$@^$ E9tAP@1v 11E11ƍDf9wf)ƃuF$&u1E1P@D$x@1 *DfD$x@1^L$x1,fff*Qf.zt fffH,Łu1[@$@1Df$*ȿ@^$ZfD9tA@1A11E1}1@ƍD8w(ƃuF$&u1E1@$@1*Df$@f1^$1*Qf.zt H,Łu1[@$@1SDf$*ȿ@^$%D8tA@1 HİD[]A\A]A^ÐH\$Ld$L%Hl$H1HL)HH9sHAHH9rH$Hl$Ld$HfffffffUHH-H)SHHXHTHHuH[]?UHSHHHt@PfffHCHHuH[ÐHHchksumeval/selaps [s]func%20s %10s %10s %10s vl_fast_resqrt_f%20s %10.2g %10.2g baselinevl_fast_resqrt_dvl_fast_sqrt_ui32%20s %10.2f %10.2g %010x vl_fast_sqrt_ui16 %10.2f %10.2g %010hx vl_fast_sqrt_ui8 %10.2f %10.2g %010hhx %20s (max relative error: %.6f%%) error: %s: relative error is too large! error: vl_fast_sqrt_ui32 != floor(sqrt((double) x)) error: vl_fast_sqrt_ui16 != floor(sqrt((double) x)) error: vl_fast_sqrt_ui8 != floor(sqrt((double) x)) 4???B% zo ?MbP????Y@;$@x 4 @B\D A(A0LzRx VJP <P9ARL Qm @ @@@@@ 0P@@@0 o@oo@hP@@@.@>@N@XPGCC: (GNU) 3.4.6 (Ubuntu 3.4.6-1ubuntu2)GCC: (GNU) 3.4.6 (Ubuntu 3.4.6-1ubuntu2)GCC: (GNU) 4.0.3 (Ubuntu 4.0.3-1ubuntu5)GCC: (GNU) 4.0.3 (Ubuntu 4.0.3-1ubuntu5)GCC: (GNU) 3.4.6 (Ubuntu 3.4.6-1ubuntu2)GCC: (GNU) 4.0.3 (Ubuntu 4.0.3-1ubuntu5)GCC: (GNU) 3.4.6 (Ubuntu 3.4.6-1ubuntu2),`@+L@@ @<@@!s_IO_stdin_used`@@../sysdeps/x86_64/elf/start.S/build/buildd/glibc-2.3.6/build-tree/glibc-2.3.6/csuGNU AS 2.16.91a@@]OQintFX @WV/build/buildd/glibc-2.3.6/build-tree/amd64-libc/csu/crti.S/build/buildd/glibc-2.3.6/build-tree/glibc-2.3.6/csuGNU AS 2.16.91f#/build/buildd/glibc-2.3.6/build-tree/amd64-libc/csu/crtn.S/build/buildd/glibc-2.3.6/build-tree/glibc-2.3.6/csuGNU AS 2.16.91%% $ > $ > 4: ; I? 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ףp@333333 @(\@(\@? ףp= @ffffff@(\@!@333333)?(\B#@q= ףpGz?)\( @{Gz?@@@m0_?-DT! -DT! @???<=@@.@A$@ AC߰ư>9B.???|=333333?333333?;f?@9@{Gz$@?B@Ah㈵>w+2aib: (%5d,%5d)=%5d dE: %10.3g I: %6.4g H: %6.4g updt: %5d 0.9.14VLFeat version %s Static config: %s %d CPU(s): %s Debug: %s no=%s: option requires an argument -- %s %s: illegal option -- %s %s: illegal option -- %c %s: option requires an argument -- %c hikmeans: branch at depth %d: %6.1f %% completed IX86, little_endianPOSIX_threads, SSE2GNU C %d ILP32%s, %s, %s SSE42 SSE41 SSE3 SSE2 SSE MMX%s%s%s%s%s%s%sikm: Elkan algorithm: total iterations: %d ikm: Elkan algorithm: distance calculations: %d (speedup: %.2f) ikm: training with %d data ikm: %d clusters kmeans: Lloyd iter %d: energy = %g kmeans: Lloyd terminating because maximum number of iterations reached kmeans: Lloyd terminating because the algorithm fully converged kmeans: Lloyd iter %d: restarted %d centers kmeans: Elkan iter 0: energy = %g, dist. calc. = %d kmeans: Elkan iter %d: energy <= %g, dist. calc. = %d kmeans: Elkan iter %d: restarted %d centers kmeans: Elkan iter %d: total dist. calc. per type: UB: %.1f%% (%d), LB: %.1f%% (%d), intra_center: %.1f%% (%d), new_center: %.1f%% (%d) kmeans: Elkan terminating because maximum number of iterations reached kmeans: Elkan terminating because the algorithm fully converged kmeans: Elkan: total dist. calc.: %d (%.2f %% of Lloyd) kmeans: Elkan: there have been %d restarts kmeans: Elkan: total dist. calc. per type: init: %.1f%% (%d), UB: %.1f%% (%d), LB: %.1f%% (%d), intra_center: %.1f%% (%d), new_center: %.1f%% (%d), finalize: %.1f%% (%d) kmeans: repetition %d of %d kmeans: K-means initialized in %.2f s kmeans: K-means termineted in %.2f s with energy %g P5 %d %d %d Error writing PGM datawbError opening PGM file for writing %udInvalid PGM data%drbError opening PGM file `%s' for readingvl_pgm_read(): PGM with BPP > 1 not supportedasciibin://444 ``p ,8DP\ht(4@LXdp| $0<HT`lx ,8DP\htP<;         $ * 0 6 < B H N T Z ` f l r x ~   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/usr/lib/dyld&a7rWq-<p$ *0  8  @loader_path/libvl.dylib 8F/usr/lib/libgcc_s.1.dylib 8/usr/lib/libSystem.B.dylib& jHHH}HuHUHAWAVATSH= H5 H HHHIAu H/t)PHp/HEtH/HDHuHHII<$Md$uHEHHtH$HHtH=WHu]UH=qHuJH}HtHDLHL&mASLAS%%UHAWAVATSH E#E4EEEVHH)HuкHH=E1L=AgffffDHL0DAD)u AAuH=1LL=AgfffLH*YLSA)u 5Áu1H [A\A^A_]Ð%p%r%t%v%x%z%|%~%L9`L5TL1HL- xF Rf op u0 {    __startdyld_stub_binding_helper__dyld_func_lookup_NXArgc_NXArgv___progname__mh_execute_header_environ_mainstart___keymgr_dwarf2_register_sections__cthread_init_routine_atexit_errno_exit_mach_init_routine_printf_putchar_puts_vl_rand_init_vl_rand_seed_by_array_vl_rand_uint32vlfeat-0.9.14/bin/maci64/test_stringop000077500000000000000000000316541167540017400175710ustar00rootroot00000000000000`H__PAGEZERO(__TEXT __text__TEXT__stubs__TEXTB__stub_helper__TEXT__cstring__TEXTXfX__unwind_info__TEXTd__eh_frame__TEXT((__DATA  __dyld__DATA  __got__DATA (  __la_symbol_ptr__DATA8 X8 __common__DATA  H__LINKEDIT000,2 P 1 /usr/lib/dyld)"8 sYJyz$ * 8  @loader_path/libvl.dylib 8F/usr/lib/libgcc_s.1.dylib 8/usr/lib/libSystem.B.dylib&0jHHH}HuHUHAWAVATSH= H5 H HHHIAu HY/t)PHp/HEtH/HDHuHHs II<$Md$uH HHtH HHt!H=Hu]UH=HuJH}HtH DLHL&ASL3AS%+ %* UHAWAVAUATSHHHHH$L5L0H%L0yHH]HH*L0EH+L0,HL5L= H߾L*A\MII3L-5L0H5H4L0I5L0HL*A\M{II<L0WI=L0>11L*A\M,IICL0IDL0H߾H1H=IL54L0H56HJL0H߾HoH=NL0_H5H:OL09H߾HMH=SL0L=HLTL0H߾HH=XL0HLYL0H=H]HwIƃ}^L=L0RLHIIƃ}`L0+LH"Iƃ}bL0LHIƃ}dL0LHIƃ}fL0LHIƃ}hL0LHHÃ}jL0hH5AHCkH5H0;HH8H5HW01H[A\A]A^A_]UHAVSHt))P)`)p)])e)m)u)}LHL@H8H HEHEHEE0Et H[A^]HӉL5I>H5IH09I>HUH<I6 # %%%%%%%%%%%LaL]LYLULQLMLILELAL=|L9p__dyld_make_delayed_module_initializer_calls__dyld_mod_term_funcs%s:%d: check failed: vision*: * '\*' *vision: vision '*' visiona/b/c/d.tar.gztsrc/test_stringop.cvl_string_copyvl_string_replace_wildcardvl_string_basenamed.tar.gzd.tardascii://bin://ascii://bin://unkown1://unknown2://bin:///which remains//which remains/%s%s passed  888  "$@ !X zRx 4z  zRx 4H 4T  __ $0<H+"  #6B  J  R  ^r  {!5E_    __startdyld_stub_binding_helper__dyld_func_lookup_check_impl_NXArgc_NXArgv___progname__mh_execute_header_environ_mainstart___keymgr_dwarf2_register_sections___stderrp___stdoutp__cthread_init_routine_atexit_errno_exit_fprintf_fputc_mach_init_routine_strcmp_vfprintf_vl_string_basename_vl_string_copy_vl_string_parse_protocol_vl_string_replace_wildcardvlfeat-0.9.14/bin/maci64/test_threads000077500000000000000000000217041167540017400173510ustar00rootroot00000000000000`H__PAGEZERO(__TEXT__text__TEXT q __stubs__TEXTb Hb __stub_helper__TEXT  __cstring__TEXT@@__unwind_info__TEXT`__eh_frame__TEXT((__DATA__dyld__DATA__got__DATA( __la_symbol_ptr__DATA8`8__common__DATA H__LINKEDIT   D" P ! /usr/lib/dyld5D՘:=dQ$ *  8  @loader_path/libvl.dylib 8F/usr/lib/libgcc_s.1.dylib 8/usr/lib/libSystem.B.dylib& jHHH}HuHUHAWAVATSH=qH5rHsHHHIAu HO/t)PHp/HEtH/HDHuHH+II<$Md$uHHHtHlHHtH=~Hu]UH=HuJH}Ht|H5DLHL]ASLAS%%UHAWAVATS0ZA L=:L%0+H)L0I<$Au0H=1[A\A^A_]ÐUHAWAVAUATSHH1LuL}L%^E1DfDAL1LLIIHHufDfDJ|1jIIu0`HH=RH0:HH1HH[A\A]A^A_]Ð%%%%%%%%%%%%L\LPL}DLy8Lu,Lq LmLiLeLaL]LY__dyld_make_delayed_module_initializer_calls__dyld_mod_term_funcs%sThread %5d: %d Thread %5d: elapsed time: %.2f s $$  <<b <  "$@XzRx 4  zRx 4 4T  __        $0"   #( 6 > F Rf o u0  -N[ks    __startdyld_stub_binding_helper__dyld_func_lookup_NXArgc_NXArgv___progname__mh_execute_header_environ_main_testThreadstart___keymgr_dwarf2_register_sections___stdoutp__cthread_init_routine__vl_state_atexit_errno_exit_fflush_mach_init_routine_printf_pthread_create_pthread_join_vl_configuration_to_string_copy_vl_get_rand_vl_rand_uint32_vl_tic_vl_tocvlfeat-0.9.14/bin/maci64/test_vec_comp000077500000000000000000000215301167540017400175070ustar00rootroot00000000000000H__PAGEZEROx__TEXT__text__TEXT  __stubs__TEXT 6 __stub_helper__TEXT l __cstring__TEXT00__const__TEXT__unwind_info__TEXT\__eh_frame__TEXT((__DATA__dyld__DATA__got__DATA  __la_symbol_ptr__DATA0H0 __common__DATAx H__LINKEDIT  X !p P ! /usr/lib/dyld&b?IG:$ *  8  @loader_path/libvl.dylib 8F/usr/lib/libgcc_s.1.dylib 8/usr/lib/libSystem.B.dylib& jHHH}HuHUHAWAVATSH=H5HHHHIAu H/t)PHp/HEtH/HDHuHHKII<$Md$uHHHtHHHtH=Hu]UH=HuJH}HtHmDLHLASL#AS%%UHAWAVAUATSHHMHUHuHILN<0|ILHIHUL*LHHMHMt_H]IfL6H*Y9ZAELH*YZAIIHuH[A\A]A^A_]UHAWAVAUATSH$H+HE0IƿzIǿz1IĐLH*YZALeH*YhZAHHuHǀH0!H$I_Mt$H}оHڹMAHRL0H=AH1ǀI0L,$H}оHڹMAtHH0}H=TLHLHH}H1H[A\A]A^A_]%%%%%%%%%LiLeLaL]LYLULQLMLI__dyld_make_delayed_module_initializer_calls__dyld_mod_term_funcsFloat L2 distnace: %.3f s Float L2 distance (SIMD): %.3f s =  88 8 "$@X zRx 4  zRx 4X  4T  __       "   # 6 x> F Rf o z  *O_g    __startdyld_stub_binding_helper__dyld_func_lookup_NXArgc_NXArgv___progname__mh_execute_header_environ_init_data_mainstart___keymgr_dwarf2_register_sections__cthread_init_routine__vl_state_atexit_errno_exit_mach_init_routine_vl_eval_vector_comparison_on_all_pairs_f_vl_get_rand_vl_get_vector_comparison_function_f_vl_rand_uint32_vl_tic_vl_tocvlfeat-0.9.14/bin/win32/000077500000000000000000000000001167540017400146055ustar00rootroot00000000000000vlfeat-0.9.14/bin/win32/Microsoft.VC90.CRT.manifest000066400000000000000000000010141167540017400214050ustar00rootroot00000000000000 vlfeat-0.9.14/bin/win32/aib.exe000066400000000000000000000170001167540017400160410ustar00rootroot00000000000000MZ@ !L!This program cannot be run in DOS mode. $W4 d u b8 r `RichPEL]N   @PM"P@!@ .text$   `.rdata @@.data40@.rsrc@@@@,U!@S\$U!@V\$W!@= @\$ hp0@!@\$,!@\$4!@\$<!@\$D!@\$L!@\$T!@\$\!@\$d!@\$lx!@\$tp!@\$|h!@ݜ$`!@ݜ$X!@ݜ$P!@ݜ$H!@ݜ$@!@ݜ$8!@ݜ$0!@ݜ$(!@ݜ$ !@ݜ$!@ݜ$!@ݜ$!@ݜ$!@ݜ$ @ݜ$ @ݜ$׃t$ $hx0@׃ uh|0@׃uh0@h0@jD$j P @U @],3 QVh0@F rU @h0@׃_3^][]h@0@$0@50@0@h0@h0@h0@ @0@}jYjhH"@3ۉ]dp]$4@SVW$ @;t;u3Fuh( @3F 4@;u jVY; 4@u,5 4@h @h @YYtE50@ 4@;uh @h @bYY 4@9]uSW, @904@th04@yYt SjS04@0@ @50@50@50@ 0@90@u7P @E MPQYYËeE0@390@uP @90@u @E0@øMZf9@t3M<@@8PEuH t uՃv39xtv39j0@P @jL @YY(4@,4@H @ 0@D @ 0@@ @4@P=0@u hE@< @Y]=0@u j8 @Y3qUE8csmu*xu$@= t=!t="t=@u3]h@ @3% @jhh"@^5,4@5l @YEu uh @YgjYe5,4@։E5(4@YYEEPEPu5L @YPKEu֣,4@uփ(4@E EjYËUuNYH]ËV8"@8"@W;stЃ;r_^ËV@"@@"@W;stЃ;r_^% @UMMZf9t3]ËA<8PEu3ҹ f9H‹]̋UEH<ASVq3WDv} H ;r X;r B(;r3_^[]̋Ujh"@h@dPSVW0@1E3PEdeEh@*tUE-@Ph@Pt;@$ЃEMd Y_^[]ËE3=‹ËeE3Md Y_^[]% @%T @h@d5D$l$l$+SVW0@1E3PeuEEEEdËMd Y__^[]QËUuuu uh@h0@]ËVhh3V t VVVVV^3ËU0@eeSWN@;t t У0@`VEP @u3u @3 @3 @3EP @E3E3;uO@ u 50@։50@^_[%X @%\ @%` @%d @; 0@u%p @%t @%x @U(2@ 1@1@1@51@=1@f2@f 2@f1@f1@f%1@f-1@2@E2@E2@E2@P1@2@1@0@ 0@0@0@ @H1@j9Yj @h!@ @=H1@ujYh  @P0 @%| @&v&b&4&&&%%%%%%N&f$|$$$$$$X$$$%%% %2%L%^%n%L$@$8$*$"$$$#####0@@@z):? ^)?1*?}8gD?c]K?oT?,eX?\m?H?JY8? Q?t$~?Ciq?Q?HPs?S?37?NbX9?6[ ?|a2U??V-?{P?u?&S?K=U?}?5^I?_LJ?N@a?>yX5?0@P1@H0@0"@@@@[@o@## ,##8 "& &v&b&4&&&%%%%%%N&f$|$$$$$$X$$$%%% %2%L%^%n%L$@$8$*$"$$$#####*vl_aib_delete,vl_aib_process+vl_aib_newvl.dll.printfMSVCR90.dll_amsg_exit__getmainargs,_cexit|_exitf_XcptFilterexit__initenv_initterm_initterm_e<_configthreadlocale__setusermatherr _adjust_fdiv__p__commode__p__fmodej_encode_pointer__set_app_typeC?terminate@@YAXXZ_unlock__dllonexitv_lock_onexit`_decode_pointers_except_handler4_common _invoke_watson?_controlfp_sK_crt_debugger_hookInterlockedExchange!SleepInterlockedCompareExchangeSetUnhandledExceptionFilterTQueryPerformanceCounterfGetTickCountGetCurrentThreadIdGetCurrentProcessIdOGetSystemTimeAsFileTime-TerminateProcessGetCurrentProcess>UnhandledExceptionFilterIsDebuggerPresentKERNEL32.dllfloatdoubleint8int16int32int64int8int16int32int64l1l2chi2kl1kl2kchi2Pic = [%f ; ... ]; AIB starting %d => %d IB done N@D0 HX@V PAPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGvlfeat-0.9.14/bin/win32/mser.exe000066400000000000000000000360001167540017400162550ustar00rootroot00000000000000MZ@ !L!This program cannot be run in DOS mode. $)GGG<GLGLGLGFGLGLGRichGPEL^N  &0@`65PP5@04.textN `.rdata` 0 @@.data @ .@.rsrcP8@@,1@VW 1@=,1@P40@u$1@VQ80@(1@_^QVWtcL$QP1@D$t@~ W1@v,WhV1@ =r_^Y_^Y_3^Ỹ>u3ËD$WPjj%NQhW1@=r _Ã>t)T$ RW\0@u_3_̋tP`0@dž̊@XAX@xD$Xx@`D$X`@hD$Xh@pD$Xp̋@,̋@4̋@D̋@P̋@LU pH@3ĉ$ 5@A@ A@T$9F9N9X9f9r9|9999999999 : :0:@:N:`:r:::8888888v8`8J8<8*88877777v7d7X7b#@#%@V'@Usage: %s [options] files ... Options include: --verbose -v Be verbose --help -h Print this help message --seeds Specify seeds file --frames Specify frames file --meta Specify meta file --delta -d Specify MSER delta paramter --epsilon -e Specify MSER epsilon parameter --no-dups Remove duplicate --dups Keep duplicates --max-area Specify maximum region (relative) area --min-area Specify minimum region (relative) area --max-variation Specify maximum absolute region stability --bright-on-dark Enable or disable bright-on-dark regions (default 1) --dark-on-bright Enable or disable dark-on-bright regions (default 1) vhd:@@v@@h@@d@@@@@@@@@@@@@@@@@@H@I@HpH@`5@5*$@$@(@)@)@690d69@0$6R<08<"< <;;;;;;l;N;F;0;";;;::::::99"9*949>9F9N9X9f9r9|9999999999 : :0:@:N:`:r:::8888888v8`8J8<8*88877777v7d7X7#_vl_statevl_unlock_statevl_thread_specific_state_newrvl_lock_statevl_string_copyvl_string_lengthvl_string_parse_protocolvl_string_replace_wildcardsvl_mser_deletetvl_mser_ell_fitvvl_mser_processuvl_mser_newyvl_pgm_extract_data|vl_pgm_get_npixels{vl_pgm_get_bppzvl_pgm_extract_headvl_string_basenamevl_string_protocol_nameCvl_get_version_string%optarg'optind$getopt_longvl.dllfopenfclosefreemalloc.printfabortexitfprintf__iob_funci_snprintfJsscanf*memsetMSVCR90.dll_amsg_exit__getmainargs,_cexit|_exitf_XcptFilter__initenv_initterm_initterm_e<_configthreadlocale__setusermatherr _adjust_fdiv__p__commode__p__fmodej_encode_pointer__set_app_typeK_crt_debugger_hookC?terminate@@YAXXZ_unlock__dllonexitv_lock_onexit`_decode_pointers_except_handler4_common _invoke_watson?_controlfp_s5TlsSetValue4TlsGetValueInterlockedExchange!SleepInterlockedCompareExchange-TerminateProcessGetCurrentProcess>UnhandledExceptionFilterSetUnhandledExceptionFilterIsDebuggerPresentTQueryPerformanceCounterfGetTickCountGetCurrentThreadIdGetCurrentProcessIdOGetSystemTimeAsFileTimeKERNEL32.dllfloatdoubleint8int16int32int64int8int16int32int64%g %d %lgl1l2chi2kl1kl2kchi2verbosehelpdeltaseedsframesmetamax-areamin-areamax-variationmin-diversitybright-on-darkdark-on-bright%.frame%.frame%.mser%.mser%.meta%.metaInvalid option '%s'.Missing mandatory argument for option '%s'.MSERs filespec: `%s' Frames filespec: `%s' Meta filespec: `%s' 0.2Version: driver %s; libvl %s %lfThe argument of '%s' must be a non-negative number.%lfmax-area argument must be in the [0,1] range.%lfmin-area argument must be in the [0,1] range.%lfmax-variation argument must be non-negative.%lfmin-diversity argument must be in the [0,1] range.The arguments of '%s' is invalid.The arguments of '%s' is invalid.The arguments of '%s' is invalid.meta file supports only ASCII protocol%dbright_on_dark must be 0 or 1.%ddark_on_bright must be 0 or 1.%s: error: %s (%d) mser: frames output mser: active %d mser: pattern %s mser: protocol %s mser: seeds output mser: active %d mser: pattern %s mser: protocol %s mser: meta output mser: active %d mser: pattern %s mser: protocol %s Basename of '%s' is too longmser: processing '%s' mser: basename is '%s' rbCould not open '%s' for reading.wOutput file name too long.Could not open '%s' for writing.wOutput file name too long.Could not open '%s' for writing.wOutput file name too long.Could not open '%s' for writing.mser: writing seeds to '%s' mser: writing frames to '%s' mser: writing meta to '%s' PGM header corrputed.mser: image is %d by %d pixels Could not allocate enough memory.PGM body corrputed.Could not create an MSER filter.mser: parameters: mser: delta = %d mser: max_area = %g mser: min_area = %g mser: max_variation = %g mser: min_diversity = %g %d %f Could not allocate enough memory.%d %f mser: err: %s (%d) N@D0 HXPV PAPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGvlfeat-0.9.14/bin/win32/msvcr90.dll000066400000000000000000024010001167540017400166020ustar00rootroot00000000000000MZ@ !L!This program cannot be run in DOS mode. $Oĭ a a a,a aa3)`3?^a3.a38 a3> a3; aRich aPEL=1G!  Z @-p Rx 0  @`|(  $ 3@.textX Z  `.datagp D^ @.rsrc  @@.reloc7 8 @B2H\r *:JVdn&:Rl|4HThv 4DRh~ &6LZt (4@Rlz .<N`p *>Ndt*Fdx0JZl~"2vTxyUx-XxSYx5Zx1#UxmXxTx=1G) CorExitProcessmscoree.dllruntime error TLOSS error SING error DOMAIN error R6034 An application has made an attempt to load the C runtime library incorrectly. Please contact the application's support team for more information. R6033 - Attempt to use MSIL code from this assembly during native code initialization This indicates a bug in your application. It is most likely the result of calling an MSIL-compiled (/clr) function from a native constructor or from DllMain. R6032 - not enough space for locale information R6031 - Attempt to initialize the CRT more than once. This indicates a bug in your application. R6030 - CRT not initialized R6028 - unable to initialize heap R6027 - not enough space for lowio initialization R6026 - not enough space for stdio initialization R6025 - pure virtual function call R6024 - not enough space for _onexit/atexit table R6019 - unable to open console device R6018 - unexpected heap error R6017 - unexpected multithread lock error R6016 - not enough space for thread data This application has requested the Runtime to terminate it in an unusual way. Please contact the application's support team for more information. R6009 - not enough space for environment R6008 - not enough space for arguments R6002 - floating point support not loaded Microsoft Visual C++ Runtime Library ...Runtime Error! Program: Microsoft.VC90.CRT.manifestWinSxS\\WinSxS\msvcr90.dll\MSCoree.dllPGORT90.dllFindActCtxSectionStringWkernel32.dllEncodePointerKERNEL32.DLLDecodePointerFlsFreeFlsSetValueFlsGetValueFlsAlloc...*?...*?.com.bat.cmd.exe./\?*.com.bat.cmd.exe./\?*=SystemRoot.\\PATHcmd.exe/cCOMSPEC=SystemRoot.\PATHcmd.exe/cCOMSPECccsUTF-8UTF-16LEUNICODE /c TMPccsUTF-8UTF-16LEUNICODE /c TMPSunMonTueWedThuFriSatJanFebMarAprMayJunJulAugSepOctNovDecTZ(null)(null)EEE50P( 8PX700WP `h````xpxxxxEEE00P('8PW700PP (`h`hhhxppwppHH:mm:ssdddd, MMMM dd, yyyyMM/dd/yyPMAMDecemberNovemberOctoberSeptemberAugustJulyJuneAprilMarchFebruaryJanuaryDecNovOctSepAugJulJunMayAprMarFebJanSaturdayFridayThursdayWednesdayTuesdayMondaySundaySatFriThuWedTueMonSuna/pam/pmbad allocationTx*Wx*WxUnknown exceptionDTxWx*WxTx*Wx*WxTx*Wx*Wxbad castbad typeid,TxTWxtTxWx*Wx|WxTxnWx*Wxbad exceptioncsm Access violation - no RTTI data!Bad read pointer - no RTTI data!Attempted a typeid of NULL pointer!Bad dynamic_cast! Complete Object Locator' Class Hierarchy Descriptor' Base Class Array' Base Class Descriptor at ( Type Descriptor'`local static thread guard'`managed vector copy constructor iterator'`vector vbase copy constructor iterator'`vector copy constructor iterator'`dynamic atexit destructor for '`dynamic initializer for '`eh vector vbase copy constructor iterator'`eh vector copy constructor iterator'`managed vector destructor iterator'`managed vector constructor iterator'`placement delete[] closure'`placement delete closure'`omni callsig' delete[] new[]`local vftable constructor closure'`local vftable'`RTTI`EH`udt returning'`copy constructor closure'`eh vector vbase constructor iterator'`eh vector destructor iterator'`eh vector constructor iterator'`virtual displacement map'`vector vbase constructor iterator'`vector destructor iterator'`vector constructor iterator'`scalar deleting destructor'`default constructor closure'`vector deleting destructor'`vbase destructor'`string'`local static guard'`typeof'`vcall'`vbtable'`vftable'^=|=&=<<=>>=%=/=-=+=*=||&&|^~(),>=><=<%/->*&+---++*->operator[]!===!<<>> delete new__unaligned__restrict__ptr64__clrcall__fastcall__thiscall__stdcall__pascal__cdecl__based($Rx$Rx$Rx$Rx$Rx$Rx$Rx$Rxx$Rxl$RxRxRxRxRx`RxDRxd$Rx\$RxRxX$RxT$RxP$RxL$RxH$RxD$Rx8$Rx4$Rx0$Rx,$Rx($Rx$$Rx $Rx$Rx$Rx$Rx$Rx $Rx$Rx$Rx$Rx#Rx#Rx#Rx#Rx#Rx#Rx#Rx#Rx#Rx#Rx#Rx#Rx#Rx#Rx#Rx#Rx#Rx#Rx#Rx#Rx#Rxx#Rxl#RxX#Rx8#Rx#Rx"Rx"Rx"Rx"Rxx"RxT"Rx4"Rx "Rx!Rx!Rx!Rx!Rx!Rx!Rx!Rx!Rx|!Rx`!Rx@!Rx!Rx Rx Rx Rx Rx\ Rx8 Rx RxRxRxRxWxWxWx[WxvWxWxWxWxWxrWxWxWxWxWxWx ?? {flat}{for `non-type-template-parameterunsigned long int short char void,,... throw()[s `template-parameter'NULLcli::pin_ptr<cli::array<void ''`anonymous namespace'`generic-type-template-parameter-::`unknown ecsu'union struct class enum coclass cointerface ) extern "C" [thunk]:public: protected: private: virtual static `template static data member destructor helper'`template static data member constructor helper'`local static destructor helper'`adjustor{`vtordisp{`vtordispex{ }'}' const volatile CV: volatile volatileconstsigned doubleUNKNOWN__int128boolwchar_t__int64__int16__int32__int8__w64 floatlongintshortchar__GLOBAL_HEAP_SELECTED__MSVCRT_HEAP_SELECTTx:Xx*WxHeapQueryInformationSetThreadStackGuarantee 5/?GGGwHHHIIIKKKsMMMtOOOuPPPQQQvRRRSSS1!x2@y3#z4${5%|6^}7&~8*9(0)-_=+ qQwWeErRtTyYuUiI oOpP[{]} aAsSdD fF!gG"hH#jJ $kK %lL &;:''"(`~)\|zZ,xX-cC.vV/bB0nN1mM 2,<3.>4/?5*r ;T^h<U_i=V`j>Wak?Xbl@YcmAZdnB[eoC\fpD]gqG7wH8I9-K4s5M6t+O1uP2Q3vR0S.CONIN$CONOUT$ SystemFunction036ADVAPI32.DLLGetProcessWindowStationGetUserObjectInformationAGetLastActivePopupGetActiveWindowMessageBoxAUSER32.DLLLC_TIMELC_NUMERICLC_MONETARYLC_CTYPELC_COLLATELC_ALL.Rx Yx.Rx[x Yx.Rx[xDXx.Rx[xw[Yx.Rx[xYYx.Rx[xMXYx  !"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcdefghijklmnopqrstuvwxyz{|}~_.,_;C=; ((((( H h(((( H H  !"#$%&'()*+,-./0123456789:;<=>?@abcdefghijklmnopqrstuvwxyz[\]^_`abcdefghijklmnopqrstuvwxyz{|}~  !"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`ABCDEFGHIJKLMNOPQRSTUVWXYZ{|}~P[x[xIllegal byte sequenceDirectory not emptyFunction not implementedNo locks availableFilename too longResource deadlock avoidedResult too largeDomain errorBroken pipeToo many linksRead-only file systemInvalid seekNo space left on deviceFile too largeInappropriate I/O control operationToo many open filesToo many open files in systemInvalid argumentIs a directoryNot a directoryNo such deviceImproper linkFile existsResource deviceUnknown errorBad addressPermission deniedNot enough spaceResource temporarily unavailableNo child processesBad file descriptorExec format errorArg list too longNo such device or addressInput/output errorInterrupted function callNo such processNo such file or directoryOperation not permittedNo error : (Press Retry to debug the application - JIT must be enabled)For information on how your program can cause an assertion failure, see the Visual C++ documentation on assertsExpression: Line: File: Program: Assertion failed!: ...Microsoft Visual C++ Runtime Library(Press Retry to debug the application - JIT must be enabled)For information on how your program can cause an assertion failure, see the Visual C++ documentation on assertsExpression: Line: File: <program name unknown>Program: Assertion failed!Visual C++ CRT: Not enough memory to complete call to strerror.Visual C++ CRT: Not enough memory to complete call to strerror.united-statesunited-kingdomtrinidad & tobagosouth-koreasouth-africasouth koreasouth africaslovakpuerto-ricopr-chinapr chinanznew-zealandhong-konghollandgreat britainenglandczechchinabritainamericausausukswissswedish-finlandspanish-venezuelaspanish-uruguayspanish-puerto ricospanish-peruspanish-paraguayspanish-panamaspanish-nicaraguaspanish-modernspanish-mexicanspanish-hondurasspanish-guatemalaspanish-el salvadorspanish-ecuadorspanish-dominican republicspanish-costa ricaspanish-colombiaspanish-chilespanish-boliviaspanish-argentinaportuguese-braziliannorwegian-nynorsknorwegian-bokmalnorwegianitalian-swissirish-englishgerman-swissgerman-luxembourggerman-lichtensteingerman-austrianfrench-swissfrench-luxembourgfrench-canadianfrench-belgianenglish-usaenglish-usenglish-ukenglish-trinidad y tobagoenglish-south africaenglish-nzenglish-jamaicaenglish-ireenglish-caribbeanenglish-canenglish-belizeenglish-ausenglish-americandutch-belgianchinese-traditionalchinese-singaporechinese-simplifiedchinese-hongkongchinesechichhcanadianbelgianaustralianamerican-englishamerican englishamericanDDRxENU0DRxENUDRxENUDRxENADRxNLBCRxENCCRxZHHCRxZHICRxCHSCRxZHHCRxCHSCRxZHICRxCHTCRxNLBxCRxENUlCRxENA\CRxENLPCRxENCB<'HR%?I&<^:ȅe?>!ʎ<sl?ecb~q<ۧ?rʀ<F(&?Zn<'`f?47j<㚦?+i U<oX?F.K;<.'?Yhʼn<4lkQg?;+U<3?М m8<&?<S(?Y=t<:@Xh?,n@4`<$?<n?9ܮ<-)?\<Řwi?=<R%é?f@<*B?aw<G^*?m(<<2;j?PE<?Ҳ?<w*I?n'$<23?z7<[>t?V-Ai<?`DTb< ;?S.<TR*?)R<2k?,%ً<r?SC? 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_,˫ 0  *RA^0(ip΀Brv0j0R axM0  *H 0y1 0 UUS10U Washington10URedmond10U Microsoft Corporation1#0!UMicrosoft Code Signing PCA0 070823002313Z 090223003313Z0t1 0 UUS10U Washington10URedmond10U Microsoft Corporation10UMicrosoft Corporation0"0  *H 0  Iͪ:4#Ykl!"ꮿ&2],6V+ n(L?A@F فWDv\t3ta~E>հ^ { `j(ub]:![sE㕉w`w73/bɮOg 90qgF PtA/$j!kIIJ9Bm\J׎>x Gؤ#{fJt&AKa`N0Ȃ p|UMwUv00U0U!@|QTKj4.&0U% 0 +0U#0vp[NQD.Dc0DU=0;09753http://crl.microsoft.com/pki/crl/products/CSPCA.crl0H+<0:08+0,http://www.microsoft.com/pki/certs/CSPCA.crt0  *H @WoSg 4K<؁ ytwOGi~bg()UיP^ oY Ӌ> EG&`W K̺MnH(5!r_i$4 ~no R*LI2,]&<[ 5OEGYxWN򭜚A\$>4GN{ɜcS׿Ϡz+,lڿ8\ʀݐ#簡L>&x,kǏ<00 a,0  *H 0y1 0 UUS10U Washington10URedmond10U Microsoft Corporation1#0!UMicrosoft Timestamping PCA0 070612235451Z 120613000451Z01 0 UUS10U Washington10URedmond10U Microsoft Corporation1'0%U nCipher DSE ESN:27F4-D440-54F31'0%UMicrosoft Timestamping Service0"0  *H 0 ˤ{sԆ}Dᳵ*Sʯݡ:1xD Eft48w<#p2ΞECtSLN/[S>V&|clVr렋xeN{"ͪYx8bCOCAE1i1v!4^RFX!9Wo_lu6vIH}v }ㇼFդkêsH%JtPibfly3'q56c+Yyu00U7Uٖ [?YJ􁖠0U#0oN?4K;AC0DU=0;09753http://crl.microsoft.com/pki/crl/products/tspca.crl0H+<0:08+0,http://www.microsoft.com/pki/certs/tspca.crt0U% 0 +0U0  *H %o9->Hl< bG' h")/rt'EM^ޏ[V*=!AY2qX![hw|"T+҈,:_:|a(9mj/0xYjV58 <@΀(jY@l ?5zyj "` !`5׿Z4zfvLM@YFLȬLB'S?c|%˗600 a,0  *H 0y1 0 UUS10U Washington10URedmond10U Microsoft Corporation1#0!UMicrosoft Timestamping PCA0 070612235451Z 120613000451Z01 0 UUS10U Washington10URedmond10U Microsoft Corporation1'0%U nCipher DSE ESN:27F4-D440-54F31'0%UMicrosoft Timestamping Service0"0  *H 0 ˤ{sԆ}Dᳵ*Sʯݡ:1xD Eft48w<#p2ΞECtSLN/[S>V&|clVr렋xeN{"ͪYx8bCOCAE1i1v!4^RFX!9Wo_lu6vIH}v }ㇼFդkêsH%JtPibfly3'q56c+Yyu00U7Uٖ [?YJ􁖠0U#0oN?4K;AC0DU=0;09753http://crl.microsoft.com/pki/crl/products/tspca.crl0H+<0:08+0,http://www.microsoft.com/pki/certs/tspca.crt0U% 0 +0U0  *H %o9->Hl< bG' h")/rt'EM^ޏ[V*=!AY2qX![hw|"T+҈,:_:|a(9mj/0xYjV58 <@΀(jY@l ?5zyj "` !`5׿Z4zfvLM@YFLȬLB'S?c|%˗600j O%EXzg0  *H 0p1+0)U "Copyright (c) 1997 Microsoft Corp.10U Microsoft Corporation1!0UMicrosoft Root Authority0 060916010447Z 190915070000Z0y1 0 UUS10U Washington10URedmond10U Microsoft Corporation1#0!UMicrosoft Timestamping PCA0"0  *H 0 7nBJqH>S,2ORȃ>3I1(dPKuǨծipfx'f趷 Y")/@VvmdmJT޿ǀL7VhGv\/}%V[jc|<%M9wt]\؆7,u9 |vlnz>q_*Ob`2҃N+"\hE/Pl%ׅvs6ƕz`3[AXn,HoCj&k(0$0U% 0 +0U0[pir#Q~Mˡr0p1+0)U "Copyright (c) 1997 Microsoft Corp.10U Microsoft Corporation1!0UMicrosoft Root Authority<<>c@0 +70UoN?4K;AC0 +7  SubCA0 U0U00  *H M1|PapEsT? -QS9V ތ;ɷQ!oi~k"Flm|"Fӄ6~p]Eݎ*|ɮ2Շc6!v;s!شTeJ(&`;exHϭ:ObX099!dcC/{FeJtn ̝(a|H!8Ŗ2@S=f7"̰wTQ:rD#1w0s00y1 0 UUS10U Washington10URedmond10U Microsoft Corporation1#0!UMicrosoft Code Signing PCA axM0 +0 *H  1  +70 +7 10  +70# *H  11Dž*jn]0B +7 1402msvcr90.dllhttp://microsoft.com0  *H ?3:F+[5sƠBͭ@sCfD$e3 e3*iQvοWhW˞V\(qN(Ӟ ~ZZ vRQZᝑ\p:OlZ M䝹0~ 74..ul(9uPq/Kʼ=k>OaPw2XYc+."ږ?j5-//mnSY_<Gx$5&#&ܳ0 *H  1 000y1 0 UUS10U Washington10URedmond10U Microsoft Corporation1#0!UMicrosoft Timestamping PCA a,0+]0 *H  1  *H 0 *H  1 071107092113Z0# *H  1=Qt)) i0  *H +@#\ qDg_E:LK"V3keSXG4( )Uxqy)T .++rZӐXڈfSE̠J0,: "DBoA @mAdhdz)b4FP`xE@@\.text(* `.rdata @.@@.dataP<@.rsrc`J@@PPQPQPQPQPQPQA̡TA@VWHA@=TA@P4@@uLA@VQ8@@PA@_^VWHA@=TA@P4@@uLA@VQ8@@PA@_^QVWtcL$QPu3ËD$WPjj%NQhW8A@=r_Ã>t)T$ RWl@@u_3_̋tPp@@dž̋t|@@$ÊD$T$$D$D$D$D$D$ T$T$D$PT$T$jT$ T$L$jQT$x@@D$ $hPP@Rt@@̋t2t |@@3ËPjL$ jQx@@s%T$RhTP@Pt@@ u3̋Stzt |@@3[ËPjL$ jQ@@D$ T$ L$ GD$T$GD$OL$WT$GOW3[Ë@@hXP@QӃu [ËWh\P@RӃ t߃K[̋@<̋@(̋@0̋@L̋@P+A4APALID̋@,̋@h̋@d@p@x݀D$XpD$XxD$ݘ́4P\@3ĉ$0$8Su_3^$034ËCLUkPl$0UD$0D$8D$<@@D$uxKD$ PL$@hQDA@ =r 9|$|$ +ȍT |Q@H$`+$H$LQ@Q@h$WWQf$\$^w\@h$aWP$d$hQ@ Q@h$+WP$t#$+$+f$+ y\@h$/WR$/$/cQ@ Q@h$h WR$3$d $h $l ){\@h$qWQ$t$xH$0uWhXD@hLD@SV@@ @@u+Bh=`ƃ<p/@$P/@D$<뜋 @@$83;D$Ls@@T$dRhS@Q@@ t 9|$dJ@@LQhS@h$3R@@ j$|3PQhU@@@@Pt@@j@@@@T$lRhT@Q@@ t 9|$l@@TRh T@h$3P끋@@$HC;$Lh\S@$|3hR@@ @Q$/@@@$;2@@TRhR@h$3P @@$X#; 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libvl %s The arguments of '%s' is invalid.The arguments of '%s' is invalid.The arguments of '%s' is invalid.The arguments of '%s' is invalid.meta file supports only ASCII protocolThe arguments of '%s' is invalid.The arguments of '%s' is invalid.%dThe argument of '%s' must be a non-negative integer.%dThe argument of '%s' must be a non-negative integer.%dThe argument of '%s' must be an integer.%lfThe argument of '%s' must be not smaller than 1.%lfThe argument of '%s' must be a non-negative float.%lfThe argument of '%s' must be a non-negative float.%s: error: %s (%d) sift: write aggregate . yesno%3s %-6s %-10s sift: write frames .... yesno%3s %-6s %-10s sift: write descriptors yesno%3s %-6s %-10s sift: write meta ...... yesno%3s %-6s %-10s sift: write GSS ....... yesno%3s %-6s %-10s sift: read frames .... yesno%3s %-6s %-10s sift: will compute orientations Basename of '%s' is too longsift: <== '%s' sift: basename is '%s' rbCould not open '%s' for reading.Cannot read from '%s'.'%s' contains a malformed PGM header.sift: image is %d by %d pixels Could not allocate enough memory.PGM body malformed.rbOutput file name too long.Could not open '%s' for reading'%s' malformed'%s' malformed'%s' malformed'%s' malformedsift: read %d keypoints from '%s' wbOutput file name too long.Could not open '%s' for writingwbOutput file name too long.Could not open '%s' for writingwbOutput file name too long.Could not open '%s' for writingwbOutput file name too long.Could not open '%s' for writingsift: writing all ....... to . '%s' sift: writing frames .... to . '%s' sift: writing descriptors to . '%s' sift: writign meta ...... to . '%s' Could not create SIFT filter.sift: filter settings: sift: octaves (O) = %d sift: levels (S) = %d sift: first octave (o_min) = %d sift: edge thresh = %g sift: peak thresh = %g sift: magnif = %g yesnosift: will source frames? %s yesnosift: will force orientations? %s sift: GSS octave %d computed Could not write GSS to PGM file.sift: detected %d (unoriented) keypoints sift: err: %s (%d) N@D0 HX`V PAPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGvlfeat-0.9.14/bin/win32/test_getopt_long.exe000066400000000000000000000160001167540017400206650ustar00rootroot00000000000000MZ@ !L!This program cannot be run in DOS mode. $P1111cR1cC1cT111cD1cV1Rich1PEL_N   @P!P@ @ .text   `.rdata @@.data0@.rsrc@@@SUVW= @h1@׋l$\$5 @jh0@h1@USփIbAt3t":t?uBh2@׃lh3@׃`6@Ph2@Nhl2@׃Gft0t-t @. @Rh2@h(1@׃ @Qh2@׃jh0@h1@USփWh(3@׋ @2;}DPhD3@F;|_^]3[h@3@$3@53@3@ht3@hx3@hp3@ @3@}jYjhH!@3ۉ]dp]6@SVW$ @;t;u3Fuh( @3F6@;u jWY;6@u,56@h @h @YYtE53@6@;uh @h @gYY6@9]uSW, @96@th6@~Yt SjS6@t3@ @5t3@5x3@5p3@ 3@9|3@u7P @E MPQYYËeEࣈ3@39|3@uP @93@u @E3@øMZf9@t3M<@@8PEuH t uՃv39xtv39j|3@ @jP @YY6@6@L @ 3@H @ 3@D @6@Q=d3@u h5@@ @Yb=`3@u j< @Y3vUE8csmu*xu$@= t=!t="t=@u3]h@ @3% @jhh!@b56@5l @YEu uh @YgjYe56@։E56@YYEEPEPu5P @YPOEu֣6@uփ6@E EjYËUuNYH]ËV8!@8!@W;stЃ;r_^ËV@!@@!@W;stЃ;r_^% @̋UMMZf9t3]ËA<8PEu3ҹ f9H‹]̋UEH<ASVq3WDv} H ;r X;r B(;r3_^[]̋Ujh!@h@dPSVWh3@1E3PEdeEh@*tUE-@Ph@Pt;@$ЃEMd Y_^[]ËE3=‹ËeE3Md Y_^[]%T @%8 @h@d5D$l$l$+SVWh3@1E3PeuEEEEdËMd Y__^[]QËUuuu uh@hh3@]ËVhh3V t VVVVV^3ËUh3@eeSWN@;t t Уl3@`VEP @u3u @3 @3 @3EP @E3E3;uO@ u 5h3@։5l3@^_[%X @%\ @%` @%d @; h3@u%p @%t @%x @U(4@ 4@4@4@54@=4@f4@f 4@f4@f4@f%4@f-4@4@E4@E4@E4@3@4@3@3@ 3@h3@l3@ @3@j9Yj @h @ @=3@ujYh  @P0 @%| @%t%`%2%%%$$$$$$L%V#d#z#####J#### $$$0$J$\$l$>#6#(# ###"""#"""@@ @3@3@Hh3@0!@@@@K@_@"" ,""8 !% %t%`%2%%%$$$$$$L%V#d#z#####J#### $$$0$J$\$l$>#6#(# ###"""#"""'optind%optarg$getopt_longvl.dllabort.printfMSVCR90.dll_amsg_exit__getmainargs,_cexit|_exitf_XcptFilterexit__initenv_initterm_initterm_e<_configthreadlocale__setusermatherr _adjust_fdiv__p__commode__p__fmodej_encode_pointer__set_app_typeC?terminate@@YAXXZ_unlock__dllonexitv_lock_onexit`_decode_pointers_except_handler4_common _invoke_watson?_controlfp_sK_crt_debugger_hookInterlockedExchange!SleepInterlockedCompareExchangeSetUnhandledExceptionFilterTQueryPerformanceCounterfGetTickCountGetCurrentThreadIdGetCurrentProcessIdOGetSystemTimeAsFileTime-TerminateProcessGetCurrentProcess>UnhandledExceptionFilterIsDebuggerPresentKERNEL32.dllfloatdoubleint8int16int32int64int8int16int32int64helpbuffyfluoridedaggersetdaggerunsetspikeP0@hX0@b`0@fl0@6@x0@6@0@*** parsing options: start hbf:test_getopt_long: test VLFeat implementation of this function. --help -h this message --buffy -b option with no arguments --fluoride -f option with required argument --daggerset auto option with no arguments --daggerrunset auto option with no argument --spike long option with optional argument option `b' or `buffy' option `f' or `flouride' with arg `%s' option `spike' with arg `%s' automatic longoption (daggerset=%d) illegal option or missing argument illegal option *** parsing options: end non-option: '%s' N@D0 HX@V PAPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGvlfeat-0.9.14/bin/win32/test_heap-def.exe000066400000000000000000000220001167540017400200120ustar00rootroot00000000000000MZ@ !L!This program cannot be run in DOS mode. $BLC#"C#"C#"dYA#"]q@#"C##k#"]qA#"]qA#"]qV#"]qB#"RichC#"PEL_N  0@`1<P1@0.text `.rdatax0@@.data@@.rsrcP @@uH̍D̍DQ$$$YQ$$YQVtW|;;$\$D$z,$\$D$AuR\$D$*$\$D$Au&\$D$t|;x_^Y$\$\$z_\$D$^YQt>u3J$$$z$$uY̋Q$SD$v 3[YË[YV׋]^̋L$D$T$ +̋D$T$ L$V4W<<_4^T$ L$D$@@%0@V4W<<_4^VW|$ T t ;sbSU;<,,J<ƍLt;r_]^[Ër<+<~<R<_]^[̅t1SWu3AV<+UnhandledExceptionFilterIsDebuggerPresentKERNEL32.dllSwapping %ux with %u Pushing heap %5u: %f Popping heap %u: %f Refilling, updating fourth element, and popping again %f -> %f %u: %f Pushing heap of structures s[%u].x = %d Popping heap of structures s[%u].x = %d Pushing heap of structures with custom swap s[%u].x = %d Popping heap of structures with custom swap s[%u].x = %d Pushing heap of structures with custom container s[%u].x = %d Popping heap of structures with custom container s[%u].x = %d N@D0 HXPV PAPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGvlfeat-0.9.14/bin/win32/test_host.exe000066400000000000000000000150001167540017400173200ustar00rootroot00000000000000MZ@ !L!This program cannot be run in DOS mode. $P1111cR1cC1cT111cD1cV1Rich1PEL`N   @Pr!P@ @ .textD  `.rdata @@.data0@.rsrc@@@ @̋ @Q,PYV @ @HVы @B,VЃ3^h,@0@$|0@50@|0@hl0@hp0@hh0@ @x0@}jYjh8!@3ۉ]dp]3@SVW$ @;t;u3Fuh( @3F3@;u jWY;3@u,53@h @h @YYtE50@3@;uh @h @kYY3@9]uSW, @93@th3@Yt SjS3@l0@ @5l0@5p0@5h0@ 0@9t0@u7P @E MPQYYËeEࣀ0@39t0@uP @90@u @E0@øMZf9@t3M<@@8PEuH t uՃv39xtv39jt0@H @jD @YY3@3@@ @ 0@< @ 0@8 @3@Q=\0@u he@P @Yf=X0@u jt @Y3zUE8csmu*xu$@= t=!t="t=@u3]h@ @3% @jhX!@f53@5` @YEu u\ @YgjYe53@։E53@YYEEPEPu5D @YPSEu֣3@uփ3@E EjYËUuNYH]ËV(!@(!@W;stЃ;r_^ËV0!@0!@W;stЃ;r_^% @̋UMMZf9t3]ËA<8PEu3ҹ f9H‹]̋UEH<ASVq3WDv} H ;r X;r B(;r3_^[]̋Ujhx!@h@dPSVW`0@1E3PEdeEh@*tUE-@Ph@Pt;@$ЃEMd Y_^[]ËE3=‹ËeE3Md Y_^[]%| @%x @h@d5D$l$l$+SVW`0@1E3PeuEEEEdËMd Y__^[]QËUuuu uh@h`0@]ËVhh3V t VVVVV^3ËU`0@eeSWN@;t t Уd0@`VEP @u3u @3 @3 @3EP @E3E3;uO@ u 5`0@։5d0@^_[%L @% @%T @%X @; `0@u%d @%h @%l @U(1@ 1@1@1@51@=1@f1@f 1@f1@f1@f%1@f-|1@1@E1@E1@E1@0@1@0@0@ 0@`0@d0@ @0@j9Yj @h @ @=0@ujYh 0 @P @%p @n%R%*%%$$$$$~$v$`$>%`#p#####L#####$($:$J$6#(####"""""#""G@@5@0@0@H`0@ !@@@@{@@"" "#8 !% n%R%*%%$$$$$~$v$`$>%`#p#####L#####$($:$J$6#(####"""""#""#_vl_state8vl_configuration_to_string_copyvl.dll_amsg_exit__getmainargs,_cexit|_exitf_XcptFilterexit__initenv_initterm_initterm_e<_configthreadlocale__setusermatherr _adjust_fdiv__p__commode__p__fmodej_encode_pointer__set_app_typeC?terminate@@YAXXZMSVCR90.dll_unlock__dllonexitv_lock_onexit`_decode_pointers_except_handler4_common _invoke_watson?_controlfp_sK_crt_debugger_hookInterlockedExchange!SleepInterlockedCompareExchangeSetUnhandledExceptionFilterTQueryPerformanceCounterfGetTickCountGetCurrentThreadIdGetCurrentProcessIdOGetSystemTimeAsFileTime-TerminateProcessGetCurrentProcess>UnhandledExceptionFilterIsDebuggerPresentKERNEL32.dllfloatdoubleint8int16int32int64int8int16int32int64N@D0 HX@V PAPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGvlfeat-0.9.14/bin/win32/test_imopv.exe000066400000000000000000000170001167540017400174770ustar00rootroot00000000000000MZ@ !L!This program cannot be run in DOS mode. $S0 d u b% r `RichPEL`N  R @Pi_!P@!@ .text   `.rdata| @@.data0@.rsrc@@@ @VW @= @P0 @u @VQ4 @ @_^̡ @HlVW @= @P0 @u @VQ4 @ @_^VW @= @P0 @u @VQ4 @ @_F^̋ @Q P҃Ë @Q,PỸ|$ SUVW}{ @H h!@3D$0t$3yJB3Ƀ Ѐ3ۀT$0@3=\$D$\|ԋt$F;t$|ST$4D$0PBL$ QP @ t,5 @PPhp0@у _^][Ë|$l$  @BUWh0@Ћ @Q V҉D$ @H Vы @D$,B j<Ћ𸉈=FFF FFFFF F$F(F,F0F4F8 @Al @ @D$T$0D$jjjjVWUWRUPӃ,l$u݋ @L$ @T$$h0@Ћ @ Al @D$T$0D$jjjjVWUWRUPӃ,l$u݋ @ @ $h0@ыT$< @UWRh0@ӋD$,WUPh1@W|$HUWh1@ӋL$l @B,QЋ @Q,@VҋD$ @Q,Pҡ @H,Wу _^]3[h@\1@$L1@5X1@L1@h<1@h@1@h81@ @H1@}jYjhx!@3ۉ]dp]俔4@SVW$ @;t;u3Fuh( @3F4@;u jVY;4@u,54@h @h @YYtE5T1@4@;uh @h @bYY4@9]uSW, @94@th4@yYt SjS4@<1@ @ @5<1@5@1@581@ P1@9D1@u7PT @E MPQYYËeEP1@39D1@uP @9T1@u @EP1@øMZf9@t3M<@@8PEuH t uՃv39xtv39jD1@d @j` @YY4@4@\ @ d1@X @ `1@ @4@P=,1@u h@P @Y]=(1@u jL @Y3qUE8csmu*xu$@= t=!t="t=@u3]h\@8 @3% @jh!@^54@5| @YEu ux @YgjYe54@։E54@YYEEPEPu5` @YPKEu֣4@uփ4@E EjYËUuNYH]ËVh!@h!@W;stЃ;r_^ËVp!@p!@W;stЃ;r_^% @UMMZf9t3]ËA<8PEu3ҹ f9H‹]̋UEH<ASVq3WDv} H ;r X;r B(;r3_^[]̋Ujh!@hu@dPSVW01@1E3PEdeEh@*tUE-@Ph@Pt;@$ЃEMd Y_^[]ËE3=‹ËeE3Md Y_^[]%D @%H @hu@d5D$l$l$+SVW01@1E3PeuEEEEdËMd Y__^[]QËUuuu uhv@h01@]ËVhh3V t VVVVV^3ËU01@eeSWN@;t t У41@`VEP @u3u @3 @3 @3EP @E3E3;uO@ u 501@։541@^_[%h @%l @%p @%t @; 01@u% @% @% @U(p2@ l2@h2@d2@5`2@=\2@f2@f |2@fX2@fT2@f%P2@f-L2@2@Et2@Ex2@E2@1@x2@t1@h1@ l1@01@41@ @1@j9Yj8 @h!@ @=1@ujYh  @P @% @Z&>&*&&%%%%%j%b%L%>%0%%###$$#@$P$^$p$$$$$$$$$ %%#####0$~#t#b#X#F#6###"@q@@`@h1@1@H01@`!@u!@5@N@@@"# d"$@ $"n& Z&>&*&&%%%%%j%b%L%>%0%%###$$#@$P$^$p$$$$$$$$$ %%#####0$~#t#b#X#F#6###"#_vl_statevl_unlock_statevl_thread_specific_state_newrvl_lock_statevl_pgm_write_fvl_tocXvl_imconvcol_vfvl_ticvl_pgm_read_new_fvl.dll_amsg_exit__getmainargs,_cexit|_exitf_XcptFilterexit__initenv_initterm_initterm_e<_configthreadlocale__setusermatherr _adjust_fdiv__p__commode__p__fmodej_encode_pointer__set_app_typeC?terminate@@YAXXZMSVCR90.dll_unlock__dllonexitv_lock_onexit`_decode_pointers_except_handler4_common _invoke_watson?_controlfp_sK_crt_debugger_hook5TlsSetValue4TlsGetValueInterlockedExchange!SleepInterlockedCompareExchangeSetUnhandledExceptionFilterTQueryPerformanceCounterfGetTickCountGetCurrentThreadIdGetCurrentProcessIdOGetSystemTimeAsFileTime-TerminateProcessGetCurrentProcess>UnhandledExceptionFilterIsDebuggerPresentKERNEL32.dllfloatdoubleint8int16int32int64int8int16int32int64l1l2chi2kl1kl2kchi2test_imopv: error: %s (%d) test_imopv: width: %d, height: %d Elapsed time no SIMD: %f [s] Elapsed time with SIMD: %f [s] /tmp/src.pgm/tmp/test.pgm/tmp/test2.pgmN@D0 HX@V PAPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGvlfeat-0.9.14/bin/win32/test_mathop.exe000066400000000000000000000240001167540017400176330ustar00rootroot00000000000000MZ@ !L!This program cannot be run in DOS mode. $)GGG<GLGLGLGFGLGLGRichGPELaN  `0@`$2PP`1@0.text `.rdata00@@.datad@ @.rsrcP$@@0@QD$Y7_0@$\$D$+ȉL$D$$0@\$D$\$D$YD$0@\$D$T$ 0+ȸ_‰L$D$D$0@V3T;r+@y^V3Tf;r+@y^S2:r*y[U00@HSUVWhp@@hx@@h@@h@@3h@@l$D\$@T1@\$P1@\$0@D$0@0@Y7_\$4T$ T$ +‰D$ D$ D$4\$ D$ \$8\$4D$4\$4D$4D$8\$8|$8 H1@\$8D$,D$8{\$,D$D$\$ @1@\$D$81@ 0@D$,Q$h@@h@@D$<01@Au0@Hh@@h@@уD$00@T1@\$3P1@\$D$@1@81@D$A\$\$D${؉L$,0@D$,0@B\$$h@@hA@ЃT1@\$3P1@\$D$@1@81@D$A\$\$D${؉L$,0@D$, 0@Q\$$h$A@h0A@T$D\$(1@\$D$0@\$ D$ T$$@0+ȸ_‰L$ D$$D$ 0@\$4%\$8D$8D$4 H1@\$8D$,D$8{\$,D$D$\$ @1@\$D$ 1@2 0@D$,Q$hDA@hXA@D$<01@Au0@HhDA@h|A@уD$0\$3(1@\$D$@1@ 1@D$A\$\$D${؉L$,0@D$,0@B\$$hDA@hA@Ѓ\$3(1@\$D$@1@ 1@D$A\$\$D${؉L$,0@D$, 0@Q\$$hA@hA@ҡ0@HhA@ы0@BhA@hA@hA@hA@hB@Ѓ033ҹIҍD;r+Byw0@\$8 0@QhB@h0B@1@D$@0@HU\$$h8B@у33։T$8D$8}1@$}|$ D$ D$@l$8|$8D$8l$w0@\$8 0@QhPB@h\B@1@D$@0@HU\$$hdB@у;t0@Bh|B@ЃD$0333Tf;r+@yGfw0@\$80@HhB@hB@1@D$@0@BS\$$hB@Ѓ0@3D$8D$,fD$,?$.D$4|$ D$ D$@l$8\$8fL$8fl$u0@\$80@BhB@hB@1@D$@ 0@QS\$$hB@҃f;t0@HhC@уnl$050@D$D$\$2ҹҊ:r*ƒyT$D$u0@\$80@BhLC@h`C@1@D$@|$ 0@QW\$$hhC@҃2D$0D$0$D$8|$ D$ D$@l$8\$8D$8؃l$u0@\$8 0@QhC@hC@1@D$@0@HW\$$hC@у8\$t0@BhC@ЃF_^][]_^][]̀@s sË%0@%0@h@D@$ D@5D@ D@hC@hD@hC@0@D@}jYjh1@3ۉ]dp]TG@SVW$0@;t;u3Fuh(0@3FPG@;u jVY;PG@u,5PG@h0@h0@YYtE5D@PG@;uh0@h0@dYYPG@9]uSW,0@9`G@th`G@{Yt SjS`G@C@ 0@5C@5D@5C@X D@9D@u7P0@E MPQYYËeED@39D@uP0@9D@u0@ED@øMZf9@t3M<@@8PEuH t uՃv39xtv39jD@0@jP0@YYXG@\G@L0@ $D@H0@ D@D0@LG@P=C@u h @@0@Y_=C@u j<0@Y3sUE8csmu*xu$@= t=!t="t=@u3]hj@ 0@3%0@jh1@`5\G@5l0@YEu uh0@YgjYe5\G@։E5XG@YYEEPEPu5P0@YPMEu֣\G@uփXG@E EjYËUuNYH]ËV1@1@W;stЃ;r_^ËV1@1@W;stЃ;r_^%0@̋UMMZf9t3]ËA<8PEu3ҹ f9H‹]̋UEH<ASVq3WDv} H ;r X;r B(;r3_^[]̋Ujh2@h @dPSVWC@1E3PEdeEh@*tUE-@Ph@Pt;@$ЃEMd Y_^[]ËE3=‹ËeE3Md Y_^[]%T0@%80@h @d5D$l$l$+SVWC@1E3PeuEEEEdËMd Y__^[]QËUuuu uh!@hC@]ËVhh3V t VVVVV^3ËUC@eeSWN@;t t УC@`VEP 0@u3u0@30@30@3EP0@E3E3;uO@ u 5C@։5C@^_[%X0@%\0@%`0@%d0@; C@u%p0@%t0@%x0@U(0E@ ,E@(E@$E@5 E@=E@fHE@f UnhandledExceptionFilterIsDebuggerPresentKERNEL32.dllfloatdoubleint8int16int32int64int8int16int32int64l1l2chi2kl1kl2kchi2chksumeval/selaps [s]func%20s %10s %10s %10s vl_fast_resqrt_f%20s (max relative error: %.6f%%) error: %s: relative error is too large! %20s %10.2g %10.2g baseline%20s %10.2g %10.2g vl_fast_resqrt_d%20s (max relative error: %.6f%%) error: %s: relative error is too large! %20s %10.2g %10.2g baseline%20s %10.2g %10.2g chksumeval/selaps [s]func%20s %10s %10s %10s vl_fast_sqrt_ui32%20s %10.2f %10.2g %010x baseline%20s %10.2f %10.2g %010x error: vl_fast_sqrt_ui32 != floor(sqrt((double) x)) vl_fast_sqrt_ui16%20s %10.2f %10.2g %010hx baseline%20s %10.2f %10.2g %010hx error: vl_fast_sqrt_ui16 != floor(sqrt((double) x)) vl_fast_sqrt_ui8%20s %10.2f %10.2g %010hhx baseline%20s %10.2f %10.2g %010hhx error: vl_fast_sqrt_ui8 != floor(sqrt((double) x)) N@D0 HXPV PAPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGvlfeat-0.9.14/bin/win32/test_mathop_abs.exe000066400000000000000000000160001167540017400204610ustar00rootroot00000000000000MZ@ !L!This program cannot be run in DOS mode. $W4 d u b? r `RichPELaN  R @P3!P@!@ .text   `.rdata @@.data0@.rsrc@@@QP @D$D$} @ @̡ @̋ @Q P҃Ë @Q,PYD$\$D$̃SUVW @D$ @H hы @؋B hЋ|$+D$$L$Q @D$D$} @ @l$%!@\.^u @+ÃD$|TA$;\$D$_D/\$D$_F\$D$_F\$D$_|;s\$D$_;r5 @ @V$hp0@҃ 3D$P @Fr|$ @|$|P;\$D$_D/\$D$_F\$D$_F\$D$_|;s $\$D$_;r5 @ @N$h0@ыT$$ @H,Rы @B,SЃ_^]3[h@0@$0@50@0@h0@h0@h0@ @0@}jYjhx!@3ۉ]dp]4@SVW$ @;t;u3Fuh( @3F4@;u jVY;4@u,54@h @h @YYtE50@4@;uh @h @bYY4@9]uSW, @94@th4@yYt SjS4@0@ @50@50@50@f 0@90@u7P @E MPQYYËeE0@390@uP @90@u @E0@øMZf9@t3M<@@8PEuH t uՃv39xtv39j0@T @j @YY4@ 4@L @ 0@H @ 0@D @3@P=0@u h@@ @Y]=0@u j< @Y3qUE8csmu*xu$@= t=!t="t=@u3]h\@ @3% @jh!@^5 4@5l @YEu uh @YgjYe5 4@։E54@YYEEPEPu5 @YPKEu֣ 4@uփ4@E EjYËUuNYH]ËVh!@h!@W;stЃ;r_^ËVp!@p!@W;stЃ;r_^% @UMMZf9t3]ËA<8PEu3ҹ f9H‹]̋UEH<ASVq3WDv} H ;r X;r B(;r3_^[]̋Ujh!@hu@dPSVW0@1E3PEdeEh@*tUE-@Ph@Pt;@$ЃEMd Y_^[]ËE3=‹ËeE3Md Y_^[]%P @%8 @hu@d5D$l$l$+SVW0@1E3PeuEEEEdËMd Y__^[]QËUuuu uhv@h0@]ËVhh3V t VVVVV^3ËU0@eeSWN@;t t У0@`VEP @u3u0 @3 @3 @3EP @E3E3;uO@ u 50@։50@^_[%X @%\ @%` @%d @; 0@u%p @%t @%x @U(1@ 1@1@1@51@=1@f1@f 1@f1@f1@f%1@f-1@1@E1@E1@E1@01@1@0@0@ 0@0@0@ @(1@j9Yj @h!@ @=(1@ujYh  @P @%| @%%%%n%B%2%%$$$$X%######z##$0$:$H$P$Z$l$$$$n#f#X#P#F#6#(####"""@q@@=A?0@01@H0@`!@u!@5@N@@@" # \"$$8 $"% %%%%n%B%2%%$$$$X%######z##$0$:$H$P$Z$l$$$$n#f#X#P#F#6#(####"""vl_rand_uint32#_vl_statevl_tocvl_tic@vl_get_randvl.dll_amsg_exit__getmainargs,_cexit|_exitf_XcptFilterexit__initenv_initterm_initterm_e<_configthreadlocale__setusermatherr _adjust_fdiv__p__commode__p__fmodej_encode_pointer__set_app_typeC?terminate@@YAXXZMSVCR90.dll_unlock__dllonexitv_lock_onexit`_decode_pointers_except_handler4_common _invoke_watson?_controlfp_sK_crt_debugger_hookInterlockedExchange!SleepInterlockedCompareExchangeSetUnhandledExceptionFilterTQueryPerformanceCounterfGetTickCountGetCurrentThreadIdGetCurrentProcessIdOGetSystemTimeAsFileTime-TerminateProcessGetCurrentProcess>UnhandledExceptionFilterIsDebuggerPresentKERNEL32.dllfloatdoubleint8int16int32int64int8int16int32int64l1l2chi2kl1kl2kchi2fabsf: %.3f vl_abs_f: %.3f N@D0 HX@V PAPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGvlfeat-0.9.14/bin/win32/test_nan.exe000066400000000000000000000150001167540017400171170ustar00rootroot00000000000000MZ@ !L!This program cannot be run in DOS mode. $W4 d u b; 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p0@u%p @%t @%x @U(1@ 1@1@1@51@=1@f1@f 1@f1@f1@f%1@f-1@1@E1@E1@E1@1@1@0@0@ 0@p0@t0@ @0@j9Yj @h @ @=0@ujYh  @P0 @%| @%%|%N%8%"%%$$$$$h%#######r##$$($0$:$L$f$x$$f#Z#R#D#<#2#"##""""@@@.@=A0@1@Hp0@@!@@@@k@@"" <"#8 "% %%|%N%8%"%%$$$$$h%#######r##$$($0$:$L$f$x$$f#Z#R#D#<#2#"##""""vl_rand_uint32vl_rand_seed_by_arrayvl_rand_initvl.dll.printfMSVCR90.dll_amsg_exit__getmainargs,_cexit|_exitf_XcptFilterexit__initenv_initterm_initterm_e<_configthreadlocale__setusermatherr _adjust_fdiv__p__commode__p__fmodej_encode_pointer__set_app_typeC?terminate@@YAXXZ_unlock__dllonexitv_lock_onexit`_decode_pointers_except_handler4_common _invoke_watson?_controlfp_sK_crt_debugger_hookInterlockedExchange!SleepInterlockedCompareExchangeSetUnhandledExceptionFilterTQueryPerformanceCounterfGetTickCountGetCurrentThreadIdGetCurrentProcessIdOGetSystemTimeAsFileTime-TerminateProcessGetCurrentProcess>UnhandledExceptionFilterIsDebuggerPresentKERNEL32.dll1000 outputs of vl_rand_uint32() %10u 1000 outputs of vl_rand_real2() %10.8f N@D0 HX@V PAPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGvlfeat-0.9.14/bin/win32/test_stringop.exe000066400000000000000000000230001167540017400202070ustar00rootroot00000000000000MZ@ !L!This program cannot be run in DOS mode. $Pw1$1$1$$1$cR$1$cC$1$cT$1$1$1$cD$1$cV$1$Rich1$PELbN  < @P{!P@ @ .text~ `.rdata  @@.data0@.rsrc@"@@|$uSV5 @WPQh0@֋= @@P׋D$ T$RPփ@P @ h0@փ@P׃j @_^́ L5@3ĉ$VW= @h0@D$jP׋h0@Q$0@X0@H@u+3;h0@%0@R(h0@D$jP׋Ah0@Q*1@0@H@u+3;h1@+,1@R= @hl0@j\j*ht0@D$(hP׋h@1@Q3\1@(0@D$:utP:Quu33҅hp1@41@R=0@H@u+3;h1@5Q1@ hl0@j\j*ht0@T$$jR׋@h1@P<1@0@(H@u+3;h2@=Q2@hl0@j\j*ht0@jj׋Bh02@RCL2@v0@(H@u+3;h`2@DQ|2@F5 @jh2@T$ hR=3@h2@IQ2@ 2@D$:utP:Quu33҅h2@J2@Rjh2@D$ hP=3@h 3@NQ 3@ \3@D$:utP:Quu33҅h43@OH3@R8jhd3@D$ hP=3@ht3@SQ3@ 3@D$I:utP:Quu33҅h3@T3@Rjh3@D$ hP=3@h3@XQ3@x (4@D$:utP:Quu33҅h4@Y4@R+5 @D$Ph04@3Ƀ|$hh5@^Qx4@T$ RW֋3|$(hi5@4@P`L$0QW3҃|$8hj5@b4@RD$@PW3Ƀ@|$hk5@dQ4@zT$RW֋3|$hl5@4@PfQL$ QW֋hm5@3҃|$,h¹4@R(D$0PW3Ƀ|$8hn5@jQ4@85@I:utP:Quu33҅Vh5@k5@R h,5@h@5@ @ P @$ _^33 ; 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(%5d,%5d)=%5d dE: %10.3g I: %6.4g H: %6.4g updt: %5d floatdoubleint8int16int32int64int8int16int32int64floatdoubleint8int16int32int64int8int16int32int64l1l2chi2kl1kl2kchi2floatdoubleint8int16int32int64int8int16int32int640.9.14yesnoVLFeat version %s Static config: %s %d CPU(s): %s Debug: %s floatdoubleint8int16int32int64int8int16int32int64 =%s: option requires an argument -- %s %s: illegal option -- %s %s: illegal option -- %c %s: option requires an argument -- %c floatdoubleint8int16int32int64int8int16int32int64hikmeans: branch at depth %d: %6.1f %% completed floatdoubleint8int16int32int64int8int16int32int64l1l2chi2kl1kl2kchi2floatdoubleint8int16int32int64int8int16int32int64 SSE42 SSE41 SSE3 SSE2 SSE MMX%s%s%s%s%s%s%sIX86, little_endianWindows_threads, SSE2Microsoft Visual C++ %d ILP32%s, %s, %sfloatdoubleint8int16int32int64int8int16int32int64ikm: training with %d data ikm: %d clusters ikm: Elkan algorithm: total iterations: %d ikm: Elkan algorithm: distance calculations: %d (speedup: %.2f) floatdoubleint8int16int32int64int8int16int32int64l1l2chi2kl1kl2kchi2floatdoubleint8int16int32int64int8int16int32int64floatdoubleint8int16int32int64int8int16int32int64l1l2chi2kl1kl2kchi2floatdoubleint8int16int32int64int8int16int32int64l1l2chi2kl1kl2kchi2kmeans: Lloyd iter %d: energy = %g kmeans: Elkan: total dist. calc.: %d (%.2f %% of Lloyd) kmeans: Lloyd terminating because maximum number of iterations reached kmeans: Lloyd terminating because the algorithm fully converged kmeans: Lloyd iter %d: restarted %d centers kmeans: Elkan iter 0: energy = %g, dist. calc. = %d kmeans: Elkan iter %d: energy <= %g, dist. calc. = %d kmeans: Elkan iter %d: restarted %d centers kmeans: Elkan iter %d: total dist. calc. per type: UB: %.1f%% (%d), LB: %.1f%% (%d), intra_center: %.1f%% (%d), new_center: %.1f%% (%d) kmeans: Elkan terminating because maximum number of iterations reached kmeans: Elkan terminating because the algorithm fully converged kmeans: Elkan: there have been %d restarts kmeans: Elkan: total dist. calc. per type: init: %.1f%% (%d), UB: %.1f%% (%d), LB: %.1f%% (%d), intra_center: %.1f%% (%d), new_center: %.1f%% (%d), finalize: %.1f%% (%d) kmeans: Lloyd iter %d: energy = %g kmeans: Lloyd terminating because maximum number of iterations reached kmeans: Lloyd terminating because the algorithm fully converged kmeans: Lloyd iter %d: restarted %d centers kmeans: Elkan iter 0: energy = %g, dist. calc. = %d kmeans: Elkan iter %d: energy <= %g, dist. calc. = %d kmeans: Elkan iter %d: restarted %d centers kmeans: Elkan iter %d: total dist. calc. per type: UB: %.1f%% (%d), LB: %.1f%% (%d), intra_center: %.1f%% (%d), new_center: %.1f%% (%d) kmeans: Elkan terminating because maximum number of iterations reached kmeans: Elkan terminating because the algorithm fully converged kmeans: Elkan: total dist. calc.: %d (%.2f %% of Lloyd) kmeans: Elkan: there have been %d restarts kmeans: Elkan: total dist. calc. per type: init: %.1f%% (%d), UB: %.1f%% (%d), LB: %.1f%% (%d), intra_center: %.1f%% (%d), new_center: %.1f%% (%d), finalize: %.1f%% (%d) kmeans: repetition %d of %d kmeans: K-means initialized in %.2f s kmeans: K-means termineted in %.2f s with energy %g floatdoubleint8int16int32int64int8int16int32int64l1l2chi2kl1kl2kchi2floatdoubleint8int16int32int64int8int16int32int64l1l2chi2kl1kl2kchi2floatdoubleint8int16int32int64int8int16int32int64l1l2chi2kl1kl2kchi2floatdoubleint8int16int32int64int8int16int32int64floatdoubleint8int16int32int64int8int16int32int64l1l2chi2kl1kl2kchi2floatdoubleint8int16int32int64int8int16int32int64l1l2chi2kl1kl2kchi2%d%d%d %udInvalid PGM dataP5 %d %d %d Error writing PGM datarbError opening PGM file `%s' for readingvl_pgm_read(): PGM with BPP > 1 not supportedwbError opening PGM file for writingfloatdoubleint8int16int32int64int8int16int32int64l1l2chi2kl1kl2kchi2߰floatdoubleint8int16int32int64int8int16int32int64l1l2chi2kl1kl2kchi2floatdoubleint8int16int32int64int8int16int32int64l1l2chi2kl1kl2kchi2floatdoubleint8int16int32int64int8int16int32int64l1l2chi2kl1kl2kchi2floatdoubleint8int16int32int64int8int16int32int64://asciibinasciibinN@D0 HXV PAPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDING00#0011P3I4667c77777748f8w88888888F999 :<:^:h:l:p:t:x:|::::::::M;E>>??L?x?? 04001Z162I2x22 3!3P3c3)4:4K4m44455d5u55P7r79::: ;;;F=L=>>>>>>>? ????4?B?a?g?q?w?????????????0020D0J0P0V0\0a0f0k0p0v0|00000000000000011 1"1(1-141G1O1T1Z1r1x1}111111111222"2'2-282R2X2]2d2w22222222222222"3(3-343G3O3T3Z33333334 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PADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGvlfeat-0.9.14/bin/win64/mser.exe000066400000000000000000000430001167540017400162600ustar00rootroot00000000000000MZ@ !L!This program cannot be run in DOS mode. $Ǹǩǩǩǩ먕<ǩ먕-ǩ먕:ǩǨǩ먕*ǩ먕8ǩRichǩPEdN#  ")@f8Pp`P0X.textq  `.rdata20$@@.dataP 6@.pdataP`@@@.rsrcpB@@HA"H\$WH "H=!"A HHu !H؋H- !HH\$0H _H\$WH HHHtqHT$0H!HD$0tG~ Hc!Ht:HKLǺT!H=r H\$8H _øH\$8H _3H\$8H _H\$WH09IHu 3H\$HH0_HT$(LAA%HHt$@D$  H=rHt$@H\$HH0_Ã;t:HH#HHujHt$@H\$HH0_Ht$@H\$H3H0_@SH HHHtHǃH [̊‰HAX̋AdHAx̋̋@UVWATAUAVAW!H+)$ HEH3H$ #H=E3DHAD$PD$pD$hH$A3D$XD$`H$D|$8D|$4ADt$QL$AH$`.DH$3A$D$5L$fWH$P!L LHALt$ =>=:@? dthvH FLD$PH<H t f/|$PiH"Lc<H$LcNLPXHHD$H HT$0=L$0H؃tL~$N$HHH$LúH=HvL<H$LcNLHA4&IH #LD$pHc;H 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HuHl$x$H$H,4 L$H$H4 $tH$L$H4n $tH$L$ H3G H$H32 H$H$,L2H$ H$H$Mt I MtI Dd$0Dd$0Mt I HtH AAHt Hd H$HtQ 3H$3H$Ht0 H$H$Ht H$Et,: L$H2HH`E AD|$<l$H  9=.u N( H"HĈH(H8csmu+xu%@ = t=!t="t=@u3H(H(H 3H(%n@SH HH 2HD$8Hu H~H 2HD$8H 2HD$@HpHLD$@HT$8hHHL$8PHq2HL$@>HW21HH [H(GHH(H\$WH H H= HHtHH;rH\$0H _H\$WH H H= HHtHH;rH\$0H _%>%@HMZf9t3HcH>>>>>>>>>=="=*=4=>=F=N=X=f=r=|=========>>$>8>D>N>`>r><<<<<<<v<`<J<<<*<<<;;;;;v;d;X;'@)@+@vhd:Usage: %s [options] files ... Options include: --verbose -v Be verbose --help -h Print this help message --seeds Specify seeds file --frames Specify frames file --meta Specify meta file --delta -d Specify MSER delta paramter --epsilon -e Specify MSER epsilon parameter --no-dups Remove duplicate --dups Keep duplicates --max-area Specify maximum region (relative) area --min-area Specify minimum region (relative) area --max-variation Specify maximum absolute region stability --bright-on-dark Enable or disable bright-on-dark regions (default 1) --dark-on-bright Enable or disable dark-on-bright regions (default 1) P@vP@hP@dP@P@P@P@P@P@P@P@Q@?Y@Y@  4 2p  4 2p  4 Rp!dJx7!dJx7!dJx7203 !x  p`P& !4*720b2P  d 2p,'(/(  B20,+I,,/  4 2p B,w--P/-  4 2p:=19=09$@0 @??????r?^?@?$??>>>>>>>>>>=="=*=4=>=F=N=X=f=r=|=========>>$>8>D>N>`>r><<<<<<<v<`<J<<<*<<<;;;;;v;d;X;#_vl_statevl_unlock_statevl_thread_specific_state_newrvl_lock_statevl_string_copyvl_string_lengthvl_string_parse_protocolvl_string_replace_wildcardsvl_mser_deletetvl_mser_ell_fitvvl_mser_processuvl_mser_newyvl_pgm_extract_data|vl_pgm_get_npixels{vl_pgm_get_bppzvl_pgm_extract_headvl_string_basenamevl_string_protocol_nameCvl_get_version_string'optind%optarg$getopt_longvl.dllfopenfclosefreemallocprintfsabortexitfprintf__iob_func(_snprintfsscanfmemsetMSVCR90.dll_amsg_exit__getmainargsY__C_specific_handlerU_XcptFilterF_exit_cexit__initenv_initterm_initterm_e _configthreadlocale__setusermatherr_commodeb_fmode7_encode_pointer__set_app_type__crt_debugger_hookC?terminate@@YAXXZ_unlock__dllonexit=_lock_onexit-_decode_pointer9TlsSetValue8TlsGetValue%Sleep1TerminateProcessGetCurrentProcessBUnhandledExceptionFilterSetUnhandledExceptionFilterIsDebuggerPresentRtlVirtualUnwindRtlLookupFunctionEntryRtlCaptureContextNQueryPerformanceCounterfGetTickCountGetCurrentThreadIdGetCurrentProcessIdOGetSystemTimeAsFileTimeKERNEL32.dllfloatdoubleint8int16int32int64int8int16int32int64%g %d %lgl1l2chi2kl1kl2kchi2verbosehelpdeltaseedsframesmetamax-areamin-areamax-variationmin-diversitybright-on-darkdark-on-bright%.frame%.frame%.mser%.mser%.meta%.metaInvalid option '%s'.Missing mandatory argument for option '%s'.0.2MSERs filespec: `%s' Frames filespec: `%s' Meta filespec: `%s' Version: driver %s; libvl %s %lf%lfThe argument of '%s' must be a non-negative number.%lfmax-area argument must be in the [0,1] range.min-area argument must be in the [0,1] range.%lf%lfmax-variation argument must be non-negative.min-diversity argument must be in the [0,1] range.The arguments of '%s' is invalid.The arguments of '%s' is invalid.The arguments of '%s' is invalid.meta file supports only ASCII protocol%dbright_on_dark must be 0 or 1.%ddark_on_bright must be 0 or 1.%s: error: %s (%d) rbmser: frames output mser: active %d mser: pattern %s mser: protocol %s mser: seeds output wmser: active %d mser: pattern %s mser: protocol %s mser: meta output mser: active %d mser: pattern %s mser: protocol %s Basename of '%s' is too longmser: processing '%s' mser: basename is '%s' Could not open '%s' for reading.Output file name too long.Could not open '%s' for writing.wOutput file name too long.Could not open '%s' for writing.wOutput file name too long.Could not open '%s' for writing.mser: writing seeds to '%s' mser: writing frames to '%s' mser: writing meta to '%s' PGM header corrputed.mser: image is %d by %d pixels Could not allocate enough memory.PGM body corrputed.%d Could not create an MSER filter.mser: parameters: mser: delta = %d %f mser: max_area = %g mser: min_area = %g %d mser: max_variation = %g %f mser: min_diversity = %g Could not allocate enough memory. mser: err: %s (%d) u2-+] f_`7p l7Jx7J777 77T&7T&&8&&L8'}'8') 8))L8))L8)F+D8H++L8++L8+\,T8\,s,L8t,,t8,,t8p--8-w.8./8/,/8,/E/8P/q/80 HXpX PADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGvlfeat-0.9.14/bin/win64/msvcr90.dll000066400000000000000000023110001167540017400166070ustar00rootroot00000000000000MZ@ !L!This program cannot be run in DOS mode. $2KaKaKal;aHaKaaUCaaUUaSaUDaaURaJaUTaJaUQaJaRichKaPEd>1G"  n&&bx   @0qCx_( ` En $  .textmn `.rdatasijr@@.datad<@.pdata E` F @@.rsrc ^ @@.reloc b @BH  H\$WH H5H  u!HHJ!H HH\$0H _3H } (!@SH HH ` !HtHЅt3H [H(3E3H(H8t*t%2XHd$ E3E333c> l* f* H8̋R* H8x ~ uX! 1P! 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HG XDAu*s$H-L_ A s`HG ``HG XPA;t2H@7L_ AK HG w p`HG ``E;u/t&D9 t HL_ A s`HG ``DgAAuA/GA;t$+t+t;ul$`ALD$pӋpA D9|$pA HD/u3"L_ A s HG D o pHG p`HG ``HG XPL_ A s HG D o p`YA#ƃ@tBu!_ @ !)D9 EHG A#΃t*GA;t+t+t ;u`  L_ AA#ƃt'*uAC`˃< u 'H\$`#AC`˃< uH\$` H؋l$`=DA?@7u DA D/uFEtAHL_ A s D oHG pHG p`HO A`< uiPyHYPpHEt HG p D oDt HG H  wHG p`HO A`< uiPHYPYA#ƃ?tAu!_ @ !)D9 EHG A#΃t*GA;t+t+t ;u`  L_ AA#ƃt*u t l$` R؃?@7u @6A D/u@At;H]L_ A s D oHG pHG p`HW J` J`HO H"At HG p D o@t HG H  wHG p`HO A` A`HG L$`hP73H\$xH A_A^A]A\_^]H$$HH $$HfnfnXf~fnfn\f~fnfnYf~fnfn^f~*f~H*f~fn-fnH-fn,fnH,fnfnIt+It/It3It7It;It?ItCItGf~f~f~f~f~f~f~f~fnfn/fnfn.fnfn_f~fnfn]f~fnQf~HH $$H$ $X$H$HHH $$H$ $\$H$HHH $$H$ $Y$H$HHH $$H$ $^$H$HHH $$H$ $It/ItxH0]@UH0HHMh xH0]@UHPH 腴HP]@UH0HfH0]@UH@HGH@]@UH H(H ]@UH H H ]@UH0HH0]@UH H˳H ]@UH HyH ]@UH H蝾~ 菾H ]@UH H38E8H ]@UH HH ]@UH HHMpHMhHEhHHM(E HE(AcsmD9uMHE(xuCHE(x tHE(x !t HE(x "uHU(HHH(H9J(uE HE(D9u[HE(xuQHE(x tHE(x !t HE(x "u*HE(Hx0u}ǀE E0E0E0H ]@SUH(HHMP} uJAcsmHD9u8{u2{ t{ !t { "uHK(at H苻޼HH˼HHH(][@UH HHM0 H ]@UH@HHE@HD$0HHD$(HHD$ LLExHUpLH@]@UH HHE3Ɂ8AAH ]@UWH(HH38@NjH(_]@SUH8HH3ہ8ËËH8][@UH0H谰H0]@UH HHM8HM(HE(HHM0HE08csmt E E wH ]@UH H} uLMxDEpHUhHM`JH ]@UH0HM`H0]@UH@H}Dt7}Ht'HcU@HHHLtHkXIdU@ȕH@]@UH@H}@t>}Dt*HHc HHLHkXIdH oH@]@UH0HꋍOH0]@UH0Hꋍ/H0]@UH H H ]@UH H H ]@UH H ծH ]@UWH(H39} u'HcUHHHHL/HkXIdUH胔H(_]@UH HgH ]@UH0HMPFH0]@UH0H+H0]@UAVHHH}@u*HcHHHL5HkXIƀdӓHHA^]@UH0HMP贓MX謓H0]@UH@Hꋍ茓H@]@UH H~H ]@UH H3ZH ]@UH0H3>H0]@UH0H}pt3H0]@UH HHE3ہ8AAH ]@UH H լH ]@UH H 趬H ]@UH H 薬H ]@UH0H wH0]@UH0H WH0]@UH0HHEpH0]@UH0HH0]@UWH8HH8_]@UH0H ׫H0]@UWH(H賑H(_]@UH HH3Ɂ8H ]@UWH(HH8t 8t3H(_]@SUH8HËH8][@SUH8HËH8][@UH HH8t 8t3H ]H)#H H*@eVeleeeeeeeee f"f0f>fLfbftffffffffgg(g2gBgVgbgrgggggggg hh$h:hPhjhhhhhhhhii,iFi^iviiiiiiiijj0j@jNj`jlj~jjjjjjjk,k1G* CorExitProcessmscoree.dllruntime error TLOSS error SING error DOMAIN error R6034 An application has made an attempt to load the C runtime library incorrectly. Please contact the application's support team for more information. R6033 - Attempt to use MSIL code from this assembly during native code initialization This indicates a bug in your application. It is most likely the result of calling an MSIL-compiled (/clr) function from a native constructor or from DllMain. R6032 - not enough space for locale information R6031 - Attempt to initialize the CRT more than once. This indicates a bug in your application. R6030 - CRT not initialized R6028 - unable to initialize heap R6027 - not enough space for lowio initialization R6026 - not enough space for stdio initialization R6025 - pure virtual function call R6024 - not enough space for _onexit/atexit table R6019 - unable to open console device R6018 - unexpected heap error R6017 - unexpected multithread lock error R6016 - not enough space for thread data This application has requested the Runtime to terminate it in an unusual way. Please contact the application's support team for more information. R6009 - not enough space for environment R6008 - not enough space for arguments R6002 - floating point support not loaded Microsoft Visual C++ Runtime Library ...Runtime Error! 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All rights reserved.@ OriginalFilenameMSVCR90.DLL^ProductNameMicrosoft Visual Studio 2008> ProductVersion9.00.21022.8DVarFileInfo$Translation 8@H`hp @HPX`hpxȡСء衐ȩЩة (08@HPX`hpxȪЪت (08@HPX`hpxȫЫث (08@HPX`hpxȬЬج4 (08@HPX`h (pЬ 0@P`pЭ 0@P`pЮ 0@P`pЯT 0@P`pР 0@P`pС 0@d(8HXhxȠؠ(8HXhxHPX`hpxХॠ ȢТآ (08@HPX`hpxȣУأ 8@Hp8@Hpp8XxШب @HPX`hpxȩЩة (08@HPX`hpxȪЪت@HP`ȫЫ   (08@HPX`hpxȮЮخ (08@HPX`hpxȯЯد  (08@HPX`hpxȠРؠ (08@HPX`hpxȡСء (08@HPX`hpxȢТآ (08@HPX`hpxȣУأ (08@HPX`hpxȤФؤ (08@HPX`hpxȥХإ(8HXhxȦئ(8HXhx$0# *H #0#1 0 +0h +7Z0X03 +70% <<<Obsolete>>>0!0 +$% D00<<>c@0  *H 0p1+0)U "Copyright (c) 1997 Microsoft Corp.10U Microsoft Corporation1!0UMicrosoft Root Authority0 970110070000Z 201231070000Z0p1+0)U "Copyright (c) 1997 Microsoft Corp.10U Microsoft Corporation1!0UMicrosoft Root Authority0"0  *H 0 p;N(x^0ꢩ%_L >|Q`2kBdyvT뜆fkzb#<Ŀ-fh&:, X&F >8,(9IBlUa|`-wL陴d;P1$+=c`Xe7RӿUE:TNzmtN̖ (!W`i7Kc@0  *H  5$w\`2 >:!W,Gb;;Z6Ti$m?̪|1=pjOiCZ Ob{+7%-e%cT!RnC2gl QRǽ0 1 )M[WeIRT(~ū7,zwvj?6A5jj5EZ38nM b T?FUp:uҠ00<<>c@0  *H 0p1+0)U "Copyright (c) 1997 Microsoft Corp.10U Microsoft Corporation1!0UMicrosoft Root Authority0 970110070000Z 201231070000Z0p1+0)U "Copyright (c) 1997 Microsoft Corp.10U Microsoft Corporation1!0UMicrosoft Root Authority0"0  *H 0 p;N(x^0ꢩ%_L >|Q`2kBdyvT뜆fkzb#<Ŀ-fh&:, X&F >8,(9IBlUa|`-wL陴d;P1$+=c`Xe7RӿUE:TNzmtN̖ (!W`i7Kc@0  *H  5$w\`2 >:!W,Gb;;Z6Ti$m?̪|1=pjOiCZ Ob{+7%-e%cT!RnC2gl QRǽ0 1 )M[WeIRT(~ū7,zwvj?6A5jj5EZ38nM b T?FUp:uҠ0`0L .P\0 +0p1+0)U "Copyright (c) 1997 Microsoft Corp.10U Microsoft Corporation1!0UMicrosoft Root Authority0 070822223102Z 120825070000Z0y1 0 UUS10U Washington10URedmond10U Microsoft Corporation1#0!UMicrosoft Code Signing PCA0"0  *H 0 y}]E941%5IwEqFԌkLRbMIl/$>e# HuEP%+ #A$bEJͳ/"J-|o;99ݽ)f;-2'Hlc\򸔣8P'N0==l9.4. }bxfs Oc,2EJ;PSfQyV>Pn5{$Rf=N+~3nGўJnS00U% 0 +0U0[pir#Q~Mˡr0p1+0)U "Copyright (c) 1997 Microsoft Corp.10U Microsoft Corporation1!0UMicrosoft Root Authority<<>c@0U00Uvp[NQD.Dc0 U0 +{~J&μNtX't*uLxMi|CʇSŸVocDDȚ  )}s9=j8m҈#i 4|.)Bk(q8 ]hͽAkf4|zB{ p֒O8|-=4 b7j#\cZ9`U3; _,˫ 0  *RA^0(ip΀Brv0j0R axM0  *H 0y1 0 UUS10U Washington10URedmond10U Microsoft Corporation1#0!UMicrosoft Code Signing PCA0 070823002313Z 090223003313Z0t1 0 UUS10U Washington10URedmond10U Microsoft Corporation10UMicrosoft Corporation0"0  *H 0  Iͪ:4#Ykl!"ꮿ&2],6V+ n(L?A@F فWDv\t3ta~E>հ^ { `j(ub]:![sE㕉w`w73/bɮOg 90qgF PtA/$j!kIIJ9Bm\J׎>x Gؤ#{fJt&AKa`N0Ȃ p|UMwUv00U0U!@|QTKj4.&0U% 0 +0U#0vp[NQD.Dc0DU=0;09753http://crl.microsoft.com/pki/crl/products/CSPCA.crl0H+<0:08+0,http://www.microsoft.com/pki/certs/CSPCA.crt0  *H @WoSg 4K<؁ ytwOGi~bg()UיP^ oY Ӌ> EG&`W K̺MnH(5!r_i$4 ~no R*LI2,]&<[ 5OEGYxWN򭜚A\$>4GN{ɜcS׿Ϡz+,lڿ8\ʀݐ#簡L>&x,kǏ<00 aGR0  *H 0y1 0 UUS10U Washington10URedmond10U Microsoft Corporation1#0!UMicrosoft Timestamping PCA0 060916015300Z 110916020300Z01 0 UUS10U Washington10URedmond10U Microsoft Corporation1'0%U nCipher DSE ESN:D8A9-CFCC-579C1'0%UMicrosoft Timestamping Service0"0  *H 0 m܆Q2;7:%oaA;ZxgU~AS(Tp;'H~C2/&Jvwɩ!X~ $D5:T 3,~A ٰ%%ӎmI ÇnFmSF?$~|,y(EO6kSQ ۼʇ 2JQ|,jsrW؛K'3m2hn:."C dɣTx)>Xߪܮeӝg00UOxY |"0U#0oN?4K;AC0DU=0;09753http://crl.microsoft.com/pki/crl/products/tspca.crl0H+<0:08+0,http://www.microsoft.com/pki/certs/tspca.crt0U% 0 +0U0  *H 7'@e&>dĐ>WC @g*ܴ6 3|._P{a1ބiX|'Ep_W^;C?aq}{nZ0 5%aN2t+zrK+:T`ا٩mDk c 2N~7_gFKm}ҾntipKe5D}9nP!>wšWi}io*"73:r0P:ŷ\ͳ00 aI|0  *H 0y1 0 UUS10U Washington10URedmond10U Microsoft Corporation1#0!UMicrosoft Timestamping PCA0 060916015522Z 110916020522Z01 0 UUS10U Washington10URedmond10U Microsoft Corporation1'0%U nCipher DSE ESN:10D8-5847-CBF81'0%UMicrosoft Timestamping Service0"0  *H 0 cyD>L$9v 2JXx+٥=&|}~2=97;4Ԓ4hrLฅ3 vs5L]J}xBFq'WуO;?aK0U#0oN?4K;AC0DU=0;09753http://crl.microsoft.com/pki/crl/products/tspca.crl0H+<0:08+0,http://www.microsoft.com/pki/certs/tspca.crt0U% 0 +0U0  *H iz F.4z'(A*!_S^)TJ} Mn(E,92o7Z#RT?d86C:> q(f1\]RR\듐i LvL4 j|D+[TE$%ĊF%i)",F^+w>#odb ->IZt9ΣּȭV<*D?}fͲ c @=e2|=&HN"n000j O%EXzg0  *H 0p1+0)U "Copyright (c) 1997 Microsoft Corp.10U Microsoft Corporation1!0UMicrosoft Root Authority0 060916010447Z 190915070000Z0y1 0 UUS10U Washington10URedmond10U Microsoft Corporation1#0!UMicrosoft Timestamping PCA0"0  *H 0 7nBJqH>S,2ORȃ>3I1(dPKuǨծipfx'f趷 Y")/@VvmdmJT޿ǀL7VhGv\/}%V[jc|<%M9wt]\؆7,u9 |vlnz>q_*Ob`2҃N+"\hE/Pl%ׅvs6ƕz`3[AXn,HoCj&k(0$0U% 0 +0U0[pir#Q~Mˡr0p1+0)U "Copyright (c) 1997 Microsoft Corp.10U Microsoft Corporation1!0UMicrosoft Root Authority<<>c@0 +70UoN?4K;AC0 +7  SubCA0 U0U00  *H M1|PapEsT? -QS9V ތ;ɷQ!oi~k"Flm|"Fӄ6~p]Eݎ*|ɮ2Շc6!v;s!شTeJ(&`;exHϭ:ObX099!dcC/{FeJtn ̝(a|H!8Ŗ2@S=f7"̰wTQ:rD#1w0s00y1 0 UUS10U Washington10URedmond10U Microsoft Corporation1#0!UMicrosoft Code Signing PCA axM0 +0 *H  1  +70 +7 10  +70# *H  1K'7q}dF0B +7 1402msvcr90.dllhttp://microsoft.com0  *H  M;nЛ]IQ+5 +uc-ܖ˥rȎ+\P葓zud#'MڡWD/ 9E} sKڨ=@wdU ѤͿeK!D-SyQ^i$яiHĖf'AըT)_yFaK߶+ }%?l;|$~RbECqjϡ0 *H  1 000y1 0 UUS10U Washington10URedmond10U Microsoft Corporation1#0!UMicrosoft Timestamping PCA aI|0+]0 *H  1  *H 0 *H  1 071107060411Z0# *H  1e2%8ãeD &0  *H b-);!,OʛR|2 @I+&滸%%\BkAC/Tdk30^*N@̢]ue¥e ŗڔ.Ifb56 @l}\ ˆmq~Zu'hn eykPfyYKJ^LK-a,yE.c4'knڂ-S@T(fb/`b8=p\vlfeat-0.9.14/bin/win64/sift.exe000066400000000000000000000570001167540017400162640ustar00rootroot00000000000000MZ@ !L!This program cannot be run in DOS mode. $ǹǩǩǩǩꤕ<ǩꤕ-ǩꤕ:ǩǨǩꤕ*ǩꤕ8ǩRichǩPEdN#  2(;@YPP.text02 `.rdataP6@@.datapJ@.pdataX@@.rsrcZ@@BBABABABABABAAHQBH\$WH  BH=1B@HHu  BH؋H?AHH\$0H _H\$WH AH=A?HHu AH؋H?AH\$0H _H\$WH HHHtqHT$0H/AHD$0tG~ HAHt:HKLǺAH=r H\$8H _øH\$8H _3H\$8H _H\$WH09IHu 3H\$HH0_HT$(LAA%HHt$@D$ f@H=rIHt$@H\$HH0_Ã;t:HH>HHu Ht$@H\$HH0_Ht$@H\$H3H0_@SH HHHtE>HǃH [L$H(t>D\$0AH(D$?LD$0D$>DBD$1D$=HL$0D$2D$tYHD$PLFHHD$(A%D$ E:H=>t HXH8HHtgHHT$8M9؅9D$0tHH X8HHtI8HdžA;}ZL\$H}@9H=97HHu 9H؋H79I8HL$`L$hH$Ht7HdžH$PH3HpA_A^^][AfW\Bf/vf/v3H\$UVWATAUAVAW;&&H+)$;D)$;H cH3H$;>E3DŽ$@L-L$8$$$AXqH$DHډA\q3fAA^qAAH$KD$LD$HD$dEDt$`EDt$0AADt$DibH$I3A$HDŽ$DqHVH$,3AH$$L$HD$ = bH$)3A$(DŽ$$HPVH$\3AH$TL$( D$PaH$Y3A$XDŽ$TAqH$4 D$0 3҉AqAfAAqL$X#AH$; `0aH$93ADŽ$4$82AqH$t+D$p+AqfAH$z+3AL$8`H$y/3A$x/DŽ$t/H3UH$l#3AH$d#L$x3D$`#b`H$i'3A$h'DŽ$d'`L$h+5;fEWfffL 8L8HӋLt$ 4H4u;:h==ƃ<HA7A6IAD|$0Hd4H$@Hp\H =4LD$LHMTH 3t D9d$L1H 4L#VH$3LcNL83H 3LD$HH(VH G3t D9d$HH3L VH$3LcNL2pH3HDŽ$0 HtgHT$43L$4HI~$4N$4H3HtSH$4 LǺ3H=|$8$4LTH$312|$8HAT7IH2H$ HH2LSH$3LcNL1RHm2H$PHmHF2LSH$3LcNLt1H 2H$`#HH H1LSH$3LcNL'1H1H$p+HH1LSH$3LcNL0kH 1LD$dH TH 0HZ1LSH$3LcNL0H 41L$HSH 0tf/$!H0LSH$3LcNL(0H 0L$HSH 40tfD/$H0LSH$3LcNL/[H v0L$HSH /tf/$cH<0LuSH$3LcNLj//LL$3HzSHH`D$ /U/̽l$D.Hc+H HL$puD9$ u D9$PtD$@AH S.D9$@HSHSH SHE.$Db/H SH.H$DH Ru.H Rh.D9$ HRHRH RHEA.$$.H RH$.H$$H R.H R.D9$PHRHRH RHE-$T.H RH-H$TH R-H R-D9$0 HRHRH RHEu-$40.H RHX-H$4 H }RC-H |R6-D9$p+HRHRH }RHE-$t/-H gRH,H$t+H WR,H VR,D9$`#H]RHZRH WRHE,$d'd-H ARH,H$d#H 1Rw,tH ,Rf,HL$pHL$p5 3)$;=2fH3HL$p|$8HyH$7DMLH$MML$Dt$@,H=r%LQH$3LϺ+] EtH QH+A~H$7H Q+HQHA+HH$Hu%LQH$3LϺXY+HT$PH,t0ehuLQH$3LϺ+EtDD$TT$PH Q*HL$P+HL$P+Hc*HL$PHH$+HL$P+HcH*LHD$xHi H` HT$PLH]+tKLJQH$3W*X LPH$3LϺ0*Y D$P3D$Tt*9Hfn[ADD$PD$TH;r9$`#H$7L$d#H$h'HD$(A%@l$ *H=r99$`#tWH|PH$h'2)H$h+Hu5L$h'LhPH$3=)C H$H$`#33~^fH$H$`#H$H$`#GH$H$`#;}!'IHcH(LH$$H$H$`#B.$AH Dt$@BL+$H BD+$BL+-L ]IcA I (l$0tL$h'H \OA'H$h+H'HDŽ$h+L$h'L OH$3'3 L$h'LNH$3e' L$h'LNH$39' L$h'LXNH$3 ' l$0$@H$7L$DH$HHD$(A%D$ (H=r:$@tWH4NH$HB&H$HHu5L$HL NH$3M&$PH$7L$TH$XHD$(A%D$ J'H=r-:$PtWHMH$X%H$X#Hu5L$XLMH$3%=$ H$7L$$H$(HD$(A%D$ &H=rv:$ tWHRMH$($H$( Hu5:L$(LBMH$3$$0 H$7L$4 H$8HD$(A%D$ %H=r:$0 tWHLH$8$H$8Hu5L$8LLH$3($~|$@tH$HH L#$ tH$(H L#$PtH$XH L#$0 tH$8H L#D$dDL$HDD$LT$TL$PD$ r$L$fA/r$fA/r$fA/rHu$LLH$3#H vL"AT$(H |L"AT$,H L"AT$0H L"H LA$fH~"H LA$fH~"H LA$fH~f"HLHLMH LHED"|$DHLHLH LHE "E3L|$ht$<DL$Dt$@3H$tHT$x3Ỉt$<" I"~AT$].]]\\88@ :@<@Usage: %s [options] files ... Options include: --verbose -v Be verbose --help -h Print this help message --output -o Specify output file --frames Specify frames file --descriptors Specify descriptors file --meta Specify meta file --gss Specify Gaussian scale space files --octaves -O Number of octaves --levels -S Number of levels per octave --first-octave Index of the first octave --edge-thresh Specify the edge threshold --peak-thresh Specift the peak threshold --magnif Specify the magnification factor --read-frames Specify a file from which to read frames --orientations Force the computation of the orientations vhO:S:o:p@vp@hp@Op@Sp@op@p@p@p@p@p@p@p@q@q@@o@?@}@}@  4 2p  4 2p  4 2p  4 Rp!dDX!dDX!dDX20  BB  4 2p!`P07P!Ԍčt@X!@X@.%h4x p`P7;!x$X!$X20b2P  d 2p>890@9  B20>=i=L@  4 2p B>>>p@>  4 2p\>_QZ_PHZbPnbXbBb2bbbaaaaata`aXaJa].]]\\#_vl_statevl_unlock_statevl_thread_specific_state_newrvl_lock_statevl_string_copyvl_string_lengthvl_string_parse_protocolvl_string_replace_wildcard}vl_pgm_insertCvl_get_version_stringvl_sift_deletevl_sift_calc_keypoint_descriptorvl_sift_calc_keypoint_orientationsvl_sift_keypoint_initvl_sift_detectvl_sift_process_next_octavevl_sift_process_first_octavevl_sift_newyvl_pgm_extract_data|vl_pgm_get_npixels{vl_pgm_get_bppzvl_pgm_extract_headvl_string_basenamevl_string_protocol_name%optarg'optind$getopt_longvl.dllfopenfclosefprintffwritesabortfscanffreadfreeprintf(_snprintfmallocqsortreallocexit__iob_funcsscanfmemsetMSVCR90.dll_amsg_exit__getmainargsY__C_specific_handlerU_XcptFilterF_exit_cexit__initenv_initterm_initterm_e _configthreadlocale__setusermatherr_commodeb_fmode7_encode_pointer__set_app_type__crt_debugger_hookC?terminate@@YAXXZ_unlock__dllonexit=_lock_onexit-_decode_pointer9TlsSetValue8TlsGetValue%Sleep1TerminateProcessGetCurrentProcessBUnhandledExceptionFilterSetUnhandledExceptionFilterIsDebuggerPresentRtlVirtualUnwindRtlLookupFunctionEntryRtlCaptureContextNQueryPerformanceCounterfGetTickCountGetCurrentThreadIdGetCurrentProcessIdOGetSystemTimeAsFileTimeKERNEL32.dllfloatdoubleint8int16int32int64int8int16int32int64%g %d %lgl1l2chi2kl1kl2kchi2verbosehelpoctaveslevelsoutputmetaframesdescriptorsgssfirst-octaveedge-threshwbpeak-threshmagnifread-frames0.1orientations_%02d_%03dsift: saved gss level to '%s' %.sift%.sift%.frame%.frame%.descr%.descr%.meta%.meta%.pgm%.pgm%.frame%.frameInvalid option '%s'.Missing mandatory argument for option '%s'.%dSIFT filespec: `%s' Frames filespec: `%s' Descriptors filespec: `%s' Meta filespec: `%s' GSS filespec: '%s' Read frames filespec: '%s' Version: driver %s; libvl %s The arguments of '%s' is invalid.The arguments of '%s' is invalid.The arguments of '%s' is invalid.The arguments of '%s' is invalid.meta file supports only ASCII protocolThe arguments of '%s' is invalid.The arguments of '%s' is invalid.The argument of '%s' must be a non-negative integer.%dThe argument of '%s' must be a non-negative integer.%dThe argument of '%s' must be an integer.%lfThe argument of '%s' must be not smaller than 1.%lfThe argument of '%s' must be a non-negative float.%lfThe argument of '%s' must be a non-negative float.%s: error: %s (%d) yessift: write aggregate . no%3s %-6s %-10s sift: write frames .... yesno%3s %-6s %-10s sift: write descriptors yesno%3s %-6s %-10s sift: write meta ...... yesno%3s %-6s %-10s sift: write GSS ....... yesno%3s %-6s %-10s sift: read frames .... yesno%3s %-6s %-10s sift: will compute orientations Basename of '%s' is too longsift: <== '%s' sift: basename is '%s' rbCould not open '%s' for reading.Cannot read from '%s'.'%s' contains a malformed PGM header.sift: image is %d by %d pixels Could not allocate enough memory.PGM body malformed.rbOutput file name too long.Could not open '%s' for reading'%s' malformed'%s' malformed'%s' malformed'%s' malformedsift: read %d keypoints from '%s' wbOutput file name too long.Could not open '%s' for writingwbOutput file name too long.Could not open '%s' for writingwbOutput file name too long.Could not open '%s' for writingwbOutput file name too long.Could not open '%s' for writingsift: writing all ....... to . '%s' sift: writing frames .... to . '%s' sift: writing descriptors to . '%s' sift: writign meta ...... to . '%s' Could not create SIFT filter.sift: filter settings: sift: octaves (O) = %d sift: levels (S) = %d sift: first octave (o_min) = %d sift: edge thresh = %g yessift: peak thresh = %g nosift: magnif = %g yessift: will source frames? %s nosift: will force orientations? %s sift: GSS octave %d computed Could not write GSS to PGM file.sift: detected %d (unoriented) keypoints sift: err: %s (%d) u2-+] fP X,X8XDX PX _dX_qxXXX%X09X@X0X0bX$X$\5$Y\5x78Yx77HY77Y888XY8 :hY :;Y;;Y;f<Yh<<Y<<Y<|=Y|==Y==Y=>Y>>Y>?Y?.@PY0@L@`YL@e@`Yp@@`Y0 HXX PADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGvlfeat-0.9.14/bin/win64/test_getopt_long.exe000066400000000000000000000170001167540017400206730ustar00rootroot00000000000000MZ@ !L!This program cannot be run in DOS mode. $6r}Hr}Hr}HUHp}Hl/kHp}Hl/|Hw}Hr}HP}Hl/lH}}Hl/~Hs}HRichr}HPEdN#  0@`Vu$"PP@ (.text `.rdata  @@.datap0@.pdata@@@.rsrcP@@H\$Ht$WH0HcH g!HL w Ll!3H֋H\$ DbRtAt(:t?uSH #v{H -#gl#H "RWH Y"CHft,ht=t2/H q"H H 1"H*HL L H֋H\$ :H "LIcH;}HH "HH;|H\$@Ht$H3H0_H8H =D"D "H"L"H"H v"D"HD$ v"y H8Ht$WH eH%0Hx33H=d"tH;u !ؿ8";u ~7""u'="HH qt=!!;uHH :!u 3H!H=!tH !tE3AP3!H HR!HLH!HI! 3!H!=%!ug =4!u P "!-!= u ) ̃=!u !  Ht$0H _H(MZf9t3OHc HHȁ9PEuA= t= u3v93ytv 9\  H H H H a H N w=u H t =u  3H(H(H(H(H8csmu+xu%@ = t=!t="t=@u3H(H(H  3H(%: @SH HH  HD$8Hu H ~H z HD$8H ` HD$@H HLD$@HT$8fHHL$8t H-HL$@b H/HH [H(GHH(H\$WH H H= HHtHH;rH\$0H _H\$WH H H= HHtHH;rH\$0H _%Z %\ HMZf9t3HcH$F$R$^$#$$$$$$$$$%%###l$###`@H@@d 4R pb  d 2pB2P20E  4 2p Bw  4 2p##!"#@ x"% %%p%F%(% %`%$$,$4$>$F$R$^$#$$$$$$$$$%%###l$###'optind%optarg$getopt_longvl.dllsabortprintfMSVCR90.dll_amsg_exit__getmainargsY__C_specific_handlerU_XcptFilterF_exit_cexitexit__initenv_initterm_initterm_e _configthreadlocale__setusermatherr_commodeb_fmode7_encode_pointer__set_app_typeC?terminate@@YAXXZ_unlock__dllonexit=_lock_onexit-_decode_pointer%SleepSetUnhandledExceptionFilterNQueryPerformanceCounterfGetTickCountGetCurrentThreadIdGetCurrentProcessIdOGetSystemTimeAsFileTimeKERNEL32.dllfloatdoubleint8int16int32int64int8int16int32int64helpbuffyfluoridedaggersetdaggerunsetspikeP0@hX0@b`0@fp0@H4@0@H4@0@*** parsing options: start hbf:test_getopt_long: test VLFeat implementation of this function. --help -h this message --buffy -b option with no arguments --fluoride -f option with required argument --daggerset auto option with no arguments --daggerrunset auto option with no argument --spike long option with optional argument option `b' or `buffy' option `f' or `flouride' with arg `%s' option `spike' with arg `%s' automatic longoption (daggerset=%d) illegal option or missing argument illegal option *** parsing options: end non-option: '%s' 2-+] f]!`!H!H-!0B!D!!X!Xo!p!!p!w"!!!0 HXPX PADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGvlfeat-0.9.14/bin/win64/test_heap-def.exe000066400000000000000000000300001167540017400200160ustar00rootroot00000000000000MZ@ !L!This program cannot be run in DOS mode. $kE + + +P +X + * +X +X +X +Rich +PEdN#  <$@pH<3<`P0.text `.rdataj0@@.data@&@.pdataP*@@.rsrc`,@@Hu3HAHHD HD B\BBKDOLH;WL;sbBB (\/(v\/v[B L\/vBBB MKDOLH;rB (\/v B Ht;WHu3HBH (\/v HHuH(HLHt7WHPH(\/vrIH(HH(H(H LBL҉BHHvE3rIH(HH(H(MDMt1BWIRH(\/vBH(̋B+F D BH Z-H%CF D B@WOLHLOTL;s}H\$@L;sRCC;yC+ʅ~RCCMC+˅~9CCMOLOTL;rH\$_CC+ʅ~CCH\$_Ht-HuE3LBIFA;yDBIMuH(LLMt(BIRHDA;y FIH(HH(@SH H L FHDBHHvE3HH [HH [H\$Hl$Ht$WH K|MHHKtH;BH;shDA;y%+ʅ~{BH H+LIщ)L$A+ȅ~UFH "+ILƉLK| Kt H;r'DA+Ѕ~FH *ILljH\$0Hl$8Ht$@H _HtRWH HH\$0Hu3HZHDA;yDH w*LÉ[HHuH\$0H _H\$VH HHHHtJH|$0HzH ;y! H *LljHHQHH|$0H\$8H ^HH\$8H ^H\$WH H LF HHD BH )3HHvE3HHH\$0H _HH\$0H _HA B+ LICEAHAFH\$OLHLOTL;fIKL;scBB;By!B+~mBBBICMBB+~LBBBICMBOLOTL;rH\$BB+~BBBICBH\$LLHt@DMuE3MAIIJBB;yBBBIBMBMuH(L LLMt:LAIQCHA;yIDBIBID\IH(HH(@SH H LLACEHAHAF HHvE3JHH [HH [HHXHhHpWH 3 D$ D$$)pHh@@@OH >@-p@p@H@@@ @p@H@H$ @0H &HHD$HH,BWffffffffnT`H c&HZfI~Ht5T`HSH(L`\/vL`HL$`T`HH;rH &HD\L$`HD$`L`HL$dHw T$`(\/vwL$`T$diT$h(\/v&\$`(\/vEL$`\$dA$L$`(\/vT$`L$hAHL$`HfnT`H Y%HZfI~HH;H E%L[Ht4T`IH(L`\/vL`HL$`T`HIHufnL$lH %fI~ZfH~^-(L$lHHw'T$|(\/}(l$|L$ll\$|$(\/(v\/vE\$ll$|A\/v(T$l$AHL$`H@L$lT$d(\/vHL$`T$lL$dHffD\L$`HD$`L`HL$dHw T$`(\/vwL$`T$diT$h(\/v&\$`(\/vEL$`\$dA$L$`(\/vT$`L$hAHL$`HFfnT`H a#HZfI~HH H L#~HfffDD H L#H[Ht(D HSHDD A;yDD HL$ D HHrH #HDLD$ HˉL$ D HvcD$ T$$Hw+ʅ~MT$ D$$CDD$(A;y+ʅ~3T$ D$$AA+ȅ~DD$ D$(AHL$ HDD H "HHHbH "eHfDD0H "HKHt;D0H{HT0;y(T0H 8!LHӉD0HL$0HkHHrH {"HfL,D$0HˉL$0H L3҉D0HD$0T$4Hw'+ʅT$03H DBD$4iDD$8A;y)+ʅ~YT$03H } DBD$4_A)A+ȅ~/3DD$0H R DBD$84AHL$0HDD0H !H HHH !HHD$HH !HDL\$@Mt6LD$HISCHE A;yG HD$H HL$@KL\$@IHL\$@HrH !xLD$@HL$HILD$@BHD$HBLL$@HT$HIvxDBIwA+~e DHD$HHLDRE;yA+~F DHD$HHAA+…~* DHD$HHAHL$@IHT$HLL$@FH !HHH4L$3I[IkIs A(sI_H8H =D!!D !H!L H H D HD$   y H8Ht$WH eH%0Hx33H= tH;u ؿ ;u ~7z u'=p HH ut=5 C ;uHH >" u 3H H=- tH $ tE3AP3 H u HHLH =us =u  ~-v=Su  ̃=au u OHt$0H _H(MZf9t3OHc HHȁ9PEuA= t= u3v93ytv 9 H H HHHp w=Su H @ =9u & 3H(H(H(H(H8csmu+xu%@ = t=!t="t=@u3H(H(H { 3H(%. @SH HH  HD$8Hu H ~H  HD$8H  HD$@H HLD$@HT$8jHHL$8 HHL$@ Ho3HH [H(GHH(H\$WH H H= HHtHH;rH\$0H _H\$WH H H= HHtHH;rH\$0H _% % HMZf9t3HcH %f %I64u: %f Pushing heap of structures s[%I64u].x = %d Popping heap of structures s[%I64u].x = %d Pushing heap of structures with custom swap s[%I64u].x = %d Popping heap of structures with custom swap s[%I64u].x = %d Pushing heap of structures with custom container s[%I64u].x = %d Popping heap of structures with custom container s[%I64u].x = %d u2-+] f110w11W1Wt1tv11O1`=1P] 2]2(282D2 X20h2t2T|2`2l!2l!!2!T#2T#9$2<$N$2P$$2$$2$d%2d%{%2|%%3%%3&&3&'03''2''2'(20 HX`X PADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGvlfeat-0.9.14/bin/win64/test_host.exe000066400000000000000000000150001167540017400173250ustar00rootroot00000000000000MZ@ !L!This program cannot be run in DOS mode. $6rHrHrHU;HpHloHpHlxHwHrHmHlhH}HlzHsHRichrHPEdN#  4@`_"PP@ .text `.rdata  @@.data0@.pdata@@@.rsrcP@@HHH`P@SH HHHR0HHPP3H [H8H =D! D  H LHH DHD$ (y H8Ht$WH eH%0Hx33H=tH;u ؿ;u ~7zu'=pHH mt=5C;uHH 6"u 3HH=-tH $tE3AP3 H HHLH J=uK=u 4~-v=Su M̃=au OHt$0H _H(MZf9t3OHc HHȁ9PEuA= t= u3v93ytv 9 H H HHH w=Su H X =9u > 3H(H(H(H(H8csmu+xu%@ = t=!t="t=@u3H(H(H  3H(%6 @SH HH  HD$8Hu H ~H  HD$8H  HD$@Hx HLD$@HT$8bHHL$8X HHL$@F Ho+HH [H(GHH(H\$WH H H= HHtHH;rH\$0H _H\$WH H H= HHtHH;rH\$0H _% % HMZf9t3HcH PADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGvlfeat-0.9.14/bin/win64/test_imopv.exe000066400000000000000000000200001167540017400174760ustar00rootroot00000000000000MZ@ !L!This program cannot be run in DOS mode. $}}}򓻁}/~}/i}}}/y}/k}Rich}PEdN#  @`"PP@ X.text   `.rdata @@.data0@.pdata@@@.rsrcP@@HAH\$WH H=! HHu H؋HHH\$0H _HH\$WH H=HHu {H؋HuoH\$0H _H\$WH 0H=A)HHu H؋HHCH\$0H _HH`8HH`P@SVWHH‹HP83E3 H$E3ۋE3əL׃ƒ+ƒAÐ3Aƒ+ƒH$IAA3Ifn[YADuI|THHL$HT$`t1H=,H HDW0HĀ_^[Ë|$`t$dHH$L$H DƋLl$xLt$pP0HcHcHHvHHP8HeHLR8HU<LR8HH==HEHEHEHEHE HE(HE0E8H ǁ AIfDL$D$PD$HD$@D$8Hl$0DϋIΉ|$(t$  HuH z H f(fH~S0L Aǃ? fffffffL$D$PD$HD$@D$8Hl$0DϋI͉|$(t$  IuH  H f(fH~S0H$H DD H DDI H DDI H H$PPH HPPH IPPH IPPLt$pLl$xL$H$3HĀ_^[H8H =DuD jHWL<H=H &D;HD$  &y H8Ht$WH eH%0Hx33H=tH;u  ؿ;u ~7u'=H H qt=;uHl H U :vu 3HqH=tH xtE3AP3`H 9 HHLH =u =u  -=u  ̃=u  Ht$0H _H(MZf9t3OHc GHHȁ9PEuA= t= u3v93ytv 9 z Hh H Y H2H3H4 w=u H  =u 3H(H(H(H(H8csmu+xu%@ = t=!t="t=@u3H(H(H 3H(%@SH HH XHD$8Hu H~H *|HD$8H jHD$@H$HLD$@HT$8fHHL$8HHL$@H/HH [H(GHH(H\$WH HH=HHtHH;rH\$0H _H\$WH HH=xHHtHH;rH\$0H _%%HMZf9t3HcH PADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGvlfeat-0.9.14/bin/win64/test_mathop.exe000066400000000000000000000250001167540017400176410ustar00rootroot00000000000000MZ@ !L!This program cannot be run in DOS mode. $l}}}y}/}/}}}/}/}Rich}PEdN#  @pC 3P`P00.text `.rdata0@@.data@@.pdataP$@@.rsrc`&@@H!D$(k!L$Y7_Y%T!+(ȉD$\$(YY\Y(YY\YD$f(!HL$H0_HY% H+HD$f(\$f(YY\Yf(YY\Y̋3DDAA;rA+y3DHfffffD;rfD+fyD2DD:rD*yHHXHhVWATH)p)xD)@3L .L.H.H .DD)HD)PD)XHv.HD$ HD)d$`D)l$PD)t$@D)|$0P0EWDE(E(D DD=;E(=D%ffD$Y7_A(⋌$A(A(Y%+$$(YY\YA((YY\Y- ^\ZfATZ^YD/wD(EXEE/AZYDZ>HEHl-H }-AZfI~P0D=xEWEAZfA/vHH/-H h-P0AyA(A(fffffffXD/ZYZwHHIfnH,H 7-f(fI~^fI~P0fDEXEE/AZYDZwHfnH,H ,f(fI~^fI~P0DaD5 E(E(H0_E(D 1D=`EHAZE$f(fA(H$HY%H+H$$f(YY\YfA(f(YY\YZAZ ^Z\ZZfAT^YeZD/wD(EXEAZEAZYfD/DZHEH+H +AZfI~P0D= EAZfA/vHRH+H +P0A%A(A(fffXZZYfD/ZwHfnH'+H +f(fI~^fI~P0EXEAZEAZYfD/DZwHfnHH+H Q+f(fI~^fI~P0HYH f*P0HHL-+L .+L3+H8+H =+L\$ P0DۋfEËùfDTD;rD+yAAwHH*H +f(P0HH *=5f(f(fH~D^fI~P0sffffffffH*PQH,w>H?H*H *f(P0H#H *f(^fH~Df(fI~P0;tHH *P0ADEӹ@fffffD;rfD+fƒyffAuHHQ*H ^*f(P0Hl=f(f(fH~H =*D^fI~P0(DfnH,ffuHH*H *f(P0HH )f(^fH~Df(fI~P0f;tHH )P0AE2@2fE2ҹDD:rD*ƒy@AuOHPH)H )f(P0H4@=f(f(fH~H )D^fI~P02@2fffnH,@uHHb)H g)f(P0HH ^)f(^fH~Df(fI~P0@:tHvH G)P0AD(t$@D(|$0L$I[ Ik(A(sA({E(CE(KE(SE([E(cE(kIA\_^%%%H8H =DI)D >)H+)L)H)H (D)HD$ (y H8Ht$WH eH%0Hx33H=(tH;u eؿ(;u ~7(u'=(HiH Jut=](k(;uHH >J(u 3HE(H=U(tH L(tE3AP34(H H'HL'H' ''='u='u '-'={'u ũ='u mw'Ht$0H _H(MZf9t3OHc HHȁ9PEuA= t= u3v93ytv 9&6H$H H'H'&H&w={&u H =a&u 3H(H(H(H(H8csmu+xu%@ = t=!t="t=@u3H(H(H 3H(%&@SH HH ,&fHD$8Hu HJ~H %8HD$8H %&HD$@HHLD$@HT$8jHHL$8H%HL$@H%3HH [H(GHH(H\$WH HH=HHtHH;rH\$0H _H\$WH HH=HHtHH;rH\$0H _%%HMZf9t3HcH PADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGvlfeat-0.9.14/bin/win64/test_mathop_abs.exe000066400000000000000000000170001167540017400204670ustar00rootroot00000000000000MZ@ !L!This program cannot be run in DOS mode. $}}}񓻅}/z}/m}}}/}}/o}Rich}PEdN#  0@`Ex"PP@ (.text   `.rdata  @@.data0@.pdata@@@.rsrcP@@H(DfI*YZH(HHH`8HH`PZfT ZH\$Hl$Ht$WATAUAVAWH@)t$0)|$ ,H=LR8H+HR8L5}=L+HE3LHDIHHDfI*Y\ZADDsuHI5HH+HHHHHL|iHNfnHHH;ZfTZGfAnL=ZfTZGfnKZfTZGfnSZfTZG|H;s!fnHHZfTZGH;rHH Df(fH~S03HI|HHrHII|gHFfnHHH;ZfTZGfAnL=ZfTZGfnKZfTZGfnSZfTZG|H;s!fnHHZfTZGH;rHH tf(fH~S0H IPPH HPPH\$pHl$x(t$0(|$ H$3H@A_A^A]A\_H8H =DmD bHOL4H5H D3HD$  y H8Ht$WH eH%0Hx33H= tH;u ! ؿ;u ~7u'=H H qt=;uH H :nu 3HiH=ytH ptE3AP3XH HHLH =ug =u P -=u ) ̃=u ! Ht$0H _H(MZf9t3OHc HHȁ9PEuA= t= u3v93ytv 9 H H H*H+ H w=u H t =u Z 3H(H(H(H(H8csmu+xu%@ = t=!t="t=@u3H(H(H  3H(%: @SH HH P HD$8Hu H ~H " HD$8H  HD$@H HLD$@HT$8fHHL$8t HHL$@b H/HH [H(GHH(H\$WH H H= HHtHH;rH\$0H _H\$WH H H= HHtHH;rH\$0H _%Z%\HMZf9t3HcH PADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGvlfeat-0.9.14/bin/win64/test_nan.exe000066400000000000000000000150001167540017400171240ustar00rootroot00000000000000MZ@ !L!This program cannot be run in DOS mode. $ܔ{~iykRichPEdN#  @`"PP@ .text `.rdatat  @@.data0@.pdata@@@.rsrcP@@HH( ,HH F fH~P0H H @ fH~P0HH B  fH~P0HH <  fH~P0HeH 6  fH~P0HGH 0  fH~P0H)H * P0HH < P03H(H8H =D D ~ Hk LP HQ H : DO HD$ : y H8Ht$WH eH%0Hx33H=( tH;u ؿ;u ~7u'=HaH But=;uHH >u 3HH=tH tE3AP3tH HHL H   =u =u  -=u  ̃=u  Ht$0H _H(MZf9 t3OHc ;HHȁ9PEuA= t= u3v93ytv 9 . H H } HFHG%H w=u H  =u  3H(H(H(H(H8csmu+xu%@ = t=!t="t=@u3H(H(H  3H(% @SH HH l^ HD$8Hu HB ~H >0 HD$8H $ HD$@H HLD$@HT$8jHHL$8 HHL$@ H3HH [H(GHH(H\$WH HK H=D HHtHH;rH\$0H _H\$WH H# H= HHtHH;rH\$0H _%N %@ HMZf9t3HcH - VL_INFINITY_D: %d u2-+] f!a!d!!!!!$<!D! ! D!D|!Q!d"0L!Le!p!0 HXPX PADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGPADDINGXXPADDINGvlfeat-0.9.14/bin/win64/test_qsort-def.exe000066400000000000000000000160001167540017400202550ustar00rootroot00000000000000MZ@ !L!This program cannot be run in DOS mode. $kE + + +P +X + * +X +X +X +Rich +PEdN#  @`$$"<P@ .textq   `.rdataR @@.data0@.pdata@@@.rsrcP@@B\BBH\$Ht$WH0)t$ WILHًM 2IIIBBHI+HHJDI+HHHI H(\/r H@H(\/r H@(\/r HH(\/r H@HHdH;s, (\/r HHH;rԋI;vLGIHH;s LWH\$@Ht$H(t$ H0_LB3eHHXWH@H <3H@&H @H @HfnL H ZfH~HHrH o3HL$ DBffffffffnL H _ZfH~5HHrH IH\$P3H@_H8H =DuD jHWL<H=H &D;HD$  &y H8Ht$WH eH%0Hx33H=tH;u  ؿ;u ~7u'=Hu H V qt=;uH$ H :vu 3HqH=tH xtE3AP3`H  HHLH =u =u 0 -=u  ̃=u  Ht$0H _H(MZf9(t3OHc WHHȁ9PEuA= t= u3v93ytv 9 B H H ! 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(%5d,%5d)=%5d dE: %10.3g I: %6.4g H: %6.4g updt: %5d floatdoubleint8int16int32int64int8int16int32int64floatdoubleint8int16int32int64int8int16int32int64l1l2chi2kl1kl2kchi2floatdoubleint8int16int32int64int8int16int32int640.9.14yesnoVLFeat version %s Static config: %s %d CPU(s): %s Debug: %s floatdoubleint8int16int32int64int8int16int32int64=%s: option requires an argument -- %s %s: illegal option -- %s %s: illegal option -- %c %s: option requires an argument -- %c floatdoubleint8int16int32int64int8int16int32int64hikmeans: branch at depth %d: %6.1f %% completed floatdoubleint8int16int32int64int8int16int32int64l1l2chi2kl1kl2kchi2floatdoubleint8int16int32int64int8int16int32int64 SSE42 SSE41 SSE3 SSE2 SSE MMX%s%s%s%s%s%s%sX64, little_endianWindows_threads, SSE2Microsoft Visual C++ %d LP64%s, %s, %sfloatdoubleint8int16int32int64int8int16int32int64ikm: training with %d data ikm: %d clusters ikm: Elkan algorithm: total iterations: %d ikm: Elkan algorithm: distance calculations: %d (speedup: %.2f) floatdoubleint8int16int32int64int8int16int32int64l1l2chi2kl1kl2kchi2floatdoubleint8int16int32int64int8int16int32int64floatdoubleint8int16int32int64int8int16int32int64l1l2chi2kl1kl2kchi2floatdoubleint8int16int32int64int8int16int32int64l1l2chi2kl1kl2kchi2kmeans: Lloyd iter %d: energy = %g kmeans: Lloyd terminating because maximum number of iterations reached kmeans: Lloyd terminating because the algorithm fully converged kmeans: Lloyd iter %d: restarted %d centers kmeans: Elkan iter 0: energy = %g, dist. calc. = %d kmeans: Elkan iter %d: energy <= %g, dist. calc. = %d kmeans: Elkan iter %d: restarted %d centers kmeans: Elkan iter %d: total dist. calc. per type: UB: %.1f%% (%d), LB: %.1f%% (%d), intra_center: %.1f%% (%d), new_center: %.1f%% (%d) kmeans: Elkan terminating because maximum number of iterations reached kmeans: Elkan terminating because the algorithm fully converged kmeans: Elkan: total dist. calc.: %d (%.2f %% of Lloyd) kmeans: Elkan: there have been %d restarts kmeans: Elkan: total dist. calc. per type: init: %.1f%% (%d), UB: %.1f%% (%d), LB: %.1f%% (%d), intra_center: %.1f%% (%d), new_center: %.1f%% (%d), finalize: %.1f%% (%d) kmeans: Lloyd iter %d: energy = %g kmeans: Lloyd terminating because maximum number of iterations reached kmeans: Lloyd terminating because the algorithm fully converged kmeans: Lloyd iter %d: restarted %d centers kmeans: Elkan iter 0: energy = %g, dist. calc. = %d kmeans: Elkan iter %d: energy <= %g, dist. calc. = %d kmeans: Elkan iter %d: restarted %d centers kmeans: Elkan iter %d: total dist. calc. per type: UB: %.1f%% (%d), LB: %.1f%% (%d), intra_center: %.1f%% (%d), new_center: %.1f%% (%d) kmeans: Elkan terminating because maximum number of iterations reached kmeans: Elkan terminating because the algorithm fully converged kmeans: Elkan: total dist. calc.: %d (%.2f %% of Lloyd) kmeans: Elkan: there have been %d restarts kmeans: Elkan: total dist. calc. per type: init: %.1f%% (%d), UB: %.1f%% (%d), LB: %.1f%% (%d), intra_center: %.1f%% (%d), new_center: %.1f%% (%d), finalize: %.1f%% (%d) kmeans: repetition %d of %d kmeans: K-means initialized in %.2f s kmeans: K-means termineted in %.2f s with energy %g floatdoubleint8int16int32int64int8int16int32int64l1l2chi2kl1kl2kchi2floatdoubleint8int16int32int64int8int16int32int64l1l2chi2kl1kl2kchi2floatdoubleint8int16int32int64int8int16int32int64l1l2chi2kl1kl2kchi2floatdoubleint8int16int32int64int8int16int32int64floatdoubleint8int16int32int64int8int16int32int64l1l2chi2kl1kl2kchi2floatdoubleint8int16int32int64int8int16int32int64l1l2chi2kl1kl2kchi2%d%d%d %udInvalid PGM dataP5 %d %d %d Error writing PGM datarbError opening PGM file `%s' for readingvl_pgm_read(): PGM with BPP > 1 not supportedwbError opening PGM file for 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vl.dll/ 1324743193 0 474 ` dN.debug$S<@B.idata$2@0.idata$6@  vl.dll' RMicrosoft (R) LINK vl.dll@comp.idR.idata$2@h.idata$6.idata$4@h.idata$5@h4H__IMPORT_DESCRIPTOR_vl__NULL_IMPORT_DESCRIPTORvl_NULL_THUNK_DATAvl.dll/ 1324743193 0 245 ` dN.debug$S<d@B.idata$3@0 vl.dll' RMicrosoft (R) LINK@comp.idR__NULL_IMPORT_DESCRIPTOR vl.dll/ 1324743193 0 276 ` dN.debug$S<@B.idata$5@@.idata$4@@ vl.dll' RMicrosoft (R) LINK@comp.idRvl_NULL_THUNK_DATAvl.dll/ 1324743193 0 47 ` dN_vl_distance_chi2_dvl.dll vl.dll/ 1324743193 0 47 ` dN_vl_distance_chi2_fvl.dll vl.dll/ 1324743193 0 52 ` dN _vl_distance_chi2_sse2_dvl.dllvl.dll/ 1324743193 0 52 ` dN _vl_distance_chi2_sse2_fvl.dllvl.dll/ 1324743193 0 52 ` dN _vl_distance_hellinger_dvl.dllvl.dll/ 1324743193 0 52 ` dN _vl_distance_hellinger_fvl.dllvl.dll/ 1324743193 0 45 ` dN_vl_distance_js_dvl.dll vl.dll/ 1324743193 0 45 ` dN_vl_distance_js_fvl.dll vl.dll/ 1324743193 0 45 ` dN_vl_distance_l1_dvl.dll vl.dll/ 1324743193 0 45 ` dN _vl_distance_l1_fvl.dll vl.dll/ 1324743193 0 50 ` dN _vl_distance_l1_sse2_dvl.dllvl.dll/ 1324743193 0 50 ` dN _vl_distance_l1_sse2_fvl.dllvl.dll/ 1324743193 0 45 ` dN _vl_distance_l2_dvl.dll vl.dll/ 1324743193 0 45 ` dN _vl_distance_l2_fvl.dll vl.dll/ 1324743193 0 50 ` dN_vl_distance_l2_sse2_dvl.dllvl.dll/ 1324743193 0 50 ` dN_vl_distance_l2_sse2_fvl.dllvl.dll/ 1324743193 0 52 ` dN _vl_dsift_update_buffersvl.dllvl.dll/ 1324743193 0 49 ` dN_vl_imconvcol_vd_sse2vl.dll vl.dll/ 1324743193 0 49 ` dN_vl_imconvcol_vf_sse2vl.dll vl.dll/ 1324743193 0 45 ` dN_vl_kernel_chi2_dvl.dll vl.dll/ 1324743193 0 45 ` dN_vl_kernel_chi2_fvl.dll vl.dll/ 1324743193 0 50 ` dN_vl_kernel_chi2_sse2_dvl.dllvl.dll/ 1324743193 0 50 ` dN_vl_kernel_chi2_sse2_fvl.dllvl.dll/ 1324743193 0 50 ` dN_vl_kernel_hellinger_dvl.dllvl.dll/ 1324743193 0 50 ` dN_vl_kernel_hellinger_fvl.dllvl.dll/ 1324743193 0 43 ` dN_vl_kernel_js_dvl.dll vl.dll/ 1324743193 0 43 ` dN_vl_kernel_js_fvl.dll vl.dll/ 1324743193 0 43 ` dN_vl_kernel_l1_dvl.dll vl.dll/ 1324743193 0 43 ` dN_vl_kernel_l1_fvl.dll vl.dll/ 1324743193 0 48 ` dN_vl_kernel_l1_sse2_dvl.dllvl.dll/ 1324743193 0 48 ` dN_vl_kernel_l1_sse2_fvl.dllvl.dll/ 1324743193 0 43 ` dN_vl_kernel_l2_dvl.dll vl.dll/ 1324743193 0 43 ` dN _vl_kernel_l2_fvl.dll vl.dll/ 1324743193 0 48 ` dN!_vl_kernel_l2_sse2_dvl.dllvl.dll/ 1324743193 0 48 ` dN"_vl_kernel_l2_sse2_fvl.dllvl.dll/ 1324743193 0 37 ` dN#_vl_statevl.dll vl.dll/ 1324743193 0 39 ` dN$getopt_longvl.dll vl.dll/ 1324743193 0 34 ` dN%optargvl.dllvl.dll/ 1324743193 0 34 ` dN&opterrvl.dllvl.dll/ 1324743193 0 34 ` dN'optindvl.dllvl.dll/ 1324743193 0 34 ` dN(optoptvl.dllvl.dll/ 1324743193 0 36 ` dN)optresetvl.dllvl.dll/ 1324743193 0 41 ` dN*vl_aib_deletevl.dll vl.dll/ 1324743193 0 38 ` dN+vl_aib_newvl.dllvl.dll/ 1324743193 0 42 ` dN,vl_aib_processvl.dllvl.dll/ 1324743193 0 44 ` dN-vl_array_deallocvl.dllvl.dll/ 1324743193 0 43 ` dN.vl_array_deletevl.dll vl.dll/ 1324743193 0 53 ` dN!/vl_array_get_num_elementsvl.dll vl.dll/ 1324743193 0 41 ` dN0vl_array_initvl.dll 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1324743193 0 63 ` dN+Bvl_get_vector_comparison_function_fvl.dll vl.dll/ 1324743193 0 49 ` dNCvl_get_version_stringvl.dll vl.dll/ 1324743193 0 42 ` dNDvl_hikm_deletevl.dllvl.dll/ 1324743193 0 40 ` dNEvl_hikm_initvl.dllvl.dll/ 1324743193 0 39 ` dNFvl_hikm_newvl.dll vl.dll/ 1324743193 0 40 ` dNGvl_hikm_pushvl.dllvl.dll/ 1324743193 0 41 ` dNHvl_hikm_trainvl.dll vl.dll/ 1324743193 0 58 ` dN&Ivl_homogeneouskernelmap_deletevl.dllvl.dll/ 1324743193 0 62 ` dN*Jvl_homogeneouskernelmap_evaluate_dvl.dllvl.dll/ 1324743193 0 62 ` dN*Kvl_homogeneouskernelmap_evaluate_fvl.dllvl.dll/ 1324743193 0 55 ` dN#Lvl_homogeneouskernelmap_newvl.dll vl.dll/ 1324743193 0 41 ` dNMvl_ikm_deletevl.dll vl.dll/ 1324743193 0 39 ` dNNvl_ikm_initvl.dll vl.dll/ 1324743193 0 44 ` dNOvl_ikm_init_randvl.dllvl.dll/ 1324743193 0 49 ` dNPvl_ikm_init_rand_datavl.dll vl.dll/ 1324743193 0 38 ` dNQvl_ikm_newvl.dllvl.dll/ 1324743193 0 39 ` dNRvl_ikm_pushvl.dll vl.dll/ 1324743193 0 43 ` dNSvl_ikm_push_onevl.dll vl.dll/ 1324743193 0 40 ` dNTvl_ikm_trainvl.dllvl.dll/ 1324743193 0 57 ` dN%Uvl_image_distance_transform_dvl.dll vl.dll/ 1324743193 0 57 ` dN%Vvl_image_distance_transform_fvl.dll vl.dll/ 1324743193 0 43 ` dNWvl_imconvcol_vdvl.dll vl.dll/ 1324743193 0 43 ` dNXvl_imconvcol_vfvl.dll vl.dll/ 1324743193 0 45 ` dNYvl_imconvcoltri_dvl.dll vl.dll/ 1324743193 0 45 ` dNZvl_imconvcoltri_fvl.dll vl.dll/ 1324743193 0 43 ` dN[vl_imintegral_dvl.dll vl.dll/ 1324743193 0 43 ` dN\vl_imintegral_fvl.dll vl.dll/ 1324743193 0 45 ` dN]vl_imintegral_i32vl.dll vl.dll/ 1324743193 0 46 ` dN^vl_imintegral_ui32vl.dllvl.dll/ 1324743193 0 41 ` dN_vl_irodriguesvl.dll vl.dll/ 1324743193 0 45 ` dN`vl_kdforest_buildvl.dll vl.dll/ 1324743193 0 46 ` dNavl_kdforest_deletevl.dllvl.dll/ 1324743193 0 43 ` dNbvl_kdforest_newvl.dll vl.dll/ 1324743193 0 45 ` dNcvl_kdforest_queryvl.dll vl.dll/ 1324743193 0 57 ` dN%dvl_kdforest_query_recursivelyvl.dll vl.dll/ 1324743193 0 45 ` dNevl_kmeans_clustervl.dll vl.dll/ 1324743193 0 44 ` dNfvl_kmeans_deletevl.dllvl.dll/ 1324743193 0 41 ` dNgvl_kmeans_newvl.dll vl.dll/ 1324743193 0 46 ` dNhvl_kmeans_new_copyvl.dllvl.dll/ 1324743193 0 46 ` dNivl_kmeans_quantizevl.dllvl.dll/ 1324743193 0 52 ` dN jvl_kmeans_refine_centersvl.dllvl.dll/ 1324743193 0 43 ` dNkvl_kmeans_resetvl.dll vl.dll/ 1324743193 0 60 ` dN(lvl_kmeans_seed_centers_plus_plusvl.dllvl.dll/ 1324743193 0 65 ` dN-mvl_kmeans_seed_centers_with_rand_datavl.dll vl.dll/ 1324743193 0 49 ` dNnvl_kmeans_set_centersvl.dll vl.dll/ 1324743193 0 41 ` dNovl_lbp_deletevl.dll vl.dll/ 1324743193 0 38 ` dNpvl_lbp_newvl.dllvl.dll/ 1324743193 0 42 ` dNqvl_lbp_processvl.dllvl.dll/ 1324743193 0 41 ` dNrvl_lock_statevl.dll vl.dll/ 1324743193 0 42 ` dNsvl_mser_deletevl.dllvl.dll/ 1324743193 0 43 ` dNtvl_mser_ell_fitvl.dll vl.dll/ 1324743193 0 39 ` dNuvl_mser_newvl.dll vl.dll/ 1324743193 0 43 ` dNvvl_mser_processvl.dll vl.dll/ 1324743193 0 57 ` dN%wvl_pegasos_train_binary_svm_dvl.dll vl.dll/ 1324743193 0 57 ` dN%xvl_pegasos_train_binary_svm_fvl.dll vl.dll/ 1324743193 0 47 ` dNyvl_pgm_extract_datavl.dll vl.dll/ 1324743193 0 47 ` dNzvl_pgm_extract_headvl.dll vl.dll/ 1324743193 0 42 ` dN{vl_pgm_get_bppvl.dllvl.dll/ 1324743193 0 46 ` dN|vl_pgm_get_npixelsvl.dllvl.dll/ 1324743193 0 41 ` dN}vl_pgm_insertvl.dll vl.dll/ 1324743193 0 43 ` dN~vl_pgm_read_newvl.dll vl.dll/ 1324743193 0 45 ` dNvl_pgm_read_new_fvl.dll vl.dll/ 1324743193 0 40 ` dNvl_pgm_writevl.dllvl.dll/ 1324743193 0 42 ` dNvl_pgm_write_fvl.dllvl.dll/ 1324743193 0 48 ` dNvl_quickshift_deletevl.dllvl.dll/ 1324743193 0 45 ` dNvl_quickshift_newvl.dll vl.dll/ 1324743193 0 49 ` dNvl_quickshift_processvl.dll vl.dll/ 1324743193 0 40 ` dNvl_rand_initvl.dllvl.dll/ 1324743193 0 40 ` dNvl_rand_seedvl.dllvl.dll/ 1324743193 0 49 ` dNvl_rand_seed_by_arrayvl.dll vl.dll/ 1324743193 0 42 ` dNvl_rand_uint32vl.dllvl.dll/ 1324743193 0 40 ` dNvl_rodriguesvl.dllvl.dll/ 1324743193 0 45 ` dNvl_set_alloc_funcvl.dll vl.dll/ 1324743193 0 45 ` dNvl_set_last_errorvl.dll vl.dll/ 1324743193 0 46 ` dNvl_set_printf_funcvl.dllvl.dll/ 1324743193 0 60 ` dN(vl_sift_calc_keypoint_descriptorvl.dllvl.dll/ 1324743193 0 62 ` dN*vl_sift_calc_keypoint_orientationsvl.dllvl.dll/ 1324743193 0 55 ` dN#vl_sift_calc_raw_descriptorvl.dll vl.dll/ 1324743193 0 42 ` dNvl_sift_deletevl.dllvl.dll/ 1324743193 0 42 ` dNvl_sift_detectvl.dllvl.dll/ 1324743193 0 49 ` dNvl_sift_keypoint_initvl.dll vl.dll/ 1324743193 0 39 ` dNvl_sift_newvl.dll vl.dll/ 1324743193 0 56 ` dN$vl_sift_process_first_octavevl.dllvl.dll/ 1324743193 0 55 ` dN#vl_sift_process_next_octavevl.dll vl.dll/ 1324743193 0 43 ` dNvl_slic_segmentvl.dll vl.dll/ 1324743193 0 66 ` dN.vl_static_configuration_to_string_copyvl.dllvl.dll/ 1324743193 0 46 ` dNvl_string_basenamevl.dllvl.dll/ 1324743193 0 47 ` dNvl_string_casei_cmpvl.dll vl.dll/ 1324743193 0 42 ` dNvl_string_copyvl.dllvl.dll/ 1324743193 0 46 ` dNvl_string_copy_subvl.dllvl.dll/ 1324743193 0 51 ` dNvl_string_find_char_revvl.dll vl.dll/ 1324743193 0 44 ` dNvl_string_lengthvl.dllvl.dll/ 1324743193 0 52 ` dN vl_string_parse_protocolvl.dllvl.dll/ 1324743193 0 51 ` dNvl_string_protocol_namevl.dll vl.dll/ 1324743193 0 54 ` dN"vl_string_replace_wildcardvl.dllvl.dll/ 1324743193 0 59 ` dN'vl_thread_specific_state_deletevl.dll vl.dll/ 1324743193 0 56 ` dN$vl_thread_specific_state_newvl.dllvl.dll/ 1324743193 0 34 ` dNvl_ticvl.dllvl.dll/ 1324743193 0 34 ` dNvl_tocvl.dllvl.dll/ 1324743193 0 43 ` dNvl_unlock_statevl.dll vlfeat-0.9.14/data/000077500000000000000000000000001167540017400140045ustar00rootroot00000000000000vlfeat-0.9.14/data/a.jpg000066400000000000000000004630311167540017400147350ustar00rootroot00000000000000JFIFHH XICC_PROFILE HLinomntrRGB XYZ  1acspMSFTIEC sRGB-HP cprtP3desclwtptbkptrXYZgXYZ,bXYZ@dmndTpdmddvuedLview$lumimeas $tech0 rTRC< gTRC< bTRC< textCopyright (c) 1998 Hewlett-Packard CompanydescsRGB IEC61966-2.1sRGB IEC61966-2.1XYZ QXYZ XYZ o8XYZ bXYZ $descIEC http://www.iec.chIEC http://www.iec.chdesc.IEC 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Home - About

VLFeat is a cross-platform open source collection of vision algorithms with a special focus on visual features (for instance SIFT and MSER) and clustering (k-means, hierarchical k-means, agglomerative information bottleneck). It bundles a MATLAB toolbox, a clean and portable C library and a number of command line utilities. Thus it is possible to use the same algorithm both from MATLAB, the command line, and your own programs.

About the authors

VLFeat is an effort initiated by Andrea Vedaldi and Brian Fulkerson in 2007, based on previously published software from the same authors.

Andrea Vedaldi picture Andrea Vedaldi received the Bachelor's degree in Information Engineering from the University of Padova, Italy, in 2003, and the Master's and Ph.D. degrees in Computer Science from the University of California - Los Angeles, in 2005 and 2008. He is the recipient of the UCLA outstanding Master's and Ph.D. awards. In 2008 he joined the Visual Geometry Group at Oxford University as postdoctoral researcher.

Brian Fulkerson picture Brian Fulkerson received his B.S. in Computer Engineering from the University of California - San Diego in 2004, and his M.S. in Computer Science from the University of California - Los Angeles in 2006. He is currently pursuing a Ph.D. in Computer Science at the UCLA Vision Lab.

 

Acknowledgments

Part of this work was supported by the UCLA Vision Lab and the Oxford VGG Lab. The authors would like to thank the many colleagues that have contributed to VLFeat by testing and providing helpful suggestions and comments.

 
vlfeat-0.9.14/doc/api/000077500000000000000000000000001167540017400144115ustar00rootroot00000000000000vlfeat-0.9.14/doc/api/aib.html000066400000000000000000000263231167540017400160400ustar00rootroot00000000000000 VLFeat - Documentation - C API
Documentation - C API
Agglomerative Information Bottleneck (AIB)
Author:
Brian Fulkerson
Andrea Vedaldi

aib.h implemens the Agglomerative Information Bottleneck (AIB) algorithm as first described in [11] .

AIB takes a discrete valued feature $x$ and a label $c$ and gradually compresses $x$ by iteratively merging values which minimize the loss in mutual information $I(x,c)$.

While the algorithm is equivalent to the one described in [11] , it has some speedups that enable handling much larger datasets. Let N be the number of feature values and C the number of labels. The algorithm of [11] is $O(N^2)$ in space and $O(C N^3)$ in time. This algorithm is $O(N)$ space and $O(C N^2)$ time in common cases ( $O(C N^3)$ in the worst case).

Overview

Given a discrete feature $x \in \mathcal{X} = \{x_1,\dots,x_N\}$ and a category label $c = 1,\dots,C$ with joint probability $p(x,c)$, AIB computes a compressed feature $[x]_{ij}$ by merging two values $x_i$ and $x_j$. Among all the pairs $ij$, AIB chooses the one that yields the smallest loss in the mutual information

\[ D_{ij} = I(x,c) - I([x]_{ij},c) = \sum_c p(x_i) \log \frac{p(x_i,c)}{p(x_i)p(c)} + \sum_c p(x_i) \log \frac{p(x_i,c)}{p(x_i)p(c)} - \sum_c (p(x_i)+p(x_j)) \log \frac {p(x_i,c)+p(x_i,c)}{(p(x_i)+p(x_j))p(c)} \]

AIB iterates this procedure until the desired level of compression is achieved.

Algorithm details

Computing $D_{ij}$ requires $O(C)$ operations. For example, in standard AIB we need to calculate

\[ D_{ij} = I(x,c) - I([x]_{ij},c) = \sum_c p(x_i) \log \frac{p(x_i,c)}{p(x_i)p(c)} + \sum_c p(x_i) \log \frac{p(x_i,c)}{p(x_i)p(c)} - \sum_c (p(x_i)+p(x_j)) \log \frac {p(x_i,c)+p(x_i,c)}{(p(x_i)+p(x_j))p(c)} \]

Thus in a basic implementation of AIB, finding the optimal pair $ij$ of feature values requires $O(CN^2)$ operations in total. In order to join all the $N$ values, we repeat this procedure $O(N)$ times, yielding $O(N^3 C)$ time and $O(1)$ space complexity (this does not account for the space need to store the input).

The complexity can be improved by reusing computations. For instance, we can store the matrix $D = [ D_{ij} ]$ (which requires $O(N^2)$ space). Then, after joining $ij$, all of the matrix D except the rows and columns (the matrix is symmetric) of indexes i and j is unchanged. These two rows and columns are deleted and a new row and column, whose computation requires $O(NC)$ operations, are added for the merged value $x_{ij}$. Finding the minimal element of the matrix still requires $O(N^2)$ operations, so the complexity of this algorithm is $O(N^2C + N^3)$ time and $O(N^2)$ space.

We can obtain a much better expected complexity as follows. First, instead of storing the whole matrix D, we store the smallest element (index and value) of each row as $(q_i, D_i)$ (notice that this is also the best element of each column since D is symmetric). This requires $O(N)$ space and finding the minimal element of the matrix requires $O(N)$ operations. After joining $ij$, we have to efficiently update this representation. This is done as follows:

  • The entries $(q_i,D_i)$ and $(q_j,D_j)$ are deleted.
  • A new entry $(q_{ij},D_{ij})$ for the joint value $x_{ij}$ is added. This requires $O(CN)$ operations.
  • We test which other entries $(q_{k},D_{k})$ need to be updated. Recall that $(q_{k},D_{k})$ means that, before the merge, the value closest to $x_k$ was $x_{q_k}$ at a distance $D_k$. Then
    • If $q_k \not = i$, $q_k \not = j$ and $D_{k,ij} \geq D_k$, then $q_k$ is still the closest element and we do not do anything.
    • If $q_k \not = i$, $q_k \not = j$ and $D_{k,ij} < D_k$, then the closest element is $ij$ and we update the entry in constant time.
    • If $q_k = i$ or $q_k = j$, then we need to re-compute the closest element in $O(CN)$ operations.

This algorithm requires only $O(N)$ space and $O(\gamma(N) C N^2)$ time, where $\gamma(N)$ is the expected number of times we fall in the last case. In common cases one has $\gamma(N) \approx \mathrm{const.}$, so the time saving is significant.

 
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Functions
aib.c File Reference

AIB - Definition. More...

#include "aib.h"
#include <stdio.h>
#include <stdlib.h>
#include <float.h>
#include <math.h>

Functions

void vl_aib_normalize_P (double *P, vl_uint nelem)
 Normalizes an array of probabilities to sum to 1.
vl_uint * vl_aib_new_nodelist (vl_uint nentries)
 Allocates and creates a list of nodes.
double * vl_aib_new_Px (double *Pcx, vl_uint nvalues, vl_uint nlabels)
 Allocates and creates the marginal distribution Px.
double * vl_aib_new_Pc (double *Pcx, vl_uint nvalues, vl_uint nlabels)
 Allocates and creates the marginal distribution Pc.
void vl_aib_min_beta (VlAIB *aib, vl_uint *besti, vl_uint *bestj, double *minbeta)
 Find the two nodes which have minimum beta.
void vl_aib_merge_nodes (VlAIB *aib, vl_uint i, vl_uint j, vl_uint new)
 Merges two nodes i,j in the internal datastructure.
void vl_aib_update_beta (VlAIB *aib)
 Updates aib->beta and aib->bidx according to aib->which.
void vl_aib_calculate_information (VlAIB *aib, double *I, double *H)
 Calculates the current information and entropy.
VlAIB * vl_aib_new (double *Pcx, vl_uint nvalues, vl_uint nlabels)
 Allocates and initializes the internal data structure.
void vl_aib_delete (VlAIB *aib)
 Deletes AIB data structure.
void vl_aib_process (VlAIB *aib)
 Runs AIB on Pcx.

Detailed Description

Author:
Brian Fulkerson
Andrea Vedaldi

Function Documentation

void vl_aib_calculate_information ( VlAIB *  aib,
double *  I,
double *  H 
)
Parameters:
aibA pointer to the internal data structure
IThe current mutual information (out).
HThe current entropy (out).

Calculates the current mutual information and entropy of Pcx and sets I and H to these new values.

void vl_aib_delete ( VlAIB *  aib)
Parameters:
aibdata structure to delete.
void vl_aib_merge_nodes ( VlAIB *  aib,
vl_uint  i,
vl_uint  j,
vl_uint  new 
)
Parameters:
aibA pointer to the internal data structure
iThe index of one member of the pair to merge
jThe index of the other member of the pair to merge
newThe index of the new node which corresponds to the union of (i, j).

Nodes are merged by replacing the entry i with the union of ij, moving the node stored in last position (called lastnode) back to jth position and the entry at the end.

After the nodes have been merged, it updates which nodes should be considered on the next iteration based on which beta values could potentially change. The merged node will always be part of this list.

void vl_aib_min_beta ( VlAIB *  aib,
vl_uint *  besti,
vl_uint *  bestj,
double *  minbeta 
)
Parameters:
aibA pointer to the internal data structure
bestiThe index of one member of the pair which has mininum beta
bestjThe index of the other member of the pair which minimizes beta
minbetaThe minimum beta value corresponding to (i, j)

Searches aib->beta to find the minimum value and fills minbeta and besti and bestj with this information.

VlAIB* vl_aib_new ( double *  Pcx,
vl_uint  nvalues,
vl_uint  nlabels 
)
Parameters:
PcxA pointer to a 2D array of probabilities
nvaluesThe number of rows in the array
nlabelsThe number of columns in the array

Creates a new VlAIB struct containing pointers to all the data that will be used during the AIB process.

Allocates memory for the following:

  • Px (nvalues*sizeof(double))
  • Pc (nlabels*sizeof(double))
  • nodelist (nvalues*sizeof(vl_uint))
  • which (nvalues*sizeof(vl_uint))
  • beta (nvalues*sizeof(double))
  • bidx (nvalues*sizeof(vl_uint))
  • parents ((2*nvalues-1)*sizeof(vl_uint))
  • costs (nvalues*sizeof(double))

Since it simply copies to pointer to Pcx, the total additional memory requirement is:

(3*nvalues+nlabels)*sizeof(double) + 4*nvalues*sizeof(vl_uint)

Returns:
An allocated and initialized VlAIB pointer
vl_uint* vl_aib_new_nodelist ( vl_uint  nentries)
Parameters:
nentriesThe size of the list which will be created
Returns:
an array containing elements 0...nentries
double* vl_aib_new_Pc ( double *  Pcx,
vl_uint  nvalues,
vl_uint  nlabels 
)
Parameters:
PcxA two-dimensional array of probabilities
nvaluesThe number of rows in Pcx
nlabelsThe number of columns in Pcx
Returns:
an array of size nlabels which contains the marginal distribution over the columns
double* vl_aib_new_Px ( double *  Pcx,
vl_uint  nvalues,
vl_uint  nlabels 
)
Parameters:
PcxA two-dimensional array of probabilities
nvaluesThe number of rows in Pcx
nlabelsThe number of columns in Pcx
Returns:
an array of size nvalues which contains the marginal distribution over the rows.
void vl_aib_normalize_P ( double *  P,
vl_uint  nelem 
)
Parameters:
PThe array of probabilities
nelemThe number of elements in the array
Returns:
Modifies P to contain values which sum to 1
void vl_aib_process ( VlAIB *  aib)
Parameters:
aibAIB object to process

The function runs Agglomerative Information Bottleneck (AIB) on the joint probability table aib->Pcx which has labels along the columns and feature values along the rows. AIB iteratively merges the two values of the feature x that causes the smallest decrease in mutual information between the random variables x and c.

Merge operations are arranged in a binary tree. The nodes of the tree correspond to the original feature values and any other value obtained as a result of a merge operation. The nodes are indexed in breadth-first order, starting from the leaves. The first index is zero. In this way, the leaves correspond directly to the original feature values. In total there are 2*nvalues-1 nodes.

The results may be accessed through vl_aib_get_parents which returns an array with one element per tree node. Each element is the index the parent node. The root parent is equal to zero. The array has 2*nvalues-1 elements.

Feature values with null probability are ignored by the algorithm and their nodes have parents indexing a non-existent tree node (a value bigger than 2*nvalues-1).

Then the function will also compute the information level after each merge. vl_get_costs will return a vector with the information level after each merge. cost has nvalues entries: The first is the value of the cost functional before any merge, and the others are the cost after the nvalues-1 merges.

void vl_aib_update_beta ( VlAIB *  aib)
Parameters:
aibAIB data structure.

The function calculates beta[i] and bidx[i] for the nodes i listed in aib->which. beta[i] is the minimal variation of mutual information (or other score) caused by merging entry i with another entry and bidx[i] is the index of this best matching entry.

Notice that for each entry i that we need to update, a full scan of all the other entries must be performed.

 
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Data Structures
aib.h File Reference

AIB (Agglomerative Information Bottleneck (AIB)) More...

#include "generic.h"
#include "mathop.h"

Data Structures

struct  VlAIB
 AIB algorithm data. More...

Functions

Create and destroy
VlAIB * vl_aib_new (double *Pcx, vl_uint nvalues, vl_uint nlabels)
 Allocates and initializes the internal data structure.
void vl_aib_delete (VlAIB *aib)
 Deletes AIB data structure.
Process data
void vl_aib_process (VlAIB *aib)
 Runs AIB on Pcx.
Retrieve results
vl_uint * vl_aib_get_parents (VlAIB const *aib)
 Get resulting list of parents.
double * vl_aib_get_costs (VlAIB const *aib)
 Get a list of merge costs.

Detailed Description

Author:
Brian Fulkerson
Andrea Vedaldi

Function Documentation

void vl_aib_delete ( VlAIB *  aib)
Parameters:
aibdata structure to delete.
double * vl_aib_get_costs ( VlAIB const *  aib) [inline]
Parameters:
aibAIB filter.
Returns:
An array of costs
vl_uint * vl_aib_get_parents ( VlAIB const *  aib) [inline]
Parameters:
aibAIB filter.
Returns:
An array of parents
VlAIB* vl_aib_new ( double *  Pcx,
vl_uint  nvalues,
vl_uint  nlabels 
)
Parameters:
PcxA pointer to a 2D array of probabilities
nvaluesThe number of rows in the array
nlabelsThe number of columns in the array

Creates a new VlAIB struct containing pointers to all the data that will be used during the AIB process.

Allocates memory for the following:

  • Px (nvalues*sizeof(double))
  • Pc (nlabels*sizeof(double))
  • nodelist (nvalues*sizeof(vl_uint))
  • which (nvalues*sizeof(vl_uint))
  • beta (nvalues*sizeof(double))
  • bidx (nvalues*sizeof(vl_uint))
  • parents ((2*nvalues-1)*sizeof(vl_uint))
  • costs (nvalues*sizeof(double))

Since it simply copies to pointer to Pcx, the total additional memory requirement is:

(3*nvalues+nlabels)*sizeof(double) + 4*nvalues*sizeof(vl_uint)

Returns:
An allocated and initialized VlAIB pointer
void vl_aib_process ( VlAIB *  aib)
Parameters:
aibAIB object to process

The function runs Agglomerative Information Bottleneck (AIB) on the joint probability table aib->Pcx which has labels along the columns and feature values along the rows. AIB iteratively merges the two values of the feature x that causes the smallest decrease in mutual information between the random variables x and c.

Merge operations are arranged in a binary tree. The nodes of the tree correspond to the original feature values and any other value obtained as a result of a merge operation. The nodes are indexed in breadth-first order, starting from the leaves. The first index is zero. In this way, the leaves correspond directly to the original feature values. In total there are 2*nvalues-1 nodes.

The results may be accessed through vl_aib_get_parents which returns an array with one element per tree node. Each element is the index the parent node. The root parent is equal to zero. The array has 2*nvalues-1 elements.

Feature values with null probability are ignored by the algorithm and their nodes have parents indexing a non-existent tree node (a value bigger than 2*nvalues-1).

Then the function will also compute the information level after each merge. vl_get_costs will return a vector with the information level after each merge. cost has nvalues entries: The first is the value of the cost functional before any merge, and the others are the cost after the nvalues-1 merges.

 
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Data Structures
Here are the data structures with brief descriptions:
optiongetopt_long option
VlAIBAIB algorithm data
VlArrayNumeric array
VlDsiftDescriptorGeometryDense SIFT descriptor geometry
VlDsiftFilterDense SIFT filter
VlDsiftKeypointDense SIFT keypoint
VlFileMetaSupport for command line drivers - Definition
VlHIKMNodeHIKM tree node
VlHIKMTreeHIKM tree
VlHomogeneousKernelMapHomogeneous kernel map
VlIKMFiltIKM quantizer
VlKDForestKDForest object
VlKDForestNeighborNeighbor of a query point
VlKMeansK-means quantizer
VlLbpLocal Binary Pattern extractor
VlMserExtrRegMSER: extremal region (declaration)
VlMserFiltMSER filter
VlMserRegMSER: basic region (declaration)
VlMserStatsMSER filter statistics definition
vlmxOptionMEX option
VlPgmImagePGM image meta data
VlQSQuick shift results
VlRandRandom numbber generator state
VlSiftFiltSIFT filter
VlSiftKeypointSIFT filter keypoint
VlStateVLFeat global state
VlThreadSpecificStateVLFeat thread state
 
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Data Structures | Defines
array.h File Reference

Array. More...

#include "generic.h"

Data Structures

struct  VlArray
 Numeric array. More...

Defines

#define VL_ARRAY_MAX_NUM_DIMENSIONS   16
 Maximum number of array dimensions.

Functions

Get data and parameters
vl_size vl_array_get_num_dimensions (VlArray const *self)
 Get number of dimensions.
vl_size const * vl_array_get_dimensions (VlArray const *self)
 Get dimensions.
void * vl_array_get_data (VlArray const *self)
 Get data.
vl_type vl_array_get_data_type (VlArray const *self)
 Get type.
vl_size vl_array_get_num_elements (VlArray const *self)
 Get number of elements in array.
Constructing and destroying
VlArray * vl_array_init (VlArray *self, vl_type type, vl_size numDimension, vl_size const *dimensions)
 New numeric array.
VlArray * vl_array_init_envelope (VlArray *self, void *data, vl_type type, vl_size numDimension, vl_size const *dimensions)
 New numeric array envelope.
VlArray * vl_array_init_matrix (VlArray *self, vl_type type, vl_size numRows, vl_size numColumns)
 New numeric array with matrix shape.
VlArray * vl_array_init_matrix_envelope (VlArray *self, void *data, vl_type type, vl_size numRows, vl_size numColumns)
 New numeric array envelpe with matrix shape.
VlArray * vl_array_new (vl_type type, vl_size numDimension, vl_size const *dimensions)
 New numeric array.
VlArray * vl_array_new_envelope (void *data, vl_type type, vl_size numDimension, vl_size const *dimensions)
 New numeric array envelope.
VlArray * vl_array_new_matrix (vl_type type, vl_size numRows, vl_size numColumns)
 New numeric array with matrix shape.
VlArray * vl_array_new_matrix_envelope (void *data, vl_type type, vl_size numRows, vl_size numColumns)
 New numeric array envelpe with matrix shape.
void vl_array_dealloc (VlArray *self)
 Delete array.
void vl_array_delete (VlArray *self)
 Delete array.

Detailed Description

Array - Definition.

Author:
Andrea Vedaldi

Function Documentation

void vl_array_dealloc ( VlArray *  self)
Parameters:
selfarray.
void vl_array_delete ( VlArray *  self)
Parameters:
selfarray.
void* vl_array_get_data ( VlArray const *  self) [inline]
Parameters:
selfarray.
Returns:
data.
vl_type vl_array_get_data_type ( VlArray const *  self) [inline]
Parameters:
selfarray.
Returns:
type.
vl_size const* vl_array_get_dimensions ( VlArray const *  self) [inline]
Parameters:
selfarray.
Returns:
dimensions.
vl_size vl_array_get_num_dimensions ( VlArray const *  self) [inline]
Parameters:
selfarray.
Returns:
number of dimensions.
vl_size vl_array_get_num_elements ( VlArray const *  self)
Parameters:
selfarray.
Returns:
number of elements.
VlArray* vl_array_init ( VlArray *  self,
vl_type  type,
vl_size  numDimensions,
vl_size const *  dimensions 
)
Parameters:
selfarray to initialize.
typedata type.
numDimensionsnumber of dimensions.
dimensionsdimensions.

The function initializes the specified array and allocates the necessary memory for storage.

VlArray* vl_array_init_envelope ( VlArray *  self,
void *  data,
vl_type  type,
vl_size  numDimensions,
vl_size const *  dimensions 
)
Parameters:
selfarray to initialize.
datadata to envelople.
typedata type.
numDimensionsnumber of dimensions.
dimensionsdimensions.

The function initializes the specified array wrapping the specified buffer.

VlArray* vl_array_init_matrix ( VlArray *  self,
vl_type  type,
vl_size  numRows,
vl_size  numColumns 
)
Parameters:
selfarray to initialize.
typetype.
numRowsnumber of rows.
numColumnsnumber of columns.
VlArray* vl_array_init_matrix_envelope ( VlArray *  self,
void *  data,
vl_type  type,
vl_size  numRows,
vl_size  numColumns 
)
Parameters:
selfarray to initialize.
datadata to envelope.
typetype.
numRowsnumber of rows.
numColumnsnumber of columns.
VlArray* vl_array_new ( vl_type  type,
vl_size  numDimensions,
vl_size const *  dimensions 
)
Parameters:
typedata type.
numDimensionsnumber of dimensions.
dimensionsdimensions.

The function creates a new VLArray instance and allocates the necessary memory for storage.

VlArray* vl_array_new_envelope ( void *  data,
vl_type  type,
vl_size  numDimensions,
vl_size const *  dimensions 
)
Parameters:
datadata to envelople.
typedata type.
numDimensionsnumber of dimensions.
dimensionsdimensions.
VlArray* vl_array_new_matrix ( vl_type  type,
vl_size  numRows,
vl_size  numColumns 
)
Parameters:
typetype.
numRowsnumber of rows.
numColumnsnumber of columns.
VlArray* vl_array_new_matrix_envelope ( void *  data,
vl_type  type,
vl_size  numRows,
vl_size  numColumns 
)
Parameters:
datadata to envelope.
typetype.
numRowsnumber of rows.
numColumnsnumber of columns.
 
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Bug List
Global vl_pgm_read_new (char const *name, VlPgmImage *im, vl_uint8 **data)
Only PGM files with 1 BPP are supported.
Global vl_pgm_read_new_f (char const *name, VlPgmImage *im, float **data)
Only PGM files with 1 BPP are supported.
 
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Bibliographic References
[1]
R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Susstrunk. SLIC superpixels. Technical report, EPFL, 2010.
[2]
D. Arthur and S. Vassilvitskii. k-means++: The advantages of careful seeding. In Proc. ACM-SIAM Symp. on Discrete Algorithms, 2007.
[3]
J. S. Beis and D. G. Lowe. Shape indexing using approximate nearest-neighbour search in high-dimensional spaces. In Proc. CVPR, 1997.
[4]
C. Elkan. Using the triangle inequality to accelerate $k$-means. In Proc. ICML, 2003.
[5]
S. Lloyd. Least square quantization in PCM. IEEE Trans. on Information Theory, 28(2), 1982.
[6]
D. G. Lowe. Object recognition from local scale-invariant features. In Proc. ICCV, 1999.
[7]
J. Matas, O. Chum, M. Urban, and T. Pajdla. Robust wide baseline stereo from maximally stable extremal regions. In Proc. BMVC, 2002.
[8]
M. Muja and D. G. Lowe. Fast approximate nearest neighbors with automatic algorithmic configuration. In Proc. VISAPP, 2009.
[9]
S. Shalev-Shwartz, Y. Singer, and N. Srebro. Pegasos: Primal estimated sub-GrAdient SOlver for SVM. In Proc. ICML, 2007.
[10]
C. Silpa-Anan and R. Hartley. Optimised KD-trees for fast image descriptor matching. In Proc. CVPR, 2008.
[11]
N. Slonim and N. Tishby. Agglomerative information bottleneck. In Proc. NIPS, 1999.
[12]
A. Vedaldi and S. Soatto. Quick shift and kernel methods for mode seeking. In Proc. ECCV, 2008.
[13]
A. Vedaldi and A. Zisserman. Efficient additive kernels via explicit feature maps. In Proc. CVPR, 2010.
[14]
A. Vedaldi and A. Zisserman. Efficient additive kernels via explicit feature maps. In PAMI, 2011.
 
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VLFeat design concepts

VLFeat is designed to be portable and simple to integrate with high level languages such as MATLAB. This section illustrates and motivates the aspects of the design that are relevant to the users of the library.

Memory and resource handling

Some VLFeat functions return pointers to memory blocks or objects. Only vl_malloc, vl_calloc, vl_realloc, or functions whose name contains either the keywords new or copy, transfer the ownership of the memory block or object to the caller. The caller must dispose explicitly of all the resources he owns (by calling vl_free for a memory block, or the appropriate destructor for an object).

The memory allocation functions can be customized by vl_set_alloc_func (which sets the implementations of vl_malloc, vl_realloc, vl_calloc and vl_free). Remapping the memory allocation functions can be done only if there are no currently allocated VLFeat memory blocks or objects. The memory allocation functions are common to all threads.

VLFeat uses three rules that simplify handling exceptions:

  • The only resource referenced by VLFeat objects is memory (for instance, it is illegal for an object to reference an open file). Other resources such as files or threads may be allocated within a VLFeat function call, but they are all released before the function ends, or their ownership is directly transferred to the caller.
  • The global library state is an exception. It cannot reference any local object created by the caller and uses the standard C memory allocation functions.

In this way, the VLFeat local state can be reset at any time simply by disposing of all the memory allocated so far. The latter can be done easily by mapping the memory allocation functions to implementations that track the memory blocks allocated, and simply disposing of all such blocks. Since the global state does not reference any local object nor uses the remapped memory functions, it is unaffected by such an operation; conversely, since no VLFeat object references anything but memory, this guarantees that all allocated resources are properly disposed (no leaks). This is used extensively in the design of MATLAB MEX files (see MATLAB integration issues).

Objects

Many VLFeat algorithms are availale in the form of “objects”. Notice that the C language, used by VLFeat, does not support objects explicitly. Here an object indicates an opaque data structure along with a number of functions (methods) operationg on it.

Object names are captilaised and start with the Vl prefix (for example VlSiftFilt). Object methods are lowercase and start with the vl_<object_name>_ suffix (e.g. vl_sift_new). Object methods typraically include a constructor (e.g. vl_sift_new), a destructor (vl_sift_delete), some getter methods (vl_sift_get_octave_index), and some setter methods (vl_sift_set_magnif).

Multi-threading

VLFeat can be used with multiple threads by treating appropriately different kinds of operations:

  • Local operations (reentrancy). Most VLFeat operations are reentrant, in the sense that they operate on local data. It is safe to execute such operations concurrently from multiple threads as long as each thread operates on an independent sets of objects or memory blocks. By contrast, operating on the same object or memory block from multiple threads requires proper synchronization by the user.
  • Task-specific operations. A few operations are intrinsically non-reentrant but thread-specific. These include: retrieving the last error by vl_get_last_error and obtaining the thread-specific random number generator by vl_get_rand. VLFeat makes such operations thread-safe by operating on task-specific data.
  • Global operations. A small number of operations are non-reentrant and affect all threads simultaneously. These are restricted to changing certain global configuration parameters, such as the memory allocation functions by vl_set_alloc_func. These operations are not thread safe and should be executed before multiple threads are started.

Some VLFeat algorithms are randomised. Each thread has his own random number generator (an instance of VlRand) accessed by vl_get_rand. To make calculations reproducible the random number generator must be seeded appropriately in each thread. Notice also that using the same VLFeat object from multiple threads (with appropriate synchronization) will cause multiple random number generators to be intermixed.

Portability features

Platform dependent details are isolated in the host.h library module. These include:

VLFeat uses processor specific features (e.g. Intel SSE) if those are available at run time.

MATLAB integration issues

The VLFeat C library is designed to integrate seamlessly with MATLAB. Binary compatibility is simplified by the use of the C language (rather than C++). In addition, the library design follows certain restrictons that make it compatible with the MATLAB MEX interface.

The main issue in calling a library function from a MATLAB MEX function is that MATLAB can abort the execution of the MEX function at any point, either due to an error, or directly upon a user request (Ctrl-C) (empirically, however, a MEX function seems to be interruptible only during the invocation of certain functions of the MEX API such as mexErrMsgTxt).

When a MEX function is interrupted, resources (memory blocks or objects) whose ownership was transferred from VLFeat to the MEX function may be leaked. Notice that interrupting a MEX function would similarly leak any memory block allocated within the MEX function. To solve this issue, MATLAB provides his own memory manager (mxMalloc, mxRealloc, ...). When a MEX file is interrupted or ends, all memory blocks allocated by using one of such functions are released, preventing leakage.

In order to integrate VLFeat with this model in the most seamless way, VLFeat memory allocation functions (vl_malloc, vl_realloc, vl_calloc) are mapped to the corresponding MEX memory allocation functions. Such functions automatically dispose of all the memory allocated by a MEX function when the function ends (even because of an exception). Because of the restrictions of the library design illustrated in Memory and resource handling, this operation is safe and correctly dispose of VLFeat local state. As a consequence, it is possible to call mexErrMsgTxt at any point in the MEX function without worring about leaking resources.

This however comes at the price of some limitations. Beyond the restrictions illustred in Memory and resource handling, here we note that no VLFeat local resoruce (memory blocks or objects) can persist across MEX file invocations. This implies that any result produced by a VLFeat MEX function must be converted back to a MATLAB object such as a vector or a structure. In particular, there is no direct way of creating an object within a MEX file, returning it to MATLAB, and passing it again to another MEX file.

Preprocessor metaprogramming

Part of VLFeat code uses a simple form of perprocessor metaprogramming. This technique is used, similarly to C++ templates, to instantiate multiple version of a given algorithm for different data types (e.g. float and double).

In most cases preprocessor metaprogramming is invisible to the library user, as it is used only internally.

 
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Documentation - C API
Dense Scale Invariant Feature Transform (DSIFT)
Author:
Andrea Vedaldi
Brian Fulkerson

dsift.h implements a dense version of SIFT. This is an object that can quickly compute descriptors for densely sampled keypoints with identical size and orientation. It can be reused for multiple images of the same size.

Overview

See also:
The SIFT module, Technical details

This module implements a fast algorithm for the calculation of a large number of SIFT descriptors of densely sampled features of the same scale and orientation. See the SIFT module for an overview of SIFT.

The feature frames (keypoints) are indirectly specified by the sampling steps (vl_dsift_set_steps) and the sampling bounds (vl_dsift_set_bounds). The descriptor geometry (number and size of the spatial bins and number of orientation bins) can be customized (vl_dsift_set_geometry, VlDsiftDescriptorGeometry).

dsift-geom.png
Dense SIFT descriptor geometry

By default, SIFT uses a Gaussian windowing function that discounts contributions of gradients further away from the descriptor centers. This function can be changed to a flat window by invoking vl_dsift_set_flat_window. In this case, gradients are accumulated using only bilinear interpolation, but instad of being reweighted by a Gassuain window, they are all weighted equally. However, after gradients have been accumulated into a spatial bin, the whole bin is reweighted by the average of the Gaussian window over the spatial support of that bin. This “approximation” substantially improves speed with little or no loss of performance in applications.

Keypoints are sampled in such a way that the centers of the spatial bins are at integer coordinates within the image boundaries. For instance, the top-left bin of the top-left descriptor is centered on the pixel (0,0). The bin immediately to the right at (binSizeX,0), where binSizeX is a paramtere in the VlDsiftDescriptorGeometry structure. vl_dsift_set_bounds can be used to further restrict sampling to the keypoints in an image.

Usage

DSIFT is implemented by a VlDsiftFilter object that can be used to process a sequence of images of a given geometry. To use the DSIFT filter:

Technical details

This section extends the SIFT descriptor section and specialzies it to the case of dense keypoints.

Dense descriptors

When computing descriptors for many keypoints differing only by their position (and with null rotation), further simplifications are possible. In this case, in fact,

\begin{eqnarray*} \mathbf{x} &=& m \sigma \hat{\mathbf{x}} + T,\\ h(t,i,j) &=& m \sigma \int g_{\sigma_\mathrm{win}}(\mathbf{x} - T)\, w_\mathrm{ang}(\angle J(\mathbf{x}) - \theta_t)\, w\left(\frac{x - T_x}{m\sigma} - \hat{x}_i\right)\, w\left(\frac{y - T_y}{m\sigma} - \hat{y}_j\right)\, |J(\mathbf{x})|\, d\mathbf{x}. \end{eqnarray*}

Since many different values of T are sampled, this is conveniently expressed as a separable convolution. First, we translate by $ \mathbf{x}_{ij} = m\sigma(\hat x_i,\ \hat y_i)^\top $ and we use the symmetry of the various binning and windowing functions to write

\begin{eqnarray*} h(t,i,j) &=& m \sigma \int g_{\sigma_\mathrm{win}}(T' - \mathbf{x} - \mathbf{x}_{ij})\, w_\mathrm{ang}(\angle J(\mathbf{x}) - \theta_t)\, w\left(\frac{T'_x - x}{m\sigma}\right)\, w\left(\frac{T'_y - y}{m\sigma}\right)\, |J(\mathbf{x})|\, d\mathbf{x}, \\ T' &=& T + m\sigma \left[\begin{array}{cc} x_i \\ y_j \end{array}\right]. \end{eqnarray*}

Then we define kernels

\begin{eqnarray*} k_i(x) &=& \frac{1}{\sqrt{2\pi} \sigma_{\mathrm{win}}} \exp\left( -\frac{1}{2} \frac{(x-x_i)^2}{\sigma_{\mathrm{win}}^2} \right) w\left(\frac{x}{m\sigma}\right), \\ k_j(y) &=& \frac{1}{\sqrt{2\pi} \sigma_{\mathrm{win}}} \exp\left( -\frac{1}{2} \frac{(y-y_j)^2}{\sigma_{\mathrm{win}}^2} \right) w\left(\frac{y}{m\sigma}\right), \end{eqnarray*}

and obtain

\begin{eqnarray*} h(t,i,j) &=& (k_ik_j * \bar J_t)\left( T + m\sigma \left[\begin{array}{cc} x_i \\ y_j \end{array}\right] \right), \\ \bar J_t(\mathbf{x}) &=& w_\mathrm{ang}(\angle J(\mathbf{x}) - \theta_t)\,|J(\mathbf{x})|. \end{eqnarray*}

Furthermore, if we use a flat rather than Gaussian windowing function, the kernels do not depend on the bin, and we have

\begin{eqnarray*} k(z) &=& \frac{1}{\sigma_{\mathrm{win}}} w\left(\frac{z}{m\sigma}\right), \\ h(t,i,j) &=& (k(x)k(y) * \bar J_t)\left( T + m\sigma \left[\begin{array}{cc} x_i \\ y_j \end{array}\right] \right), \end{eqnarray*}

(here $ \sigma_\mathrm{win} $ is the side of the flat window).

Note:
In this case the binning functions $ k(z) $ are triangular and the convolution can be computed in time independent on the filter (i.e. descriptor bin) support size by integral signals.

Sampling

To avoid resampling and dealing with special boundary conditions, we impose some mild restrictions on the geometry of the descriptors that can be computed. In particular, we impose that the bin centers $ T + m\sigma (x_i,\ y_j) $ are always at integer coordinates within the image boundaries. This eliminates the need for costly interpolation. This condition amounts to (expressed in terms of the x coordinate, and equally applicable to y)

\[ \{0,\dots, W-1\} \ni T_x + m\sigma x_i = T_x + m\sigma i - \frac{N_x-1}{2} = \bar T_x + m\sigma i, \qquad i = 0,\dots,N_x-1. \]

Notice that for this condition to be satisfied, the descriptor center $ T_x $ needs to be either fractional or integer depending on $ N_x $ being even or odd. To eliminate this complication, it is simpler to use as a reference not the descriptor center T, but the coordinates of the upper-left bin $ \bar T $. Thus we sample the latter on a regular (integer) grid

\[ \left[\begin{array}{cc} 0 \\ 0 \end{array}\right] \leq \bar T = \left[\begin{array}{cc} \bar T_x^{\min} + p \Delta_x \\ \bar T_y^{\min} + q \Delta_y \\ \end{array}\right] \leq \left[\begin{array}{cc} W - 1 - m\sigma N_x \\ H - 1 - m\sigma N_y \end{array}\right], \quad \bar T = \left[\begin{array}{cc} T_x - \frac{N_x - 1}{2} \\ T_y - \frac{N_y - 1}{2} \\ \end{array}\right] \]

and we impose that the bin size $ m \sigma $ is integer as well.

 
vlfeat-0.9.14/doc/api/dsift_8c.html000066400000000000000000000517601167540017400170130ustar00rootroot00000000000000 VLFeat - Documentation - C API
Documentation - C API
Functions
dsift.c File Reference

Dense SIFT - Definition. More...

#include "dsift.h"
#include "pgm.h"
#include "mathop.h"
#include "imopv.h"
#include <math.h>
#include <string.h>

Functions

float * _vl_dsift_new_kernel (int binSize, int numBins, int binIndex, double windowSize)
 Initialize new convolution kernel.
float _vl_dsift_normalize_histogram (float *begin, float *end)
 Normalize histogram.
static void _vl_dsift_free_buffers (VlDsiftFilter *self)
 Free internal buffers.
void _vl_dsift_update_buffers (VlDsiftFilter *self)
 Updates internal buffers to current geometry.
static void _vl_dsift_alloc_buffers (VlDsiftFilter *self)
 Allocate internal buffers.
VlDsiftFilter * vl_dsift_new (int imWidth, int imHeight)
 Create a new DSIFT filter.
VlDsiftFilter * vl_dsift_new_basic (int imWidth, int imHeight, int step, int binSize)
 Create a new DSIFT filter (basic interface)
void vl_dsift_delete (VlDsiftFilter *self)
 Delete DSIFT filter.
void _vl_dsift_with_gaussian_window (VlDsiftFilter *self)
 Process with Gaussian window.
void _vl_dsift_with_flat_window (VlDsiftFilter *self)
 Process with flat window.
void vl_dsift_process (VlDsiftFilter *self, float const *im)
 Compute keypoints and descriptors.

Detailed Description

Author:
Andrea Vedaldi
Brian Fulkerson

Function Documentation

static void _vl_dsift_alloc_buffers ( VlDsiftFilter *  self) [static]
Parameters:
selfDSIFT filter.

The function (re)allocates the internal buffers in accordance with the current image and descriptor geometry.

static void _vl_dsift_free_buffers ( VlDsiftFilter *  self) [static]
Parameters:
selfDSIFT filter.
float* _vl_dsift_new_kernel ( int  binSize,
int  numBins,
int  binIndex,
double  windowSize 
)
Parameters:
binSize
numBins
binIndexnegative to use flat window.
windowSize
Returns:
a pointer to new filter.
float _vl_dsift_normalize_histogram ( float *  begin,
float *  end 
) [inline]
Parameters:
beginfirst element of the histogram.
endlast plus one element of the histogram.

The function divides the specified histogram by its l2 norm.

void _vl_dsift_update_buffers ( VlDsiftFilter *  self)
void _vl_dsift_with_flat_window ( VlDsiftFilter *  self) [inline]
Parameters:
selfDSIFT filter object.
void _vl_dsift_with_gaussian_window ( VlDsiftFilter *  self) [inline]
Parameters:
selfDSIFT filter.
void vl_dsift_delete ( VlDsiftFilter *  self)
Parameters:
selfDSIFT filter.
VlDsiftFilter* vl_dsift_new ( int  imWidth,
int  imHeight 
)
Parameters:
imWidthwidth of the image.
imHeightheight of the image
Returns:
new filter.
VlDsiftFilter* vl_dsift_new_basic ( int  imWidth,
int  imHeight,
int  step,
int  binSize 
)
Parameters:
imWidthwidth of the image.
imHeightheight of the image.
stepsampling step.
binSizebin size.
Returns:
new filter.

The descriptor geometry matches the standard SIFT descriptor.

void vl_dsift_process ( VlDsiftFilter *  self,
float const *  im 
)
Parameters:
selfDSIFT filter.
imimage data.
 
vlfeat-0.9.14/doc/api/dsift_8h.html000066400000000000000000001161361167540017400170170ustar00rootroot00000000000000 VLFeat - Documentation - C API
Documentation - C API
Data Structures | Functions
dsift.h File Reference

Dense SIFT (Dense Scale Invariant Feature Transform (DSIFT)) More...

#include "generic.h"

Data Structures

struct  VlDsiftKeypoint
 Dense SIFT keypoint. More...
struct  VlDsiftDescriptorGeometry
 Dense SIFT descriptor geometry. More...
struct  VlDsiftFilter
 Dense SIFT filter. More...

Functions

VlDsiftFilter * vl_dsift_new (int width, int height)
 Create a new DSIFT filter.
VlDsiftFilter * vl_dsift_new_basic (int width, int height, int step, int binSize)
 Create a new DSIFT filter (basic interface)
void vl_dsift_delete (VlDsiftFilter *self)
 Delete DSIFT filter.
void vl_dsift_process (VlDsiftFilter *self, float const *im)
 Compute keypoints and descriptors.
void vl_dsift_transpose_descriptor (float *dst, float const *src, int numBinT, int numBinX, int numBinY)
 Transpose descriptor.
void _vl_dsift_update_buffers (VlDsiftFilter *self)
 Updates internal buffers to current geometry.
Setting parameters
void vl_dsift_set_steps (VlDsiftFilter *self, int stepX, int stepY)
 Set steps.
void vl_dsift_set_bounds (VlDsiftFilter *self, int minX, int minY, int maxX, int maxY)
 Set bounds.
void vl_dsift_set_geometry (VlDsiftFilter *self, VlDsiftDescriptorGeometry const *geom)
 Set SIFT descriptor geometry.
void vl_dsift_set_flat_window (VlDsiftFilter *self, vl_bool useFlatWindow)
 Set flat window flag.
void vl_dsift_set_window_size (VlDsiftFilter *self, double windowSize)
 Set SIFT descriptor Gaussian window size.
Retrieving data and parameters
float const * vl_dsift_get_descriptors (VlDsiftFilter const *self)
 Get descriptors.
int vl_dsift_get_descriptor_size (VlDsiftFilter const *self)
 Get descriptor size.
int vl_dsift_get_keypoint_num (VlDsiftFilter const *self)
 Get number of keypoints.
VlDsiftKeypoint const * vl_dsift_get_keypoints (VlDsiftFilter const *self)
 Get keypoints.
void vl_dsift_get_bounds (VlDsiftFilter const *self, int *minX, int *minY, int *maxX, int *maxY)
 Get bounds.
void vl_dsift_get_steps (VlDsiftFilter const *self, int *stepX, int *stepY)
 Get steps.
VlDsiftDescriptorGeometry const * vl_dsift_get_geometry (VlDsiftFilter const *self)
 Get SIFT descriptor geometry.
vl_bool vl_dsift_get_flat_window (VlDsiftFilter const *self)
 Get flat window flag.
double vl_dsift_get_window_size (VlDsiftFilter const *self)
 Get SIFT descriptor Gaussian window size.

Detailed Description

Author:
Andrea Vedaldi
Brian Fulkerson

Function Documentation

void _vl_dsift_update_buffers ( VlDsiftFilter *  self)
void vl_dsift_delete ( VlDsiftFilter *  self)
Parameters:
selfDSIFT filter.
void vl_dsift_get_bounds ( VlDsiftFilter const *  self,
int *  minX,
int *  minY,
int *  maxX,
int *  maxY 
) [inline]
Parameters:
selfDSIFT filter object.
minXbounding box minimum X coordinate.
minYbounding box minimum Y coordinate.
maxXbounding box maximum X coordinate.
maxYbounding box maximum Y coordinate.
int vl_dsift_get_descriptor_size ( VlDsiftFilter const *  self) [inline]
Parameters:
selfDSIFT filter object.
Returns:
size of a descriptor.
float const * vl_dsift_get_descriptors ( VlDsiftFilter const *  self) [inline]
Parameters:
selfDSIFT filter object.
Returns:
descriptors.
int vl_dsift_get_flat_window ( VlDsiftFilter const *  self) [inline]
Parameters:
selfDSIFT filter object.
Returns:
TRUE if the DSIFT filter uses a flat window.
VlDsiftDescriptorGeometry const * vl_dsift_get_geometry ( VlDsiftFilter const *  self) [inline]
Parameters:
selfDSIFT filter object.
Returns:
DSIFT descriptor geometry.
int vl_dsift_get_keypoint_num ( VlDsiftFilter const *  self) [inline]
Parameters:
selfDSIFT filter object.
VlDsiftKeypoint const * vl_dsift_get_keypoints ( VlDsiftFilter const *  self) [inline]
Parameters:
selfDSIFT filter object.
void vl_dsift_get_steps ( VlDsiftFilter const *  self,
int *  stepX,
int *  stepY 
) [inline]
Parameters:
selfDSIFT filter object.
stepXsampling step along X.
stepYsampling step along Y.
double vl_dsift_get_window_size ( VlDsiftFilter const *  self) [inline]
Parameters:
selfDSIFT filter object.
Returns:
window size.
VlDsiftFilter* vl_dsift_new ( int  imWidth,
int  imHeight 
)
Parameters:
imWidthwidth of the image.
imHeightheight of the image
Returns:
new filter.
VlDsiftFilter* vl_dsift_new_basic ( int  imWidth,
int  imHeight,
int  step,
int  binSize 
)
Parameters:
imWidthwidth of the image.
imHeightheight of the image.
stepsampling step.
binSizebin size.
Returns:
new filter.

The descriptor geometry matches the standard SIFT descriptor.

void vl_dsift_process ( VlDsiftFilter *  self,
float const *  im 
)
Parameters:
selfDSIFT filter.
imimage data.
void vl_dsift_set_bounds ( VlDsiftFilter *  self,
int  minX,
int  minY,
int  maxX,
int  maxY 
) [inline]
Parameters:
selfDSIFT filter object.
minXbounding box minimum X coordinate.
minYbounding box minimum Y coordinate.
maxXbounding box maximum X coordinate.
maxYbounding box maximum Y coordinate.
void vl_dsift_set_flat_window ( VlDsiftFilter *  self,
vl_bool  useFlatWindow 
) [inline]
Parameters:
selfDSIFT filter object.
useFlatWindowtrue if the DSIFT filter should use a flat window.
void vl_dsift_set_geometry ( VlDsiftFilter *  self,
VlDsiftDescriptorGeometry const *  geom 
) [inline]
Parameters:
selfDSIFT filter object.
geomdescriptor geometry parameters.
void vl_dsift_set_steps ( VlDsiftFilter *  self,
int  stepX,
int  stepY 
) [inline]
Parameters:
selfDSIFT filter object.
stepXsampling step along X.
stepYsampling step along Y.
void vl_dsift_set_window_size ( VlDsiftFilter *  self,
double  windowSize 
) [inline]
Parameters:
selfDSIFT filter object.
windowSizewindow size.
void vl_dsift_transpose_descriptor ( float *  dst,
float const *  src,
int  numBinT,
int  numBinX,
int  numBinY 
) [inline]
Parameters:
dstdestination buffer.
srcsource buffer.
numBinT
numBinX
numBinYThe function writes to dst the transpose of the SIFT descriptor src. Let I be an image. The transpose operator satisfies the equation transpose(dsift(I,x,y)) = dsift(transpose(I),y,x)
 
vlfeat-0.9.14/doc/api/files.html000066400000000000000000000303421167540017400164030ustar00rootroot00000000000000 VLFeat - Documentation - C API
Documentation - C API
File List
Here is a list of all documented files with brief descriptions:
aib.cAIB - Definition
aib.hAIB (Agglomerative Information Bottleneck (AIB))
array.hArray
dsift.cDense SIFT - Definition
dsift.hDense SIFT (Dense Scale Invariant Feature Transform (DSIFT))
generic.cGeneric - Definition
generic.hGeneric (Preprocssor, library state, etc.)
getopt_long.cGetopt_long - Definition
getopt_long.hGetopt_long
heap-def.hHeap preprocessor metaprogram
hikmeans.cHierarchical Integer K-Means Clustering - Declaration
hikmeans.hHierarchical integer K-Means clustering
homkermap.cHomogeneous kernel map - Definition
homkermap.hHomogeneous kernel map (Homogeneous kernel map)
host.cHost - Definition
host.hHost
ikmeans.cInteger K-Means clustering - Definition
ikmeans.hInteger K-Means clustering
ikmeans_elkan.tcInteger K-Means - Elkan Algorithm - Definition
ikmeans_init.tcInteger K-Means - Initialization - Definition
ikmeans_lloyd.tcInteger K-Means - LLoyd Algorithm - Definition
imopv.cVectorized image operations - Definition
imopv.hVectorized image operations
imopv_sse2.cVectorized image operations - SSE2 - Definition
imopv_sse2.hVectorized image operations - SSE2
kdtree.cKD-tree - Definition
kdtree.hKD-tree (KD-trees and forests)
kmeans.cK-means - Declaration
kmeans.hK-means (K-means clustering)
lbp.cLocal Binary Patterns - Definition
lbp.hLocal Binary Patterns
mathop.cMath operations - Definition
mathop.hMath operations
mathop_sse2.cMathop for SSE2 - Definition
mathop_sse2.hMathop for sse2
mexutils.hMEX utilities
mser.cMSER - Definition
mser.hMSER (Maximally Stable Extremal Regions)
pegasos.cPEGASOS - Definition
pegasos.hPEGASOS (PEGASOS SVM solver)
pgm.cPortable graymap format (PGM) parser - Definition
pgm.hPortable graymap format (PGM) parser
qsort-def.hQSort preprocessor metaprogram
quickshift.cQuick shift - Definition
quickshift.hQuick shift (Quick shift image segmentation)
random.cRandom number generator - Definition
random.hRandom number generator
rodrigues.h
shuffle-def.hShuffle preprocessor metaprogram
sift.cSIFT - Definition
sift.hSIFT (Scale Invariant Feature Transform (SIFT))
slic.cSLIC superpixels - Definition
slic.hSLIC superpixels (Simple Linear Iterative Clustering (SLIC))
stringop.cString operations - Definition
stringop.hString operations
 
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Documentation - C API
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vlfeat-0.9.14/doc/api/functions_vars.html000066400000000000000000000630641167540017400203530ustar00rootroot00000000000000 VLFeat - Documentation - C API
Documentation - C API
 

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vlfeat-0.9.14/doc/api/generic.html000066400000000000000000000215511167540017400167170ustar00rootroot00000000000000 VLFeat - Documentation - C API
Documentation - C API
Preprocssor, library state, etc.
Author:
Andrea Vedaldi

generic.h provides the following functionalities:

C preprocessor helpers

VLFeat provides a few C preprocessor macros of general utility. These include stringification (VL_STRINGIFY, VL_XSTRINGIFY) and concatenation (VL_CAT, VL_XCAT) of symbols.

VLFeat state and configuration parameters

VLFeat has some global configuration parameters that can changed. Changing the configuration is thread unsave (Multi-threading). Use vl_set_simd_enabled to toggle the use of a SIMD unit (Intel SSE code), vl_set_alloc_func to change the memory allocation functions, and vl_set_printf_func to change the logging function.

Error handling

Some VLFeat functions signal errors in a way similar to the standard C library. In case of error, a VLFeat function may return an error code directly, or an invalid result (for instance a negative file descriptor or a null pointer). Then vl_get_last_error and vl_get_last_error_message can be used to retrieve further details about the error (these functions should be used right after an error has occurred, before any other VLFeat call).

Memory allocation

VLFeat uses the vl_malloc, vl_realloc, vl_calloc and vl_free functions to allocate memory. Normally these functions are mapped to the underlying standard C library implementations. However vl_set_alloc_func can be used to map them to other implementations. For instance, in MATALB MEX files these functions are mapped to the MATLAB equivalent which has a garbage collection mechanism to cope with interruptions during execution.

Logging

VLFeat uses the macros VL_PRINT and VL_PRINTF to print progress or debug informations. These functions are normally mapped to the printf function of the underlying standard C library. However vl_set_printf_func can be used to map it to a different implementation. For instance, in MATLAB MEX files this function is mapped to mexPrintf. Setting the function to NULL disables logging.

Measuring time

VLFeat provides vl_tic and vl_toc as an easy way of measuring elapsed time.

 
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Documentation - C API
Functions | Variables
generic.c File Reference

Generic - Definition. More...

#include "generic.h"
#include <assert.h>
#include <stdlib.h>
#include <stdio.h>
#include <stdarg.h>
#include <math.h>
#include <Windows.h>
#include <unistd.h>

Functions

char const * vl_get_version_string ()
 Get version string.
char * vl_configuration_to_string_copy ()
 Human readable library configuration.
static int do_nothing_printf (char const *format,...)
 A printf that does not do anything.
void vl_lock_state ()
 Lock VLFeat state.
void vl_unlock_state ()
 Unlock VLFeat state.
int vl_set_last_error (int error, char const *errorMessage,...)
 Set last VLFeat error.
void vl_set_alloc_func (void *(*malloc_func)(size_t), void *(*realloc_func)(void *, size_t), void *(*calloc_func)(size_t, size_t), void(*free_func)(void *))
 Set memory allocation functions.
void vl_set_printf_func (printf_func_t printf_func)
 Set printf function.
double vl_get_cpu_time ()
 Get processor time.
void vl_tic ()
 Reset processor time reference The function resets VLFeat TIC/TOC time reference.
double vl_toc ()
 Get elapsed time since tic.
VlRand * vl_get_rand ()
 Get the random number generator for this thread.
static void vl_constructor ()
 Initialize VLFeat.
static void vl_destructor ()
 Destruct VLFeat.

Variables

VlState _vl_state

Detailed Description

Author:
Andrea Vedaldi

Function Documentation

static int do_nothing_printf ( char const *  format,
  ... 
) [static]
char* vl_configuration_to_string_copy ( )
Returns:
a new string with the library configuration.

The function returns a new string with a human readable rendition of the library configuration.

static void vl_constructor ( ) [static]
static void vl_destructor ( ) [static]
double vl_get_cpu_time ( )
Returns:
processor time.
See also:
vl_tic, vl_toc
VlRand* vl_get_rand ( )
Returns:
random number generator.

The function returns a pointer to the random number genrator for this thread.

char const* vl_get_version_string ( )
Returns:
library version string
void vl_lock_state ( )

The function locks VLFeat global state mutex.

The mutex is recursive: locking multiple times from the same thread is a valid operations, but requires an equivalent number of calls to vl_unlock_state.

See also:
vl_unlock_state
void vl_set_alloc_func ( void *(*)(size_t)  malloc_func,
void *(*)(void *, size_t)  realloc_func,
void *(*)(size_t, size_t)  calloc_func,
void(*)(void *)  free_func 
)
Parameters:
malloc_funcpointer to malloc.
realloc_funcpointer to realloc.
calloc_funcpointer to calloc.
free_funcpointer to free.
int vl_set_last_error ( int  error,
char const *  errorMessage,
  ... 
)

The function sets the code and optionally the error message of the last encountered error. errorMessage is the message format. It uses the printf convention and is followed by the format arguments. The maximum lenght of the error message is given by VL_ERR_MSG_LEN (longer messages are truncated).

Passing NULL as errorMessage sets the error message to the empty string.

Parameters:
errorerror code.
errorMessageerror message format string.
...format string arguments.
Returns:
error code.
void vl_set_printf_func ( printf_func_t  printf_func)
Parameters:
printf_funcpointer to printf. Let print_func be NULL to disable printf.
void vl_tic ( )
See also:
vl_get_cpu_time, vl_toc.
double vl_toc ( )

The function returns the processor time elapsed since vl_tic was called last.

Remarks:
In multi-threaded applications, there is an independent timer for each execution thread.
On UNIX, this function uses the clock() system call. On Windows, it uses the QueryPerformanceCounter() system call, which is more accurate than clock() on this platform.
Returns:
elapsed time in seconds.
void vl_unlock_state ( )

The function unlocks VLFeat global state mutex.

See also:
vl_lock_state

Variable Documentation

VlState _vl_state

---------------------------------------------------------------

 
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Data Structures | Defines
generic.h File Reference

Generic (Preprocssor, library state, etc.) More...

#include "host.h"
#include "random.h"
#include <stdlib.h>
#include <stddef.h>
#include <time.h>
#include <assert.h>
#include <Windows.h>

Data Structures

struct  VlThreadSpecificState
 VLFeat thread state. More...
struct  VlState
 VLFeat global state. More...

Defines

#define VL_VERSION_STRING   "0.9.14"
 Library version string.
#define VL_ERR_MSG_LEN   1024
 Maximum length (in characters) of an error message.
Common operations
#define VL_MIN(x, y)   (((x)<(y))?(x):(y))
 Min operation.
#define VL_MAX(x, y)   (((x)>(y))?(x):(y))
 Max operation.
#define VL_SHIFT_LEFT(x, n)   (((n)>=0)?((x)<<(n)):((x)>>-(n)))
 Signed left shift operation.

Functions

Memory allocation
void vl_set_alloc_func (void *(*malloc_func)(size_t), void *(*realloc_func)(void *, size_t), void *(*calloc_func)(size_t, size_t), void(*free_func)(void *))
 Set memory allocation functions.
void * vl_malloc (size_t n)
 Call customizable malloc function.
void * vl_realloc (void *ptr, size_t n)
 Call customizable resize function.
void * vl_calloc (size_t n, size_t size)
 Call customizable calloc function.
void vl_free (void *ptr)
 Call customizable free function.
Measuring time
void vl_tic ()
 Reset processor time reference The function resets VLFeat TIC/TOC time reference.
double vl_toc ()
 Get elapsed time since tic.
double vl_get_cpu_time ()
 Get processor time.

Type identidifers for atomic data types

#define VL_TYPE_FLOAT   1
#define VL_TYPE_DOUBLE   2
#define VL_TYPE_INT8   3
#define VL_TYPE_UINT8   4
#define VL_TYPE_INT16   5
#define VL_TYPE_UINT16   6
#define VL_TYPE_INT32   7
#define VL_TYPE_UINT32   8
#define VL_TYPE_INT64   9
#define VL_TYPE_UINT64   10
typedef vl_uint32 vl_type
char const * vl_get_type_name (vl_type type)
 Get the name of a data type.
vl_size vl_get_type_size (vl_type type)
 Get data type size.

Library state and configuration

VlState _vl_state
 VLFeat global state.
VlState * vl_get_state ()
 Return VLFeat global state.
VlThreadSpecificState * vl_get_thread_specific_state ()
 Get VLFeat thread state.
void vl_lock_state ()
 Lock VLFeat state.
void vl_unlock_state ()
 Unlock VLFeat state.
VlThreadSpecificState * vl_thread_specific_state_new ()
void vl_thread_specific_state_delete (VlThreadSpecificState *self)
char const * vl_get_version_string ()
 Get version string.
char * vl_configuration_to_string_copy ()
 Human readable library configuration.
void vl_set_simd_enabled (vl_bool x)
 Toggle usage of SIMD instructions.
vl_bool vl_get_simd_enabled ()
 Are SIMD instructons enabled?
vl_bool vl_cpu_has_sse3 ()
 Check for SSE3 instruction set.
vl_bool vl_cpu_has_sse2 ()
 Check for SSE2 instruction set.
int vl_get_num_cpus ()
VlRand * vl_get_rand ()
 Get the random number generator for this thread.

Error handling

#define VL_ERR_OK   0
#define VL_ERR_OVERFLOW   1
#define VL_ERR_ALLOC   2
#define VL_ERR_BAD_ARG   3
#define VL_ERR_IO   4
#define VL_ERR_EOF   5
#define VL_ERR_NO_MORE   5
int vl_get_last_error ()
 Get VLFeat last error code.
char const * vl_get_last_error_message ()
 Get VLFeat last error message.
int vl_set_last_error (int error, char const *errorMessage,...)
 Set last VLFeat error.

Logging

#define VL_PRINTF   (*vl_get_state()->printf_func)
 Call user-customizable printf function.
#define VL_PRINT   (*vl_get_state()->printf_func)
 Same as VL_PRINTF (legacy code)
typedef int(* printf_func_t )(char const *format,...)
 Customizable printf function pointer type.
void vl_set_printf_func (printf_func_t printf_func)
 Set printf function.

Detailed Description

Author:
Andrea Vedaldi

Define Documentation

#define VL_ERR_ALLOC   2

Resource allocation error

#define VL_ERR_BAD_ARG   3

Bad argument or illegal data error

#define VL_ERR_EOF   5

End-of-file or end-of-sequence error

#define VL_ERR_IO   4

Input/output error

#define VL_ERR_NO_MORE   5

End-of-sequence

Deprecated:
#define VL_ERR_OK   0

No error

#define VL_ERR_OVERFLOW   1

Buffer overflow error

#define VL_MAX (   x,
  y 
)    (((x)>(y))?(x):(y))
Parameters:
xvalue.
yvalue.
Returns:
the maximum of x and y.
#define VL_MIN (   x,
  y 
)    (((x)<(y))?(x):(y))
Parameters:
xvalue
yvalue
Returns:
the minimum of x and y.
#define VL_PRINTF   (*vl_get_state()->printf_func)

The function calls the user customizable printf.

#define VL_SHIFT_LEFT (   x,
  n 
)    (((n)>=0)?((x)<<(n)):((x)>>-(n)))

The macro is equivalent to the builtin << operator, but it supports negative shifts too.

Parameters:
xvalue.
nnumber of shift positions.
Returns:
x << n .
#define VL_TYPE_DOUBLE   2

double type

#define VL_TYPE_FLOAT   1

float type

#define VL_TYPE_INT16   5

vl_int16 type

#define VL_TYPE_INT32   7

vl_int32 type

#define VL_TYPE_INT64   9

vl_int64 type

#define VL_TYPE_INT8   3

vl_int8 type

#define VL_TYPE_UINT16   6

vl_uint16 type

#define VL_TYPE_UINT32   8

vl_uint32 type

#define VL_TYPE_UINT64   10

vl_uint64 type

#define VL_TYPE_UINT8   4

vl_uint8 type

Function Documentation

void * vl_calloc ( size_t  n,
size_t  size 
) [inline]
Parameters:
nsize of each element in byte.
sizesize of the array to allocate (number of elements).

The function calls the user-customizable calloc.

Returns:
result of the user-customizable calloc.
char* vl_configuration_to_string_copy ( )
Returns:
a new string with the library configuration.

The function returns a new string with a human readable rendition of the library configuration.

vl_bool vl_cpu_has_sse2 ( ) [inline]
Returns:
true if SSE2 is present.
vl_bool vl_cpu_has_sse3 ( ) [inline]
Returns:
true if SSE3 is present.
void vl_free ( void *  ptr) [inline]
Parameters:
ptrbuffer to free.

The function calls the user customizable free.

double vl_get_cpu_time ( )
Returns:
processor time.
See also:
vl_tic, vl_toc
int vl_get_last_error ( ) [inline]

The function returns the code of the last error generated by VLFeat.

Returns:
laste error code.
char const * vl_get_last_error_message ( ) [inline]

The function returns the message of the last error generated by VLFeat.

Returns:
last error message.
VlRand* vl_get_rand ( )
Returns:
random number generator.

The function returns a pointer to the random number genrator for this thread.

vl_bool vl_get_simd_enabled ( ) [inline]
Returns:
true is SIMD instructions are enabled.
VlState * vl_get_state ( ) [inline]

The function returns a pointer to VLFeat global state.

Returns:
pointer to the global state structure.
VlThreadSpecificState * vl_get_thread_specific_state ( ) [inline]

The function returns a pointer to VLFeat thread state.

Returns:
pointer to the thread state structure.
char const* vl_get_type_name ( vl_type  type) [inline]
Parameters:
typedata type.
Returns:
data name of the data type.

type is one of VL_TYPE_FLOAT, VL_TYPE_DOUBLE, VL_TYPE_INT8, VL_TYPE_INT16, VL_TYPE_INT32, VL_TYPE_INT64, VL_TYPE_UINT8, VL_TYPE_UINT16, VL_TYPE_UINT32, VL_TYPE_UINT64.

vl_size vl_get_type_size ( vl_type  type) [inline]
Parameters:
typedata type.
Returns:
size (in byte)

type is one of VL_TYPE_FLOAT, VL_TYPE_DOUBLE, VL_TYPE_INT8, VL_TYPE_INT16, VL_TYPE_INT32, VL_TYPE_INT64, VL_TYPE_UINT8, VL_TYPE_UINT16, VL_TYPE_UINT32, VL_TYPE_UINT64.

char const* vl_get_version_string ( )
Returns:
library version string
void vl_lock_state ( )

The function locks VLFeat global state mutex.

The mutex is recursive: locking multiple times from the same thread is a valid operations, but requires an equivalent number of calls to vl_unlock_state.

See also:
vl_unlock_state
void * vl_malloc ( size_t  n) [inline]
Parameters:
nnumber of bytes to allocate.

The function calls the user customizable malloc.

Returns:
result of malloc
void * vl_realloc ( void *  ptr,
size_t  n 
) [inline]
Parameters:
ptrbuffer to reallocate.
nnumber of bytes to allocate.

The function calls the user-customizable realloc.

Returns:
result of the user-customizable realloc.
void vl_set_alloc_func ( void *(*)(size_t)  malloc_func,
void *(*)(void *, size_t)  realloc_func,
void *(*)(size_t, size_t)  calloc_func,
void(*)(void *)  free_func 
)
Parameters:
malloc_funcpointer to malloc.
realloc_funcpointer to realloc.
calloc_funcpointer to calloc.
free_funcpointer to free.
int vl_set_last_error ( int  error,
char const *  errorMessage,
  ... 
)

The function sets the code and optionally the error message of the last encountered error. errorMessage is the message format. It uses the printf convention and is followed by the format arguments. The maximum lenght of the error message is given by VL_ERR_MSG_LEN (longer messages are truncated).

Passing NULL as errorMessage sets the error message to the empty string.

Parameters:
errorerror code.
errorMessageerror message format string.
...format string arguments.
Returns:
error code.
void vl_set_printf_func ( printf_func_t  printf_func)
Parameters:
printf_funcpointer to printf. Let print_func be NULL to disable printf.
void vl_set_simd_enabled ( vl_bool  x) [inline]
Parameters:
xtrue if SIMD instructions are used.

Notice that SIMD instructions are used only if the CPU model supports them. Note also that data alignment may restrict the use of such instructions.

See also:
vl_cpu_has_sse2(), vl_cpu_has_sse3(), etc.
void vl_tic ( )
See also:
vl_get_cpu_time, vl_toc.
double vl_toc ( )

The function returns the processor time elapsed since vl_tic was called last.

Remarks:
In multi-threaded applications, there is an independent timer for each execution thread.
On UNIX, this function uses the clock() system call. On Windows, it uses the QueryPerformanceCounter() system call, which is more accurate than clock() on this platform.
Returns:
elapsed time in seconds.
void vl_unlock_state ( )

The function unlocks VLFeat global state mutex.

See also:
vl_lock_state

Variable Documentation

VlState _vl_state
 
vlfeat-0.9.14/doc/api/getopt__long_8c.html000066400000000000000000000210021167540017400203440ustar00rootroot00000000000000 VLFeat - Documentation - C API
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Functions
getopt_long.c File Reference

getopt_long - Definition More...

#include <stdlib.h>
#include <string.h>
#include <stdio.h>
#include "generic.h"
#include "getopt_long.h"

Functions

int getopt_long (int argc, char *const argv[], const char *optstring, const struct option *longopts, int *longindex)
 Parse long options (BSD style)

Detailed Description

Author:
Andrea Vedaldi

Function Documentation

int getopt_long ( int  argc,
char *const  argv[],
const char *  optstring,
const struct option *  longopts,
int *  longindex 
)
Parameters:
argcnumber of arguments.
argvpointer to the vector of arguments.
optstringlist of abbreviated options
longoptslist of long options.
longindexindex of current option in longopts.
Returns:
the code of the next option.

This function extract long and short options from the argument list argv of argc entries.

A short options sequence is introduced by a singe dash character -. Each short option is described by a single character in the string optstring, possibly followed by a : character to denote a (mandatory) argument of the short option. A short option with an argument cannot appear in the middle of a short option sequence, but only at the end.

A long option is introduced by a double dash --. Each long option is described by an instance of the option structure in the longopts table (the last entry must be filled with zeroes to denote the end).

Illegal options and missing arguments cause the function to skip the option and return '?'. If opterr is true (default), the function prints an error message to stderr. Finally, if optstring has a leading :, then error messages are suppressed and a missing argument causes : to be returned.

Remarks:
The function is currently not thread safe.
 
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Documentation - C API
Data Structures | Defines | Functions | Variables
getopt_long.h File Reference

getopt_long More...

#include "generic.h"

Data Structures

struct  option
 getopt_long option More...

Defines

#define no_argument   0
#define required_argument   1
#define optional_argument   2

Functions

int getopt_long (int argc, char *const argv[], const char *optstring, const struct option *longopts, int *longindex)
 Parse long options (BSD style)

Variables

int opterr
int optind
int optopt
char * optarg
int optreset

Detailed Description

Author:
Andrea Vedaldi

This is a drop-in replacament of GNU getopt_long meant to be used on platforms that do not support such functionality.

Author:
Andrea Vedaldi

Define Documentation

#define no_argument   0

option with no argument

#define optional_argument   2

option with optional argument

#define required_argument   1

option with required argument

Function Documentation

int getopt_long ( int  argc,
char *const  argv[],
const char *  optstring,
const struct option *  longopts,
int *  longindex 
)
Parameters:
argcnumber of arguments.
argvpointer to the vector of arguments.
optstringlist of abbreviated options
longoptslist of long options.
longindexindex of current option in longopts.
Returns:
the code of the next option.

This function extract long and short options from the argument list argv of argc entries.

A short options sequence is introduced by a singe dash character -. Each short option is described by a single character in the string optstring, possibly followed by a : character to denote a (mandatory) argument of the short option. A short option with an argument cannot appear in the middle of a short option sequence, but only at the end.

A long option is introduced by a double dash --. Each long option is described by an instance of the option structure in the longopts table (the last entry must be filled with zeroes to denote the end).

Illegal options and missing arguments cause the function to skip the option and return '?'. If opterr is true (default), the function prints an error message to stderr. Finally, if optstring has a leading :, then error messages are suppressed and a missing argument causes : to be returned.

Remarks:
The function is currently not thread safe.

Variable Documentation

char* optarg

argument of the current option

int opterr

code of the last error occured while parsing an option

int optind

index of the next option to process in argv

int optopt

current option

int optreset

reset flag

 
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Defines | Functions
heap-def.h File Reference

Heap preprocessor metaprogram. More...

#include "host.h"
#include <assert.h>

Defines

#define VL_HEAP_array   VL_HEAP_type*
#define VL_HEAP_prefix   HeapObject
#define VL_HEAP_type   HeapType
#define VL_HEAP_array   HeapType*
#define VL_HEAP_array   HeapType const*

Functions

vl_uindex vl_heap_parent (vl_uindex index)
 Get index of parent node.
vl_uindex vl_heap_left_child (vl_uindex index)
 Get index of left child.
vl_uindex vl_heap_right_child (vl_uindex index)
 Get index of right child.
VL_HEAP_type VL_HEAP_cmp (VL_HEAP_array_const array, vl_uindex indexA, vl_uindex indexB)
 Compare two heap elements.
void VL_HEAP_swap (VL_HEAP_array array, vl_uindex indexA, vl_uindex indexB)
 Swap two heap elements.
void VL_HEAP_up (VL_HEAP_array array, vl_size heapSize, vl_uindex index)
 Heap up operation.
void VL_HEAP_down (VL_HEAP_array array, vl_uindex index)
 Heap down operation.
void VL_HEAP_push (VL_HEAP_array array, vl_size *heapSize)
 Heap push operation.
vl_uindex VL_HEAP_pop (VL_HEAP_array array, vl_size *heapSize)
 Heap pop operation.
void VL_HEAP_update (VL_HEAP_array array, vl_size heapSize, vl_uindex index)
 Heap update operation.

Detailed Description

Author:
Andrea Vedaldi

A heap organizes an array of objects in a priority queue. This module is a template metaprogram that defines heap operations on array of generic objects, or even generic object containers.

Overview

To use heap-def.h one must specify at least a prefix and the data type for the heap elements:

 #define VL_HEAP_prefix  my_heap
 #define VL_HEAP_type    float
 #include <vl/heap-def.h>

This code fragment defines a number of functions prefixed by VL_HEAP_prefix, such as my_heap_push (VL_HEAP_push) and my_heap_pop (VL_HEAP_pop), that implement the heap operations. These functions operate on an array that has type VL_HEAP_array. By default, this is defined to be:

 #define VL_HEAP_array VL_HEAP_type*
 #define VL_HEAP_array_const VL_HEAP_type const*

The array itself is accessed uniquely by means of two functions:

  • VL_HEAP_cmp, that compares two array elements. The default implementation assumes that VL_HEAP_type is numeric.
  • VL_HEAP_swap, that swaps two array elements. The default implementation assumes that VL_HEAP_type can be copied by the = operator.

The heap state is a integer numElements (of type vl_size) counting the number of elements of the array that are currently part of the heap and the content of the first numElements elements of the array. The portion of the array that constitutes the heap satisfies a certain invariant property (heap property, Technical details). From a user viewpoint, the most important consequence is that the first element of the array (the one of index 0) is also the smallest (according to VL_HEAP_cmp).

Elements are added to the heap by VL_HEAP_push and removed from the heap by VL_HEAP_pop. A push operation adds to the heap the array element immediately after the last element already in the heap (i.e. the element of index numElements) and increases the number of heap elements numElements. Elements in the heap are swapped as required in order to maintain the heap consistency. Similarly, a pop operation removes the first (smaller) element from the heap and decreases the number of heap elements numElements.

The values of nodes currently in the heap can be updated by VL_HEAP_update. Notice however that using this function requires knowing the index of the element that needs to be updated up to the swapping operations that the heap performs to maintain consistency. Typically, this requires redefining VL_HEAP_swap to keep track of such changes (General usage).

General usage

The heap container may be mapped to any type by reimplementing VL_HEAP_cmp and VL_HEAP_swap explicitly. For instance the following code redefines VL_HEAP_cmp to deal with the case in which the heap is an array of structures:

 typedef struct _S { int x ; } S ;
 int s_cmp (S const * v, vl_uindex a, vl_uindex b) {
   return v[a].x - v[b].x ;
 }
 #define VL_HEAP_prefix  s_heap
 #define VL_HEAP_type    S
 #define VL_HEAP_cmp     s_cmp
 #include <vl/heap-def.h>

In the following example, the heap itself is an arbitrary structure:

 typedef struct _H { int* array ; } H ;
 int h_cmp (H const * h, vl_uindex a, vl_uindex b) {
   return h->array[a] - h->array[b] ;
 }
 int h_swap (H * h, vl_uindex a, vl_uindex b) {
   int t = h->array[a] ;
   h->array[a] = h->array[b] ;
   h->array[b] = t ;
 }
 #define VL_HEAP_prefix  h_heap
 #define VL_HEAP_swap    h_swap
 #define VL_HEAP_cmp     h_cmp
 #include <vl/heap-def.h>

Technical details

The heap is organised as a binary tree with the property (heap property) that any node is not larger than any of its children. In particular, the root is the smallest node.

heap-def.h uses the standard binary tree representation as a linear array. Tree nodes are mapped to array elements as follows: array[0] corresponds to the root, array[1] and array[2] to the root left and right children and so on. In this way, the tree structure is fully specified by the total number of nodes N.

Assuming that the heap has N nodes (from array[0] to array[N-1]), adding the node array[N] to the heap is done by a push down operation: if the node array[N] is smaller than its parent (violating the heap property) it is pushed down by swapping it with the parent, and so on recursively.

Removing the smallest element array[0] with an heap of N nodes is done by swapping array[0] with array[N-1]. If then array[0] is larger than any of its children, it is swapped with the smallest of the two and so on recursively (push up operation).

Restoring the heap property after an element array[i] has been modified can be done by a push up or push down operation on that node.

Define Documentation

#define VL_HEAP_array   VL_HEAP_type*

Data type of the heap container

Const data type of the heap container

#define VL_HEAP_array   HeapType*

Data type of the heap container

Const data type of the heap container

#define VL_HEAP_array   HeapType const*

Data type of the heap container

Const data type of the heap container

#define VL_HEAP_prefix   HeapObject

Prefix of the heap functions

#define VL_HEAP_type   HeapType

Data type of the heap elements

Function Documentation

VL_HEAP_type VL_HEAP_cmp ( VL_HEAP_array_const  array,
vl_uindex  indexA,
vl_uindex  indexB 
) [inline]
Parameters:
arrayheap array.
indexAindex of the first element A to compare.
indexBindex of the second element B to comapre.
Returns:
a negative number if A<B, 0 if A==B, and a positive number if if A>B.
void VL_HEAP_down ( VL_HEAP_array  array,
vl_uindex  index 
) [inline]
Parameters:
arraypointer to the heap node array.
indexindex of the node to push up.
vl_uindex vl_heap_left_child ( vl_uindex  index) [inline]
Parameters:
indexa node index.
Returns:
index of the left child.
vl_uindex vl_heap_parent ( vl_uindex  index) [inline]
Parameters:
indexa node index.
Returns:
index of the parent node.
vl_uindex VL_HEAP_pop ( VL_HEAP_array  array,
vl_size *  heapSize 
) [inline]
Parameters:
arraypointer to the heap array.
heapSize(in/out) size of the heap.
Returns:
index of the popped element.

The function extracts from the heap the element of index 0 (the smallest element) and decreases heapSize.

The element extracted is moved as the first element after the heap end (thus it has index heapSize). For convenience, this index is returned by the function.

Popping from an empty heap is undefined.

void VL_HEAP_push ( VL_HEAP_array  array,
vl_size *  heapSize 
) [inline]
Parameters:
arraypointer to the heap array.
heapSize(in/out) size of the heap.

The function adds to the heap the element of index heapSize and increments heapSize.

vl_uindex vl_heap_right_child ( vl_uindex  index) [inline]
Parameters:
indexa node index.
Returns:
index of the right child.
void VL_HEAP_swap ( VL_HEAP_array  array,
vl_uindex  indexA,
vl_uindex  indexB 
) [inline]
Parameters:
arrayarray of nodes.
arrayheap array.
indexAindex of the first node to swap.
indexBindex of the second node to swap.

The function swaps the two heap elements a and @ b. The function uses a temporary element and the copy operator, which must be well defined for the heap elements.

void VL_HEAP_up ( VL_HEAP_array  array,
vl_size  heapSize,
vl_uindex  index 
) [inline]
Parameters:
arraypointer to the heap array.
heapSizesize of the heap.
indexindex of the node to push up.
void VL_HEAP_update ( VL_HEAP_array  array,
vl_size  heapSize,
vl_uindex  index 
) [inline]
Parameters:
arraypointer to the heap array.
heapSizesize of the heap.
indexindex of the node to update.

The function updates the heap to account for a change to the element of index index in the heap.

Notice that using this function requires knowing the index of the heap index of element that was changed. Since the heap swaps elements in the array, this is in general different from the index that that element had originally.

 
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Functions
hikmeans.c File Reference

Hierarchical Integer K-Means Clustering - Declaration. More...

#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <math.h>
#include "hikmeans.h"

Functions

vl_uint8 * vl_hikm_copy_subset (vl_uint8 const *data, vl_uint *ids, int N, int M, vl_uint id, int *N2)
 Copy a subset of the data to a buffer.
static VlHIKMNode * xmeans (VlHIKMTree *tree, vl_uint8 const *data, int N, int K, int height)
 Compute HIKM clustering.
static void xdelete (VlHIKMNode *node)
 Delete node.
VlHIKMTree * vl_hikm_new (int method)
 New HIKM tree.
void vl_hikm_delete (VlHIKMTree *f)
 Delete HIKM tree.
void vl_hikm_init (VlHIKMTree *f, int M, int K, int depth)
 Initialize HIKM tree.
void vl_hikm_train (VlHIKMTree *f, vl_uint8 const *data, int N)
 Train HIKM tree.
void vl_hikm_push (VlHIKMTree *f, vl_uint *asgn, vl_uint8 const *data, int N)
 Project data down HIKM tree.

Detailed Description

Author:
Brian Fulkerson
Andrea Vedaldi

Function Documentation

vl_uint8* vl_hikm_copy_subset ( vl_uint8 const *  data,
vl_uint *  ids,
int  N,
int  M,
vl_uint  id,
int *  N2 
)
Parameters:
dataData
idsData labels
NNumber of indices
MData dimensionality
idLabel of data to copy
N2Number of data copied (out)
Returns:
a new buffer with a copy of the selected data.
void vl_hikm_delete ( VlHIKMTree *  f)
Parameters:
fHIKM tree.
void vl_hikm_init ( VlHIKMTree *  f,
int  M,
int  K,
int  depth 
)
Parameters:
fHIKM tree.
MData dimensionality.
KNumber of clusters per node.
depthTree depth.
Remarks:
depth cannot be smaller than 1.
Returns:
a new HIKM tree representing the clustering.
VlHIKMTree* vl_hikm_new ( int  method)
Parameters:
methodclustering method.
Returns:
new HIKM tree.
void vl_hikm_push ( VlHIKMTree *  f,
vl_uint *  asgn,
vl_uint8 const *  data,
int  N 
)
Parameters:
fHIKM tree.
asgnPath down the tree (out).
dataData to project.
NNumber of data.

The function writes to asgn the path of the data data down the HIKM tree f. The parameter asgn must point to an array of M by N elements, where M is the depth of the HIKM tree and N is the number of data point to process.

void vl_hikm_train ( VlHIKMTree *  f,
vl_uint8 const *  data,
int  N 
)
Parameters:
fHIKM tree.
dataData to cluster.
NNumber of data.
static void xdelete ( VlHIKMNode *  node) [static]
Parameters:
nodeto delete.

The function deletes recursively node and all its descendent.

static VlHIKMNode* xmeans ( VlHIKMTree *  tree,
vl_uint8 const *  data,
int  N,
int  K,
int  height 
) [static]
Parameters:
treeHIKM tree to initialize.
dataData to cluster.
NNumber of data points.
KNumber of clusters for this node.
heightTree height.
Remarks:
height cannot be smaller than 1.
Returns:
a new HIKM node representing a sub-clustering.
 
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Data Structures
hikmeans.h File Reference

Hierarchical integer K-Means clustering. More...

#include "generic.h"
#include "ikmeans.h"

Data Structures

struct  VlHIKMNode
 HIKM tree node. More...
struct  VlHIKMTree
 HIKM tree. More...

Functions

Create and destroy
VlHIKMTree * vl_hikm_new (int method)
 New HIKM tree.
void vl_hikm_delete (VlHIKMTree *f)
 Delete HIKM tree.
Retrieve data and parameters
int vl_hikm_get_ndims (VlHIKMTree const *f)
 Get data dimensionality.
int vl_hikm_get_K (VlHIKMTree const *f)
 Get K.
int vl_hikm_get_depth (VlHIKMTree const *f)
 Get depth.
int vl_hikm_get_verbosity (VlHIKMTree const *f)
 Get verbosity level.
int vl_hikm_get_max_niters (VlHIKMTree const *f)
 Get maximum number of iterations.
VlHIKMNode const * vl_hikm_get_root (VlHIKMTree const *f)
 Get maximum number of iterations.
Set parameters
void vl_hikm_set_verbosity (VlHIKMTree *f, int verb)
 Set verbosity level.
void vl_hikm_set_max_niters (VlHIKMTree *f, int max_niters)
 Set maximum number of iterations.
Process data
void vl_hikm_init (VlHIKMTree *f, int M, int K, int depth)
 Initialize HIKM tree.
void vl_hikm_train (VlHIKMTree *f, vl_uint8 const *data, int N)
 Train HIKM tree.
void vl_hikm_push (VlHIKMTree *f, vl_uint *asgn, vl_uint8 const *data, int N)
 Project data down HIKM tree.

Detailed Description

Hierarchical Integer K-Means Clustering.

Hierarchical integer K-Means clustering (HIKM) is a simple hierarchical version of integer K-Means (IKM). The algorithm recursively applies integer K-means to create more refined partitions of the data.

Create a tree with vl_hikm_new() and delete it with vl_hikm_delete(). Use vl_hikm_train() to build the tree from training data and vl_hikm_push() to project new data down a HIKM tree.

HIKM tree

The HIKM tree is represented by a VlHIKMTree structure, which contains a tree composed of VlHIKMNode. Each node is an integer K-means filter which partitions the data into K clusters.

Author:
Brian Fulkerson

Function Documentation

void vl_hikm_delete ( VlHIKMTree *  f)
Parameters:
fHIKM tree.
int vl_hikm_get_depth ( VlHIKMTree const *  f) [inline]
Parameters:
fHIKM tree.
Returns:
depth.
int vl_hikm_get_K ( VlHIKMTree const *  f) [inline]
Parameters:
fHIKM tree.
Returns:
K.
int vl_hikm_get_max_niters ( VlHIKMTree const *  f) [inline]
Parameters:
fHIKM tree.
Returns:
maximum number of iterations.
int vl_hikm_get_ndims ( VlHIKMTree const *  f) [inline]
Parameters:
fHIKM tree.
Returns:
data dimensionality.
VlHIKMNode const * vl_hikm_get_root ( VlHIKMTree const *  f) [inline]
Parameters:
fHIKM tree.
Returns:
maximum number of iterations.
int vl_hikm_get_verbosity ( VlHIKMTree const *  f) [inline]
Parameters:
fHIKM tree.
Returns:
verbosity level.
void vl_hikm_init ( VlHIKMTree *  f,
int  M,
int  K,
int  depth 
)
Parameters:
fHIKM tree.
MData dimensionality.
KNumber of clusters per node.
depthTree depth.
Remarks:
depth cannot be smaller than 1.
Returns:
a new HIKM tree representing the clustering.
VlHIKMTree* vl_hikm_new ( int  method)
Parameters:
methodclustering method.
Returns:
new HIKM tree.
void vl_hikm_push ( VlHIKMTree *  f,
vl_uint *  asgn,
vl_uint8 const *  data,
int  N 
)
Parameters:
fHIKM tree.
asgnPath down the tree (out).
dataData to project.
NNumber of data.

The function writes to asgn the path of the data data down the HIKM tree f. The parameter asgn must point to an array of M by N elements, where M is the depth of the HIKM tree and N is the number of data point to process.

void vl_hikm_set_max_niters ( VlHIKMTree *  f,
int  max_niters 
) [inline]
Parameters:
fHIKM tree.
max_nitersmaximum number of iterations.
void vl_hikm_set_verbosity ( VlHIKMTree *  f,
int  verb 
) [inline]
Parameters:
fHIKM tree.
verbverbosity level.
void vl_hikm_train ( VlHIKMTree *  f,
vl_uint8 const *  data,
int  N 
)
Parameters:
fHIKM tree.
dataData to cluster.
NNumber of data.
 
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Documentation - C API
Homogeneous kernel map
Author:
Andrea Vedaldi

homkermap.h implements the homogeneous kernel maps introduced in [13] , [14] . Such maps are efficient linear representations of popular kernels such as the intersection, $ \chij^2 $, and Jensen-Shannon ones.

Overview

The homogeneous kernel map is a finite dimensional linear approximation of homgeneous kernels, including the intersection, $ \chi^2 $, and Jensen-Shannon kernels. These kernels are ffrequently used in computer vision applications because they are particular suitable for data in the format of histograms, which encompasses many visual descriptors used.

Let $ x,y \in \mathbb{R}_+ $ be non-negative scalars and let $ k(x,y) \in \mathbb{R} $ be an homogeneous kernel such as the $ \chi^2 $ and or the intersection ones:

\[ k_{\mathrm{inters}}(x,y) = \min\{x, y\}, \quad k_{\chi^2}(x,y) = 2 \frac{(x - y)^2}{x+y}. \]

For vectorial data $ \mathbf{x},\mathbf{y} \in \mathbb{R}_+^d $, the homogeneous kernels in an additive combination $ K(\mathbf{x},\mathbf{y}) = \sum_{i=1}^d k(x_i,y_i) $.

The homogeneous kernel map of order $ n $ is a vectorial function $ \Psi(x) \in \mathbb{R}^{2n+1} $ such that, for any choice of $ x, y \in \mathbb{R}_+ $, one has

\[ k(x,y) \approx \langle \Psi(x), \Psi(y) \rangle. \]

Given the feature map for the scalar case, the corresponding feature map $ \Psi(\mathbf{x}) $ for the vectorial case is obtained by stacking $ [\Psi(x_1), \dots, \Psi(x_n)] $. Note that the combined feature $ \Psi(\mathbf{x}) $ has dimension $ d(2n+1) $.

Using linear analysis tools (e.g. a linear support vector machine) on top of dataset that has been encoded by the homogeneous kernel map is therefore approximately equivalent to using a method based on the corresponding non-linear kernel.

Extension to the negative reals

Any positive (semi-)definite kernel $ k(x,y) $ defined on the non-negative reals $ x,y \in \mathbb{R}_+ $ can be extended to the entiere real line by using the definition:

\[ k_\pm(x,y) = \operatorname{sign}(x) \operatorname{sign}(y) k(|x|,|y|). \]

The homogeneous kernel map implements this extension by defining $ \Psi_\pm(x) = \operatorname{sign}(x) \Psi(|x|) $. Note that other extensions are possible, such as

\[ k_\pm(x,y) = H(xy) \operatorname{sign}(y) k(|x|,|y|) \]

where $ H $ is the Heavyside function, but may require higher dimensional feature maps.

Homogeneity order

Any (1-)homogeneous kernel $ k_1(x,y) $ can be extended to a so called gamma-homgeneous kernel $ k_\gamma(x,y) $ by the definition

\[ k_\gamma(x,y) = (xy)^{\frac{\gamma}{2}} \frac{k_1(x,y)}{\sqrt{xy}} \]

Smaller value of $ \gamma $ enhance the kernel non-linearity and are sometimes beneficial in applications (see [1,2] for details).

Windowing and period

This section discusses aspects of the homogeneous kernel map which are more technical and may be skipped. The homogeneous kernel map approximation is based on periodicizing the kernel; given the kernel signature

\[ \Kappa(\lambda) = k(e^{\frac{\lambda}{2}}, e^{-\frac{\lambda}{2}}) \]

the homogeneous kerne map is a feature map for the windowed and periodicized kernel whose signature is given by

\[ \hat{\mathcal{K}}(\lambda) = \sum_{i=-\infty}^{+\infty} \mathcal{K}(\lambda + k \Lambda) W(\lambda + k \Lambda) \]

where $ W(\lambda) $ is a windowing function and $ \Lambda $ is the period. This implementation of the homogeneous kernel map supports the use of a uniform window ( $ W(\lambda) = 1 $) or of a rectangular window ( $ W(\lambda) = \operatorname{rect}(\lambda/\Lambda) $). Note that $ \lambda = \log(y/x) $ is equal to the logarithmic ratio of the arguments of the kernel. Empirically, the rectangular window seems to have a slight edge in applications.

Usage

The homogeneous kernel map is implemented as an object of type VlHomogeneousKernelMap. To use thois object, first create an instance by vl_homogeneouskernelmap_new, then use vl_homogeneouskernelmap_evaluate_d or vl_homogeneouskernelmap_evaluate_f (depdening on whether the data is double or float) to compute the feature map $ \Psi(x) $. When done, dispose of the object by calling vl_homogeneouskernelmap_delete.

The constructor vl_homogeneouskernelmap_new requires the kernel type kernel (see VlHomogeneousKernelType), the homogeneity order gamma (use one for the standard kernels), the approximation order order (usually order one is enough), the period period (use a negative value to use the default period), and a window type window (use VlHomogeneousKernelMapWindowRectangular if unsure). The approximation order trades off the quality and dimensionality of the approximation. The resulting feature map $ \Psi(x) $, computed by vl_homogeneouskernelmap_evaluate_d or vl_homogeneouskernelmap_evaluate_f , is 2*order+1 dimensional.

The code pre-computes the map $ \Psi(x) $ for efficient evaluation. The table spans values of $ x $ in the range $[2^{-20}, 2^{8}) $. In particular, values smaller than $ 2^{-20} $ are treated as zeroes (which result in a null feature).

Technical details

The code uses the expressions given in [13] , [14] to compute in closed form the maps $ \Psi(x) $ for the suppoerted kernel types. For efficiency reasons, it tabulates $ \Psi(x) $ when the homogeneous kernel map object is created.

The interal table stores $ \Psi(x) \in \mathbb{R}^{2n+1} $ for $ x \geq 0 $ by sampling this variable. In particular, x is decomposed as 

  x = mantissa * (2**exponent),
  minExponent <= exponent <= maxExponent,
  1 <= matnissa < 2.

Each octave is further subdivided in numSubdivisions sublevels.

When the map $ \Psi(x) $ is evaluated, x is decomposed back into exponent and mantissa, and the result is computed by bilinear interpolation from the appropriate table entries.

 
vlfeat-0.9.14/doc/api/homkermap_8c.html000066400000000000000000000330261167540017400176600ustar00rootroot00000000000000 VLFeat - Documentation - C API
Documentation - C API
Functions
homkermap.c File Reference

Homogeneous kernel map - Definition. More...

#include "homkermap.h"
#include "mathop.h"
#include <math.h>
#include "homkermap.c"

Functions

double vl_homogeneouskernelmap_get_spectrum (VlHomogeneousKernelMap const *self, double omega)
 Sample the kernel specturm.
double vl_homogeneouskernelmap_get_smooth_spectrum (VlHomogeneousKernelMap const *self, double omega)
 Sample the smoothed kernel spectrum.
VlHomogeneousKernelMap * vl_homogeneouskernelmap_new (VlHomogeneousKernelType kernelType, double gamma, vl_size order, double period, VlHomogeneousKernelMapWindowType windowType)
 Create a new homgeneous kernel map.
void vl_homogeneouskernelmap_delete (VlHomogeneousKernelMap *self)
 Delete a map object.

Detailed Description

Author:
Andrea Vedaldi

Function Documentation

void vl_homogeneouskernelmap_delete ( VlHomogeneousKernelMap *  self)
Parameters:
selfmap object. The function deletes the specified map object.
double vl_homogeneouskernelmap_get_smooth_spectrum ( VlHomogeneousKernelMap const *  self,
double  omega 
) [inline]
Parameters:
selfhomogeneous kernel map.
omegasampling frequency.
Returns:
the spectrum sampled at omega after smoothing.
double vl_homogeneouskernelmap_get_spectrum ( VlHomogeneousKernelMap const *  self,
double  omega 
) [inline]
Parameters:
selfhomogeneous kernel map.
omegasampling frequency.
Returns:
the spectrum sampled at omega.
VlHomogeneousKernelMap* vl_homogeneouskernelmap_new ( VlHomogeneousKernelType  kernelType,
double  gamma,
vl_size  order,
double  period,
VlHomogeneousKernelMapWindowType  windowType 
)
Parameters:
kernelTypetype of homogeneous kernel.
gammakernel homogeneity degree.
orderapproximation order.
periodkernel period.
windowTypetype of window used to truncate the kernel.
Returns:
the new homogeneous kernel map.

The function intializes a new homogeneous kernel map for the specified kernel type, homogeneity degree, approximation order, period, and truncation window. See Overview for details.

The homogeneity degree gamma must be positive (the standard kernels are obtained by setting gamma to 1). When unsure, set windowType to VlHomogeneousKernelMapWindowRectangular. The period should be non-negative; specifying a negative or null value causes the function to switch to a default value.

The function returns NULL if there is not enough free memory.

 
vlfeat-0.9.14/doc/api/homkermap_8h.html000066400000000000000000000476631167540017400177010ustar00rootroot00000000000000 VLFeat - Documentation - C API
Documentation - C API
Data Structures | Enumerations | Functions
homkermap.h File Reference

Homogeneous kernel map (Homogeneous kernel map) More...

#include "generic.h"
#include <math.h>

Data Structures

struct  VlHomogeneousKernelMap
 Homogeneous kernel map. More...

Enumerations

enum  VlHomogeneousKernelType { VlHomogeneousKernelIntersection = 0, VlHomogeneousKernelChi2, VlHomogeneousKernelJS }
 Type of kernel. More...
enum  VlHomogeneousKernelMapWindowType { VlHomogeneousKernelMapWindowUniform = 0, VlHomogeneousKernelMapWindowRectangular = 1 }
 Type of spectral windowing function. More...

Functions

VlHomogeneousKernelMap * vl_homogeneouskernelmap_new (VlHomogeneousKernelType kernelType, double gamma, vl_size order, double period, VlHomogeneousKernelMapWindowType windowType)
 Create a new homgeneous kernel map.
void vl_homogeneouskernelmap_delete (VlHomogeneousKernelMap *self)
 Delete a map object.
void vl_homogeneouskernelmap_evaluate_d (VlHomogeneousKernelMap const *self, double *destination, vl_size stride, double x)
 Evaluate map.
void vl_homogeneouskernelmap_evaluate_f (VlHomogeneousKernelMap const *self, float *destination, vl_size stride, double x)
 Evaluate map.

Detailed Description

Author:
Andrea Vedaldi

Enumeration Type Documentation

enum VlHomogeneousKernelMapWindowType
Enumerator:
VlHomogeneousKernelMapWindowUniform 

uniform window

VlHomogeneousKernelMapWindowRectangular 

rectangular window

enum VlHomogeneousKernelType
Enumerator:
VlHomogeneousKernelIntersection 

intersection kernel

VlHomogeneousKernelChi2 

Chi2 kernel

VlHomogeneousKernelJS 

Jensen-Shannon kernel

Function Documentation

void vl_homogeneouskernelmap_delete ( VlHomogeneousKernelMap *  self)
Parameters:
selfmap object. The function deletes the specified map object.
vl_homogeneouskernelmap_evaluate_d ( VlHomogeneousKernelMap const *  self,
double *  destination,
vl_size  stride,
double  x 
)
Parameters:
selfmap object.
destinationoutput buffer.
stridestride of the output buffer.
xvalue to expand.

The function evaluates the feature map on x and stores the resulting 2*order+1 dimensional vector to destination[0], destination[stride], destination[2*stride], ....

vl_homogeneouskernelmap_evaluate_f ( VlHomogeneousKernelMap const *  self,
float *  destination,
vl_size  stride,
double  x 
)
Parameters:
selfmap object.
destinationoutput buffer.
stridestride of the output buffer.
xvalue to expand.

The function evaluates the feature map on x and stores the resulting 2*order+1 dimensional vector to destination[0], destination[stride], destination[2*stride], ....

VlHomogeneousKernelMap* vl_homogeneouskernelmap_new ( VlHomogeneousKernelType  kernelType,
double  gamma,
vl_size  order,
double  period,
VlHomogeneousKernelMapWindowType  windowType 
)
Parameters:
kernelTypetype of homogeneous kernel.
gammakernel homogeneity degree.
orderapproximation order.
periodkernel period.
windowTypetype of window used to truncate the kernel.
Returns:
the new homogeneous kernel map.

The function intializes a new homogeneous kernel map for the specified kernel type, homogeneity degree, approximation order, period, and truncation window. See Overview for details.

The homogeneity degree gamma must be positive (the standard kernels are obtained by setting gamma to 1). When unsure, set windowType to VlHomogeneousKernelMapWindowRectangular. The period should be non-negative; specifying a negative or null value causes the function to switch to a default value.

The function returns NULL if there is not enough free memory.

 
vlfeat-0.9.14/doc/api/host_8c.html000066400000000000000000000151161167540017400166520ustar00rootroot00000000000000 VLFeat - Documentation - C API
Documentation - C API
Defines | Functions
host.c File Reference

Host - Definition. More...

#include "host.h"
#include "generic.h"
#include <stdio.h>
#include <intrin.h>

Defines

#define HAS_CPUID

Functions

char * vl_static_configuration_to_string_copy ()
 Human readable static library configuration.

Detailed Description

Author:
Andrea Vedaldi

Define Documentation

#define HAS_CPUID

---------------------------------------------------------------

Function Documentation

char* vl_static_configuration_to_string_copy ( )
Returns:
a new string with the static configuration.

The string includes information about the compiler, the host, and other static configuration parameters. The string must be released by vl_free.

 
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Defines | Functions
host.h File Reference

Host. More...

Defines

#define VL_YESNO(x)   ((x)?"yes":"no")
 Convert a boolean to "yes" or "no" strings.
Configuration options
#define VL_DISABLE_THREADS
 Defined if multi-threading support is disabled.
#define VL_DISABLE_SSE2
 Defined if SSE2 support if disabled.
Defining functions
#define VL_EXPORT
 Declares a DLL exported symbol.
#define VL_INLINE
 Adds appropriate inline function qualifier.
C preprocessor helper macros
#define VL_STRINGIFY(x)   # x
 Convert the argument to a string.
#define VL_XSTRINGIFY(x)   VL_STRINGIFY(x)
 Expand and then convert the argument to a string.
#define VL_CAT(x, y)   x ## y
 Concatenate two arguments into a lexical unit.
#define VL_XCAT(x, y)   VL_CAT(x,y)
 Expand and then concatenate two arguments into a lexical unit.
#define VL_XCAT3(x, y, z)   VL_XCAT(VL_XCAT(x,y),z)
 Expand and then concatenate three arguments into a lexical unit.
#define VL_XCAT4(x, y, z, u)   VL_XCAT(VL_XCAT3(x,y,z),u)
 Expand and then concatenate four arguments into a lexical unit.
#define VL_XCAT5(x, y, z, u, v)   VL_XCAT(VL_XCAT4(x,y,z,u),v)
 Expand and then concatenate five arguments into a lexical unit.
Identifying the host operating system
#define VL_OS_LINUX   1
 Defined if the host operating system is Linux.
#define VL_THREADS_POSIX   1
#define VL_OS_MACOSX   1
 Defined if the host operating system is Mac OS X.
#define VL_THREADS_POSIX   1
#define VL_OS_WIN   1
 Defined if the host operating system is Windows (32 or 64)
#define VL_THREADS_WIN   1
#define VL_OS_WIN64   1
 Defined if the host operating system is Windows-64.
#define VL_THREADS_WIN   1
Identifying the host compiler
#define VL_COMPILER_GNUC
 Defined if the host compiler is GNU C.
#define VL_COMPILER_MSC   _MSC_VER
 Defined if the host compiler is Microsoft Visual C++.
#define VL_COMPILER_LCC   1
Identifying the host CPU architecture
#define VL_ARCH_IX86   300
 Defined if the host CPU is of the Intel x86 family.
#define VL_ARCH_IA64
 Defined if the host CPU is of the Intel Architecture-64 family.
Identifying the host data model
#define VL_COMPILER_LLP64
 Defined if the host compiler data model is LLP64.
#define VL_COMPILER_LP64
 Defined if the host compiler data model is LP64.
#define VL_COMPILER_ILP32
 Defined if the host compiler data model is ILP32.
Identifying the host endianness
#define VL_ARCH_LITTLE_ENDIAN
 Defined if the host CPU is little endian.
#define VL_ARCH_BIG_ENDIAN
 Defined if the host CPU is big endian.
Printing the atomic data types
#define VL_FL_INT64   "I64"
 printf length flag for vl_int64 and vl_uint64.
#define VL_FL_INT32   ""
 printf length flag for vl_int32 and vl_uint32.
#define VL_FL_INT16   "h"
 printf length flag for vl_int16 and vl_uint16.
#define VL_FL_INT8   "hh"
 printf length flag for vl_int8 and vl_uint8.
#define VL_FL_INDEX   VL_FL_INT64
 printf length flag for vl_index and vl_uindex
#define VL_FL_INDEX   VL_FL_INT32
 printf length flag for vl_index and vl_uindex
#define VL_FMT_INDEX   VL_FL_INDEX "d"
 printf flag for vl_index
#define VL_FMT_INTPTR   VL_FMT_INDEX
 printf flag for vl_intptr
#define VL_FMT_UINDEX   VL_FL_INDEX "u"
 printf flag for vl_uindex
#define VL_FMT_SIZE   VL_FMT_UINDEX
 printf flag for vl_size
#define VL_FMT_UINTPTR   VL_FMT_UINDEX
 printf flag for vl_uintptr
Atomic data types limits
#define VL_BIG_INT   0x7FFFFFFFL
 Largest integer (math constant)
#define VL_SMALL_INT   (- VL_BIG_INT - 1)
 Smallest integer (math constant)
#define VL_BIG_UINT   0xFFFFFFFFUL
 Largest unsigned integer (math constant)

Functions

char * vl_static_configuration_to_string_copy ()
 Human readable static library configuration.
Endianness detection and conversion
void vl_swap_host_big_endianness_8 (void *dst, void *src)
 Host <-> big endian transformation for 8-bytes value.
void vl_swap_host_big_endianness_4 (void *dst, void *src)
 Host <-> big endian transformation for 4-bytes value.
void vl_swap_host_big_endianness_2 (void *dst, void *src)
 Host <-> big endian transformation for 2-bytes value.
Obtaining host info at run time
void _vl_x86cpu_info_init (VlX86CpuInfo *self)
char * _vl_x86cpu_info_to_string_copy (VlX86CpuInfo const *self)

Atomic data types

#define VL_TRUE   1
 true (1) constant
#define VL_FALSE   0
 false (0) constant
typedef long long vl_int64
 Signed 64-bit integer.
typedef int vl_int32
 Signed 32-bit integer.
typedef short vl_int16
 Signed 16-bit integer.
typedef char vl_int8
 Signed 8-bit integer.
typedef long long unsigned vl_uint64
 Unsigned 64-bit integer.
typedef int unsigned vl_uint32
 Unsigned 32-bit integer.
typedef short unsigned vl_uint16
 Unsigned 16-bit integer.
typedef char unsigned vl_uint8
 Unsigned 8-bit integer.
typedef int vl_int
 Same as int.
typedef unsigned int vl_uint
 Same as unsigned int.
typedef int vl_bool
 Boolean.
typedef vl_int64 vl_intptr
 Integer holding a pointer.
typedef vl_uint64 vl_uintptr
 Unsigned integer holding a pointer.
typedef vl_uint64 vl_size
 Unsigned integer holding the size of a memory block.
typedef vl_int64 vl_index
 Signed version of vl_size and vl_uindex.
typedef vl_uint64 vl_uindex
 Same as vl_size.

Detailed Description

This module provides functionalities to identify the host operating system, C compiler, and CPU architecture. It also provides a few features to abstract from such details.

See also:
http://predef.sourceforge.net/index.php

Host operating system

The module defines a symbol to identify the host operating system: VL_OS_WIN for Windows, VL_OS_LINUX for Linux, VL_OS_MACOSX for Mac OS X, and so on.

Host compiler

The module defines a symbol to identify the host compiler: VL_COMPILER_MSC for Microsoft Visual C++, VL_COMPILER_GNUC for GNU C, and so on. The (integer) value of such symbols corresponds the version of the compiler.

The module defines a symbol to identify the data model of the compiler: VL_COMPILER_ILP32, VL_COMPILER_LP64, or VL_COMPILER_LLP64 (see Sect. Data models). For convenience, it also defines a number of atomic types of prescribed width (vl_int8, vl_int16, vl_int32, etc.).

Remarks:
While some of such functionalities are provided by the standard header stdint.h, the latter is not supported by all platforms.

Data models

The C language defines a number of atomic data types (such as char, short, int and so on). The number of bits (width) used to represent each data type depends on the compiler data model. The following table summarizes the relevant conventions:

Compiler data models. The table shows how many bits are allocated to each atomic data type according to each model.
Data model short int long long long void* Compiler
ILP32 16 32 32 64 32 common 32 bit architectures
LP64 16 32 64 64 64 UNIX-64 (Linux, Mac OS X)
ILP64 16 64 64 64 64 Alpha, Cray
SLIP64 64 64 64 64 64
LLP64 16 32 32 64 64 Windows-64

Other compiler-specific features

The module provides the macro VL_EXPORT to declare symbols exported from the library and the macro VL_INLINE to declare inline functions. Such features are not part of the C89 standard, and change depending on the compiler.

Example:
The following header file declares a function f that should be visible from outside the library. 
 #include <vl/generic.h>
 VL_EXPORT void f () ;
 VL_EXPORT int i ;
Notice that the macro VL_EXPORT needs not to be included again when the function is defined.
Example:
The following header file declares an inline function f: 
 #include <vl/generic.h>
 VL_INLINE int f() ;

 VL_INLINE int f() { return 1 ; }

Here the first instruction defines the function f, where the second declares it. Notice that since this is an inline function, its definition must be found in the header file rather than in an implementation file. Notice also that definition and declaration can be merged.

These macros translate according to the following tables:

Macros for exporting library symbols
Platform Macro name Value when building the library Value when importing the library
Unix/GCC VL_EXPORT empty (assumes -visibility=hidden GCC option) __attribute__((visibility ("default")))
Win/Visual C++ VL_EXPORT __declspec(dllexport) __declspec(dllimport)
Macros for declaring inline functions
Platform Macro name Value
Unix/GCC VL_INLINE static inline
Win/Visual C++ VL_INLINE static __inline

Host CPU architecture

The module defines a symbol to identify the host CPU architecture: VL_ARCH_IX86 for Intel x86, VL_ARCH_IA64 for Intel 64, and so on.

Endianness

The module defines a symbol to identify the host CPU endianness: VL_ARCH_BIG_ENDIAN for big endian and VL_ARCH_LITTLE_ENDIAN for little endian. The functions vl_swap_host_big_endianness_8(), vl_swap_host_big_endianness_4(), vl_swap_host_big_endianness_2() to change the endianness of data (from/to host and network order).

Recall that endianness concerns the way multi-byte data types (such as 16, 32 and 64 bits integers) are stored into the addressable memory. All CPUs uses a contiguous address range to store atomic data types (e.g. a 16-bit integer could be assigned to the addresses 0x10001 and 0x10002), but the order may differ.

  • The convention is big endian, or in network order, if the most significant byte of the multi-byte data types is assigned to the smaller memory address. This is the convention used for instance by the PPC architecture.
  • The convention is little endian if the least significant byte is assigned to the smaller memory address. This is the convention used for instance by the x86 architecture.
Remarks:
The names “big endian” and “little endian” are a little confusing. “Big endian” means “big endian first”, i.e. the address of the most significant byte comes first. Similarly, “little endian” means “little endian first”, in the sense that the address of the least significant byte comes first.

Endianness is a concern when data is either exchanged with processors that use different conventions, transmitted over a network, or stored to a file. For the latter two cases, one usually saves data in big endian (network) order regardless of the host CPU.

host-threads

The file defines #VL_THREADS_WIN if multi-threading support is enabled and the host supports Windows threads and #VL_THREADS_POSIX if it supports POSIX threads.

Author:
Andrea Vedaldi

Define Documentation

#define VL_ARCH_BIG_ENDIAN
See also:
Endianness
#define VL_ARCH_IA64
See also:
Host CPU architecture
#define VL_ARCH_IX86   300
See also:
Host CPU architecture
#define VL_ARCH_LITTLE_ENDIAN
See also:
Endianness
#define VL_CAT (   x,
  y 
)    x ## y
Parameters:
xfirst argument to be concatenated.
ysecond argument to be concatenated.

This macro concatenates its arguments into a single lexical unit by means of the ## preprocessor operator. Notice that arguments concatenated by ## are not pre-expanded by the C preprocessor. To macro-expand the arguments and then concatenate them,use VL_XCAT.

See also:
VL_XCAT
#define VL_COMPILER_GNUC
Value:
(__GNUC__ * 10000 \
+ __GNUC_MINOR__ * 100)

This macro is defined if the compiler is GNUC. Its value is calculated as 

 10000 * MAJOR + 100 * MINOR + PATCHLEVEL
See also:
Host compiler
#define VL_COMPILER_ILP32
See also:
Data models
#define VL_COMPILER_LLP64
See also:
Data models
#define VL_COMPILER_LP64
See also:
Data models
#define VL_COMPILER_MSC   _MSC_VER
See also:
Host compiler
#define VL_DISABLE_SSE2

Define this symbol to disable SSE2 support.

#define VL_DISABLE_THREADS

Define this symbol to disable multi-threading support.

#define VL_EXPORT
See also:
Other compiler-specific features
#define VL_INLINE
See also:
Other compiler-specific features
#define VL_STRINGIFY (   x)    # x
Parameters:
xvalue to be stringified.

This macro stringifies the argument x by means of the # prerpocessor operator.

The standard C preprocessor does not prescan arguments which are stringified, so

 #define A B
 char const * str = VL_STRINGIFY(A) ;

initializes str with a pointer to the string "A", which mihgt be unexpected. To fix this issue, you can use VL_XSTRINGIFY.

See also:
VL_XSTRINGIFY
#define VL_XCAT (   x,
  y 
)    VL_CAT(x,y)
Parameters:
xfirst argument to be concatenated.
ysecond argument to be concatenated.

This macro is the same as VL_CAT, except that the arguments are macro expanded before being concatenated.

See also:
VL_CAT
#define VL_XCAT3 (   x,
  y,
  z 
)    VL_XCAT(VL_XCAT(x,y),z)
Parameters:
xfirst argument to be concatenated.
ysecond argument to be concatenated.
zthird argument to be concatenated.

This macro is the same as VL_XCAT, except that it has three arguments.

See also:
VL_XCAT
#define VL_XCAT4 (   x,
  y,
  z,
  u 
)    VL_XCAT(VL_XCAT3(x,y,z),u)
Parameters:
xfirst argument to be concatenated.
ysecond argument to be concatenated.
zthird argument to be concatenated.
ufourth argument to be concatenated.

This macro is the same as VL_XCAT, except that it has four arguments.

See also:
VL_XCAT
#define VL_XCAT5 (   x,
  y,
  z,
  u,
  v 
)    VL_XCAT(VL_XCAT4(x,y,z,u),v)
Parameters:
xfirst argument to be concatenated.
ysecond argument to be concatenated.
zthird argument to be concatenated.
ufourth argument to be concatenated.
vfifth argument to be concatenated.

This macro is the same as VL_XCAT, except that it has five arguments.

See also:
VL_XCAT
#define VL_XSTRINGIFY (   x)    VL_STRINGIFY(x)
Parameters:
xvalue to be macro-expanded and converted.

This macro macro-expands the argument x and stringifies the result of the expansion. For instance

 #define A B
 char const * str = VL_STRINGIFY(A) ;

initializes str with a pointer to the string "B".

See also:
VL_STRINGIFY
#define VL_YESNO (   x)    ((x)?"yes":"no")
Parameters:
xboolean to convert. A pointer to either the string "yes" (if x is true) or the string "no".
Example
 VL_PRINTF("Is x true? %s.", VL_YESNO(x))

Function Documentation

char* vl_static_configuration_to_string_copy ( )
Returns:
a new string with the static configuration.

The string includes information about the compiler, the host, and other static configuration parameters. The string must be released by vl_free.

void vl_swap_host_big_endianness_2 ( void *  dst,
void *  src 
) [inline]
Parameters:
dstdestination 2-byte buffer.
srcsource 2-byte bufffer.
See also:
Endianness.
void vl_swap_host_big_endianness_4 ( void *  dst,
void *  src 
) [inline]
Parameters:
dstdestination 4-byte buffer.
srcsource 4-byte bufffer.
See also:
Endianness.
void vl_swap_host_big_endianness_8 ( void *  dst,
void *  src 
) [inline]
Parameters:
dstdestination 8-byte buffer.
srcsource 8-byte bufffer.
See also:
Endianness.
 
vlfeat-0.9.14/doc/api/ikmeans_8c.html000066400000000000000000000352721167540017400173310ustar00rootroot00000000000000 VLFeat - Documentation - C API
Documentation - C API
Functions
ikmeans.c File Reference

Integer K-Means clustering - Definition. More...

#include "ikmeans.h"
#include <stdlib.h>
#include <stdio.h>
#include <string.h>
#include "assert.h"
#include "ikmeans_init.tc"
#include "ikmeans_lloyd.tc"
#include "ikmeans_elkan.tc"

Functions

VlIKMFilt * vl_ikm_new (int method)
 Create a new IKM quantizer.
void vl_ikm_delete (VlIKMFilt *f)
 Delete IKM quantizer.
int vl_ikm_train (VlIKMFilt *f, vl_uint8 const *data, int N)
 Train clusters.
void vl_ikm_push (VlIKMFilt *f, vl_uint *asgn, vl_uint8 const *data, int N)
 Project data to clusters.
vl_uint vl_ikm_push_one (vl_ikm_acc const *centers, vl_uint8 const *data, int M, int K)
 Project one datum to clusters.

Detailed Description

Author:
Brian Fulkerson
Andrea Vedaldi

Function Documentation

void vl_ikm_delete ( VlIKMFilt *  f)
Parameters:
fIKM quantizer.
VlIKMFilt* vl_ikm_new ( int  method)
Parameters:
methodClustering algorithm.

The function allocates initializes a new IKM quantizer to operate based algorithm method.

method has values in the enumerations VlIKMAlgorithms.

Returns:
new IKM quantizer.
void vl_ikm_push ( VlIKMFilt *  f,
vl_uint *  asgn,
vl_uint8 const *  data,
int  N 
)
Parameters:
fIKM quantizer.
asgnAssignments (out).
datadata.
Nnumber of data (N >= 1).

The function projects the data data on the integer K-means clusters specified by the IKM quantizer f. Notice that the quantizer must be initialized.

vl_uint vl_ikm_push_one ( vl_ikm_acc const *  centers,
vl_uint8 const *  data,
int  M,
int  K 
)
Parameters:
centerscenters.
datadatum to project.
Knumber of centers.
Mdimensionality of the datum.

The function projects the specified datum data on the clusters specified by the centers centers.

Returns:
the cluster index.
int vl_ikm_train ( VlIKMFilt *  f,
vl_uint8 const *  data,
int  N 
)
Parameters:
fIKM quantizer.
datadata.
Nnumber of data (N >= 1).
Returns:
-1 if an overflow may have occurred.
 
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Data Structures | Typedefs | Enumerations
ikmeans.h File Reference

Integer K-Means clustering. More...

#include "generic.h"
#include "random.h"

Data Structures

struct  VlIKMFilt
 IKM quantizer. More...

Typedefs

typedef vl_int32 vl_ikm_acc

Enumerations

enum  VlIKMAlgorithms { VL_IKM_LLOYD, VL_IKM_ELKAN }
 IKM algorithms. More...

Functions

Create and destroy
VlIKMFilt * vl_ikm_new (int method)
 Create a new IKM quantizer.
void vl_ikm_delete (VlIKMFilt *f)
 Delete IKM quantizer.
Process data
void vl_ikm_init (VlIKMFilt *f, vl_ikm_acc const *centers, int M, int K)
 Initialize quantizer with centers.
void vl_ikm_init_rand (VlIKMFilt *f, int M, int K)
 Initialize quantizer with random centers.
void vl_ikm_init_rand_data (VlIKMFilt *f, vl_uint8 const *data, int M, int N, int K)
 Initialize with centers from random data.
int vl_ikm_train (VlIKMFilt *f, vl_uint8 const *data, int N)
 Train clusters.
void vl_ikm_push (VlIKMFilt *f, vl_uint *asgn, vl_uint8 const *data, int N)
 Project data to clusters.
vl_uint vl_ikm_push_one (vl_ikm_acc const *centers, vl_uint8 const *data, int M, int K)
 Project one datum to clusters.
Retrieve data and parameters
int vl_ikm_get_ndims (VlIKMFilt const *f)
 Get data dimensionality.
int vl_ikm_get_K (VlIKMFilt const *f)
 Get the number of centers K.
int vl_ikm_get_verbosity (VlIKMFilt const *f)
 Get verbosity level.
int vl_ikm_get_max_niters (VlIKMFilt const *f)
 Get maximum number of iterations.
vl_ikm_acc const * vl_ikm_get_centers (VlIKMFilt const *f)
 Get maximum number of iterations.
Set parameters
void vl_ikm_set_verbosity (VlIKMFilt *f, int verb)
 Set verbosity level.
void vl_ikm_set_max_niters (VlIKMFilt *f, int max_niters)
 Set maximum number of iterations.

Detailed Description

Integer K-means (IKM) is an implementation of K-means clustering (or Vector Quantization, VQ) for integer data. This is particularly useful for clustering large collections of visual descriptors.

Use the function vl_ikm_new() to create a IKM quantizer. Initialize the IKM quantizer with K clusters by vl_ikm_init() or similar function. Use vl_ikm_train() to train the quantizer. Use vl_ikm_push() or vl_ikm_push_one() to quantize new data.

Given data $x_1,\dots,x_N\in R^d$ and a number of clusters $K$, the goal is to find assignments $a_i\in\{1,\dots,K\},$ and centers $c_1,\dots,c_K\in R^d$ so that the expected distortion

\[ E(\{a_{i}, c_j\}) = \frac{1}{N} \sum_{i=1}^N d(x_i, c_{a_i}) \]

is minimized. Here $d(x_i, c_{a_i})$ is the distortion, i.e. the cost we pay for representing $ x_i $ by $ c_{a_i} $. IKM uses the squared distortion $d(x,y)=\|x-y\|^2_2$.

Algorithms

Initialization

Most K-means algorithms are iterative and needs an initialization in the form of an initial choice of the centers $c_1,\dots,c_K$. We include the following options:

Lloyd

The Lloyd (also known as Lloyd-Max and LBG) algorithm iteratively:

  • Fixes the centers, optimizing the assignments (minimizing by exhaustive search the association of each data point to the centers);
  • Fixes the assignments and optimizes the centers (by descending the distortion error function). For the squared distortion, this step is in closed form.

This algorithm is not particularly efficient because all data points need to be compared to all centers, for a complexity $O(dNKT)$, where T is the total number of iterations.

Elkan

The Elkan algorithm is an optimized variant of Lloyd. By making use of the triangle inequality, many comparisons of data points and centers are avoided, especially at later iterations. Usually 4-5 times less comparisons than Lloyd are preformed, providing a dramatic speedup in the execution time.

Author:
Brian Fulkerson
Andrea Vedaldi

Typedef Documentation

typedef vl_int32 vl_ikm_acc

IKM accumulator data type

Enumeration Type Documentation

enum VlIKMAlgorithms
Enumerator:
VL_IKM_LLOYD 

Lloyd algorithm

VL_IKM_ELKAN 

Elkan algorithm

Function Documentation

void vl_ikm_delete ( VlIKMFilt *  f)
Parameters:
fIKM quantizer.
vl_ikm_acc const * vl_ikm_get_centers ( VlIKMFilt const *  f) [inline]
Parameters:
fIKM filter.
Returns:
maximum number of iterations.
int vl_ikm_get_K ( VlIKMFilt const *  f) [inline]
Parameters:
fIKM filter.
Returns:
number of centers K.
int vl_ikm_get_max_niters ( VlIKMFilt const *  f) [inline]
Parameters:
fIKM filter.
Returns:
maximum number of iterations.
int vl_ikm_get_ndims ( VlIKMFilt const *  f) [inline]
Parameters:
fIKM filter.
Returns:
data dimensionality.
int vl_ikm_get_verbosity ( VlIKMFilt const *  f) [inline]
Parameters:
fIKM filter.
Returns:
verbosity level.
void vl_ikm_init ( VlIKMFilt *  f,
vl_ikm_acc const *  centers,
int  M,
int  K 
)
Parameters:
fIKM quantizer.
centerscenters.
Mdata dimensionality.
Knumber of clusters.
void vl_ikm_init_rand ( VlIKMFilt *  f,
int  M,
int  K 
)
Parameters:
fIKM quantizer.
Mdata dimensionality.
Knumber of clusters.
void vl_ikm_init_rand_data ( VlIKMFilt *  f,
vl_uint8 const *  data,
int  M,
int  N,
int  K 
)
Parameters:
fIKM quantizer.
datadata.
Mdata dimensionality.
Nnumber of data.
Knumber of clusters.
VlIKMFilt* vl_ikm_new ( int  method)
Parameters:
methodClustering algorithm.

The function allocates initializes a new IKM quantizer to operate based algorithm method.

method has values in the enumerations VlIKMAlgorithms.

Returns:
new IKM quantizer.
void vl_ikm_push ( VlIKMFilt *  f,
vl_uint *  asgn,
vl_uint8 const *  data,
int  N 
)
Parameters:
fIKM quantizer.
asgnAssignments (out).
datadata.
Nnumber of data (N >= 1).

The function projects the data data on the integer K-means clusters specified by the IKM quantizer f. Notice that the quantizer must be initialized.

vl_uint vl_ikm_push_one ( vl_ikm_acc const *  centers,
vl_uint8 const *  data,
int  M,
int  K 
)
Parameters:
centerscenters.
datadatum to project.
Knumber of centers.
Mdimensionality of the datum.

The function projects the specified datum data on the clusters specified by the centers centers.

Returns:
the cluster index.
void vl_ikm_set_max_niters ( VlIKMFilt *  f,
int  max_niters 
) [inline]
Parameters:
fIKM filter.
max_nitersmaximum number of iterations.
void vl_ikm_set_verbosity ( VlIKMFilt *  f,
int  verb 
) [inline]
Parameters:
fIKM filter.
verbverbosity level.
int vl_ikm_train ( VlIKMFilt *  f,
vl_uint8 const *  data,
int  N 
)
Parameters:
fIKM quantizer.
datadata.
Nnumber of data (N >= 1).
Returns:
-1 if an overflow may have occurred.
 
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Functions
ikmeans_elkan.tc File Reference

Integer K-Means - Elkan Algorithm - Definition. More...

Functions

static unsigned int isqrt (unsigned long val)
static void vl_ikm_elkan_update_inter_dist (VlIKMFilt *f)
static void vl_ikm_init_elkan (VlIKMFilt *f)
 Helper function to initialize filter for Triangle algorithm.
static int vl_ikm_train_elkan (VlIKMFilt *f, vl_uint8 const *data, int N)
 Elkan algorithm.
static void vl_ikm_push_elkan (VlIKMFilt *f, vl_uint *asgn, vl_uint8 const *data, int N)
 Elkan algorithm.

Detailed Description

Author:
Andrea Vedaldi

Function Documentation

static unsigned int isqrt ( unsigned long  val) [static]

Square root of an integer value

Author:
Jim Ulery
static void vl_ikm_elkan_update_inter_dist ( VlIKMFilt *  f) [static]

Update inter cluster distance table.

static void vl_ikm_init_elkan ( VlIKMFilt *  f) [static]
Parameters:
ffilter.
static void vl_ikm_push_elkan ( VlIKMFilt *  f,
vl_uint *  asgn,
vl_uint8 const *  data,
int  N 
) [static]
Parameters:
fIKM quantizer.
asgnAssignment of data to centers (out).
dataData to quantize.
NNumber of data elements.
static int vl_ikm_train_elkan ( VlIKMFilt *  f,
vl_uint8 const *  data,
int  N 
) [static]
Parameters:
fIKM quantizer.
dataData to quantize.
NNumber of data elements.
 
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Functions
ikmeans_init.tc File Reference

Integer K-Means - Initialization - Definition. More...

#include "random.h"

Functions

static void alloc (VlIKMFilt *f, int M, int K)
 Helper function to allocate memory for an IKM quantizer.
static void vl_ikm_init_helper (VlIKMFilt *f)
 Helper function to initialize the quantizer.
void vl_ikm_init (VlIKMFilt *f, vl_ikm_acc const *centers, int M, int K)
 Initialize quantizer with centers.
void vl_ikm_init_rand (VlIKMFilt *f, int M, int K)
 Initialize quantizer with random centers.
void vl_ikm_init_rand_data (VlIKMFilt *f, vl_uint8 const *data, int M, int N, int K)
 Initialize with centers from random data.

Detailed Description

Author:
Andrea Vedaldi

Function Documentation

static void alloc ( VlIKMFilt *  f,
int  M,
int  K 
) [static]
Parameters:
fquantizer.
Mdata dimensionality.
Knumber of clusters.
void vl_ikm_init ( VlIKMFilt *  f,
vl_ikm_acc const *  centers,
int  M,
int  K 
)
Parameters:
fIKM quantizer.
centerscenters.
Mdata dimensionality.
Knumber of clusters.
static void vl_ikm_init_helper ( VlIKMFilt *  f) [static]
Parameters:
fIKM quantizer.
void vl_ikm_init_rand ( VlIKMFilt *  f,
int  M,
int  K 
)
Parameters:
fIKM quantizer.
Mdata dimensionality.
Knumber of clusters.
void vl_ikm_init_rand_data ( VlIKMFilt *  f,
vl_uint8 const *  data,
int  M,
int  N,
int  K 
)
Parameters:
fIKM quantizer.
datadata.
Mdata dimensionality.
Nnumber of data.
Knumber of clusters.
 
vlfeat-0.9.14/doc/api/ikmeans__lloyd_8tc.html000066400000000000000000000236271167540017400210600ustar00rootroot00000000000000 VLFeat - Documentation - C API
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Functions
ikmeans_lloyd.tc File Reference

Integer K-Means - LLoyd Algorithm - Definition. More...

Functions

static void vl_ikm_init_lloyd (VlIKMFilt *f)
 Helper function to initialize a filter for Lloyd algorithm.
static int vl_ikm_train_lloyd (VlIKMFilt *f, vl_uint8 const *data, int N)
 LLoyd algorithm.
static void vl_ikm_push_lloyd (VlIKMFilt *f, vl_uint *asgn, vl_uint8 const *data, int N)
 LLoyd algorithm.

Detailed Description

Author:
Brian Fulkerson
Andrea Vedaldi

Function Documentation

static void vl_ikm_init_lloyd ( VlIKMFilt *  f) [static]
Parameters:
ffilter.
static void vl_ikm_push_lloyd ( VlIKMFilt *  f,
vl_uint *  asgn,
vl_uint8 const *  data,
int  N 
) [static]
Parameters:
fIKM quantizer.
asgnAssignments (out).
dataData to quantize.
NNumber of data.
static int vl_ikm_train_lloyd ( VlIKMFilt *  f,
vl_uint8 const *  data,
int  N 
) [static]
Parameters:
fIKM quantizer.
dataTraining data.
NNumber of traning data.
 
vlfeat-0.9.14/doc/api/imopv_8c.html000066400000000000000000000107521167540017400170300ustar00rootroot00000000000000 VLFeat - Documentation - C API
Documentation - C API
imopv.c File Reference

Vectorized image operations - Definition. More...

#include "imopv.h"
#include "imopv_sse2.h"
#include "mathop.h"
#include "imopv.c"
#include "float.th"

Detailed Description

Author:
Andrea Vedaldi
 
vlfeat-0.9.14/doc/api/imopv_8h.html000066400000000000000000001562351167540017400170440ustar00rootroot00000000000000 VLFeat - Documentation - C API
Documentation - C API
imopv.h File Reference

Vectorized image operations. More...

#include "generic.h"

Defines

Image convolution flags
#define VL_PAD_BY_ZERO   (0x0 << 0)
 Pad with zeroes.
#define VL_PAD_BY_CONTINUITY   (0x1 << 0)
 Pad by continuity.
#define VL_PAD_MASK   (0x3)
 Padding field selector.
#define VL_TRANSPOSE   (0x1 << 2)
 Transpose result.

Functions

Image convolution
void vl_imconvcol_vf (float *dst, int dst_stride, float const *src, int src_width, int src_height, int src_stride, float const *filt, int filt_begin, int filt_end, int step, unsigned int flags)
void vl_imconvcol_vd (double *dst, int dst_stride, double const *src, int src_width, int src_height, int src_stride, double const *filt, int filt_begin, int filt_end, int step, unsigned int flags)
 Convolve image along columns.
void vl_imconvcoltri_f (float *dest, vl_size destStride, float const *image, vl_size imageWidth, vl_size imageHeight, vl_size imageStride, vl_size filterSize, vl_size step, int unsigned flags)
 Convolve an image along the columns with a triangular kernel.
void vl_imconvcoltri_d (double *dest, vl_size destStride, double const *image, vl_size imageWidth, vl_size imageHeight, vl_size imageStride, vl_size filterSize, vl_size step, int unsigned flags)
 Convolve an image along the columns with a triangular kernel.
Integral image
void vl_imintegral_f (float *integral, vl_size integralStride, float const *image, vl_size imageWidth, vl_size imageHeight, vl_size imageStride)
 Compute integral image.
void vl_imintegral_d (double *integral, vl_size integralStride, double const *image, vl_size imageWidth, vl_size imageHeight, vl_size imageStride)
 Compute integral image.
void vl_imintegral_i32 (vl_int32 *integral, vl_size integralStride, vl_int32 const *image, vl_size imageWidth, vl_size imageHeight, vl_size imageStride)
 Compute integral image.
void vl_imintegral_ui32 (vl_uint32 *integral, vl_size integralStride, vl_uint32 const *image, vl_size imageWidth, vl_size imageHeight, vl_size imageStride)
 Compute integral image.
Distance transform
void vl_image_distance_transform_d (double const *image, vl_size numColumns, vl_size numRows, vl_size columnStride, vl_size rowStride, double *distanceTransform, vl_uindex *indexes, double coeff, double offset)
 Compute the distance transform of an image.
void vl_image_distance_transform_f (float const *image, vl_size numColumns, vl_size numRows, vl_size columnStride, vl_size rowStride, float *distanceTransform, vl_uindex *indexes, float coeff, float offset)

Detailed Description

This module provides the following image operations:

  • Separable convolution. The function vl_imconvcol_vf() can be used to compute separable convolutions.
  • Convolution by a triangular kernel. The function vl_imconvcoltri_vf() is an optimized convolution routine for triangular kernels.
Remarks:
Some operations are optimized to exploit possible SIMD instructions. This requires image data to be properly aligned (typically to 16 bytes). Similalry, the image stride (the number of bytes to skip to move to the next image row), must be aligned.
Author:
Andrea Vedaldi

Function Documentation

vl_image_distance_transform_d ( double const *  image,
vl_size  numColumns,
vl_size  numRows,
vl_size  columnStride,
vl_size  rowStride,
double *  distanceTransform,
vl_uindex *  indexes,
double  coeff,
double  offset 
)
Parameters:
imageimage.
numColumnsnumber of columns of the image.
numRowsnumber of rows of the image.
columnStrideoffset from one column to the next.
rowStrideoffset from one row to the next.
distanceTransformdistance transform (out).
indexesnearest neighbor indexes (in/out).
coeffquadratic cost coefficient (non-negative).
offsetquadratic cost offset.

The function computes the distance transform along the first dimension of the image image. Let $ I(u,v) $ be image. Its distance transfrom $ D(u,v) $ is given by:

\[ u^*(u,v) = \min_{u'} I(u',v) + \mathtt{coeff} (u' - u - \mathtt{offset})^2, \quad D(u,v) = I(u^*(u,v),v). \]

Notice that coeff must be non negative.

The function fills in the buffer distanceTransform with $ D $. This buffer must have the same size as image.

If indexes is not NULL, it must be a matrix of the same size o the image. The function interprets the value of this matrix as indexes of the pixels, i.e $ \mathtt{indexes}(u,v) $ is the index of pixel $ (u,v) $. On output, the matrix indexes contains $ \mathtt{indexes}(u^*(u,v),v) $. This information can be used to determine for each pixel $ (u,v) $ its “nearest neighbor&rdquo.

Notice that by swapping numRows and numColumns and columnStride and rowStride, the function can be made to operate along the other image dimension. Specifically, to compute the distance transform along columns and rows, call the functinon twice:

   for (i = 0 ; i < numColumns * numRows ; ++i) indexes[i] = i ;
   vl_image_distance_transform_d(image,numColumns,numRows,1,numColumns,
                                 distanceTransform,indexes,u_coeff,u_offset) ;
   vl_image_distance_transform_d(distanceTransform,numRows,numColumns,numColumns,1,
                                 distanceTransform,indexes,u_coeff,u_offset) ;
Algorithm

The function implements the algorithm described in: P. F. Felzenszwalb and D. P. Huttenlocher, Distance Transforms of Sampled Functions, Technical Report, Cornell University, 2004.

Since the algorithm operates along one dimension per time, consider the 1D version of the problem for simplicity:

\[ d(y) = \min_{x} g(y;x), \quad g(y;x) = f(x) + \alpha (y - x - \beta)^2, \quad x,y \in \{0,1,\dots,N-1\}. \]

Hence the distance transform $ d(y) $ is the lower envelope of the family of parabolas $ g(y;x) $ indexed by $ x $. Notice that all parabolas have the same curvature and that their centers are located at $ x + \beta, $ $ x=0,\dots,N-1 $. The algorithm considers one parabola per time, from left to right, and finds the interval for which the parabola belongs to the lower envelope (if any).

Initially, only the leftmost parabola $ g(y;0) $ has been considered, and its validity interval is $(-\infty, \infty) $. Then the second parabola $ g(y;1) $ is considered. As long as $ \alpha > 0 $, the two parabolas $ g(y;0),\ g(y;1) $ intersect at a unique point $ \bar y $. Then the first parabola belongs to the envelope in the interval $ (-\infty, \bar y] $ and the second one in the interval $ (\bar y, +\infty] $. When the third parabola $ g(y;2) $ is considered, the intersection point $ \hat y $ with the previously added parabola $ g(y;1) $ is found. Now two cases may arise:

  • $ \hat y > \bar y $, in which case all three parabolas belong to the envelope in the intervals $ (-\infty,\bar y], (\bar y, \hat y], (\hat y, +\infty] $.
  • $ \hat y \leq \bar y $, in which case the second parabola $ g(y;1) $ has no point beloning to the envelope, and it is removed. One then remains with the two parabolas $ g(y;0),\ g(y;2) $ and the algorithm is re-iterated.

The algorithm proceeds in this fashion. Every time a new parabola is considered, its intersection point with the previously added parabola on the left is computed, and that parabola is potentially removed. The cost of an iteration is 1 plus the number of deleted parabolas. Since there are $ N $ iterations and at most $ N $ parabolas to delete overall, the complexity is linear, i.e. $ O(N) $.

vl_image_distance_transform_f ( float const *  image,
vl_size  numColumns,
vl_size  numRows,
vl_size  columnStride,
vl_size  rowStride,
float *  distanceTransform,
vl_uindex *  indexes,
float  coeff,
float  offset 
)
See also:
vl_image_distance_transform_d
vl_imconvcol_vd ( double *  dst,
int  dst_stride,
double const *  src,
int  src_width,
int  src_height,
int  src_stride,
double const *  filt,
int  filt_begin,
int  filt_end,
int  step,
unsigned int  flags 
)
Parameters:
dstdestination image.
dst_stridewidth of the destination image including padding.
srcsource image.
src_widthwidth of the source image.
src_heightheight of the source image.
src_stridewidth of the source image including padding.
filtfilter kernel.
filt_begincoordinate of the first filter element.
filt_endcoordinate of the last filter element.
stepsub-sampling step.
flagsoperation modes.

The function convolves the column of the image src by the filter filt and saves the result to the image dst. The size of dst must be equal to the size of src. Formally, this results in the calculation

\[ \mathrm{dst} [x,y] = \sum_{p=y-\mathrm{filt\_end}}^{y-\mathrm{filt\_begin}} \mathrm{src}[x,y] \mathrm{filt}[y - p - \mathrm{filt\_begin}] \]

The function subsamples the image along the columns according to the parameter step. Setting step to 1 (one) computes the elements $\mathrm{dst}[x,y]$ for all pairs (x,0), (x,1), (x,2) and so on. Setting step two 2 (two) computes only (x,0), (x,2) and so on (in this case the height of the destination image is floor(src_height/step)+1).

Calling twice the function can be used to compute 2-D separable convolutions. Use the flag VL_TRANSPOSE to transpose the result (in this case dst has transposed dimension as well).

The function allows the support of the filter to be any range. Usually the support is filt_end = -filt_begin.

The convolution operation may pick up values outside the image boundary. To cope with this edge cases, the function either pads the image by zero (VL_PAD_BY_ZERO) or with the values at the boundary (VL_PAD_BY_CONTINUITY).

vl_imconvcol_vf ( float *  dst,
int  dst_stride,
float const *  src,
int  src_width,
int  src_height,
int  src_stride,
float const *  filt,
int  filt_begin,
int  filt_end,
int  step,
unsigned int  flags 
)
See also:
vl_imconvcol_vf()
vl_imconvcoltri_d ( double *  dest,
vl_size  destStride,
double const *  image,
vl_size  imageWidth,
vl_size  imageHeight,
vl_size  imageStride,
vl_size  filterSize,
vl_size  step,
int unsigned  flags 
)
Parameters:
destdestination image.
destStridedestination image stride.
imageimage to convolve.
imageWidthwidth of the image.
imageHeightheight of the image.
imageStridewidth of the image including padding.
filterSizesize of the triangular filter.
stepsub-sampling step.
flagsoperation modes.

The function convolves the columns of the image image with the triangular kernel

\[ k(t) = \frac{1}{\Delta^2} \max\{ \Delta - |t|, 0 \}, \quad t \in \mathbb{Z} \]

The paramter $ \Delta $, equal to the function argument filterSize, controls the width of the kernel. Notice that the support of $ k(x) $ as a continuous function of $ x $ is the open interval $ (-\Delta,\Delta) $, which has length $ 2\Delta $. However, $ k(x) $ restricted to the ingeter domain $ x \in \mathcal{Z} $ has support $ \{ -\Delta + 1, \Delta +2, \dots, \Delta-1 \} $, which counts $ 2 \Delta - 1 $ elements only. In particular, the discrete kernel is symmetric about the origin for all values of $ \Delta $.

The normalization factor $ 1 / \Delta^2 $ guaratnees that the filter is normalized to one, i.e.:

\[ \sum_{t=-\infty}^{+\infty} k(t) = 1 \]

Algorithm

The function exploits the fact that convolution by a triangular kernel can be expressed as the repeated convolution by a rectangular kernel, and that the latter can be performed in time indepenedent on the fiter width by using an integral-image type trick. Overall, the algorithm complexity is independent on the parameter filterSize and linear in the nubmer of image pixels.

See also:
vl_imconvcol_vd for details on the meaning of the other parameters.
vl_imconvcoltri_f ( float *  dest,
vl_size  destStride,
float const *  image,
vl_size  imageWidth,
vl_size  imageHeight,
vl_size  imageStride,
vl_size  filterSize,
vl_size  step,
int unsigned  flags 
)
See also:
vl_imconvcoltri_d()
vl_imintegral_d ( double *  integral,
vl_size  integralStride,
double const *  image,
vl_size  imageWidth,
vl_size  imageHeight,
vl_size  imageStride 
)
Parameters:
integralintegral image.
integralStrideintegral image stride.
imagesource image.
imageWidthsource image width.
imageHeightsource image height.
imageStridesource image stride.

Let $ I(x,y), (x,y) \in [0, W-1] \times [0, H-1] $. The function computes the integral image $ J(x,y) $ of $ I(x,g) $:

\[ J(x,y) = \sum_{x'=0}^{x} \sum_{y'=0}^{y} I(x',y') \]

The integral image $ J(x,y) $ can be used to compute quickly the integral of of $ I(x,y) $ in a rectangular region $ R = [x',x'']\times[y',y''] $:

\[ \sum_{(x,y)\in[x',x'']\times[y',y'']} I(x,y) = (J(x'',y'') - J(x'-1, y'')) - (J(x'',y'-1) - J(x'-1,y'-1)). \]

Note that the order of operations is important when the integral image has an unsigned data type (e.g. vl_uint32). The formula is easily derived as follows:

\begin{eqnarray*} \sum_{(x,y)\in R} I(x,y) &=& \sum_{x=x'}^{x''} \sum_{y=y'}^{y''} I(x,y)\\ &=& \sum_{x=0}^{x''} \sum_{y=y'}^{y''} I(x,y) - \sum_{x=0}^{x'-1} \sum_{y=y'}^{y''} I(x,y)\\ &=& \sum_{x=0}^{x''} \sum_{y=0}^{y''} I(x,y) - \sum_{x=0}^{x''} \sum_{y=0}^{y'-1} I(x,y) - \sum_{x=0}^{x'-1} \sum_{y=0}^{y''} I(x,y) + \sum_{x=0}^{x'-1} \sum_{y=0}^{y'-1} I(x,y)\\ &=& J(x'',y'') - J(x'-1,y'') - J(x'',y'-1) + J(x'-1,y'-1). \end{eqnarray*}

vl_imintegral_f ( float *  integral,
vl_size  integralStride,
float const *  image,
vl_size  imageWidth,
vl_size  imageHeight,
vl_size  imageStride 
)
See also:
vl_imintegral_d.
vl_imintegral_i32 ( vl_int32 *  integral,
vl_size  integralStride,
vl_int32 const *  image,
vl_size  imageWidth,
vl_size  imageHeight,
vl_size  imageStride 
)
See also:
vl_imintegral_d.
vl_imintegral_ui32 ( vl_uint32 *  integral,
vl_size  integralStride,
vl_uint32 const *  image,
vl_size  imageWidth,
vl_size  imageHeight,
vl_size  imageStride 
)
See also:
vl_imintegral_d.
 
vlfeat-0.9.14/doc/api/imopv__sse2_8c.html000066400000000000000000000106501167540017400201200ustar00rootroot00000000000000 VLFeat - Documentation - C API
Documentation - C API
imopv_sse2.c File Reference

Vectorized image operations - SSE2 - Definition. More...

#include <emmintrin.h>
#include "imopv.h"
#include "imopv_sse2.h"
#include "imopv_sse2.c"

Detailed Description

Author:
Andrea Vedaldi
 
vlfeat-0.9.14/doc/api/imopv__sse2_8h.html000066400000000000000000000102561167540017400201270ustar00rootroot00000000000000 VLFeat - Documentation - C API
Documentation - C API
imopv_sse2.h File Reference

Vectorized image operations - SSE2. More...

#include "generic.h"

Detailed Description

Author:
Andrea Vedaldi
 
vlfeat-0.9.14/doc/api/index.html000066400000000000000000000155571167540017400164230ustar00rootroot00000000000000 VLFeat - Documentation - C API
Documentation - C API
VLFeat -- Vision Lab Features Library
Version:
0.9.14
Author:
Andrea Vedaldi (vedaldi@robots.ox.ac.uk)
Brian Fulkerson (bfulkers@cs.ucla.edu)
Copyright © 2007-12 Andrea Vedaldi and Brian Fulkerson

VLFeat C library contains implementations of common computer vision algorithms, with a special focus on visual features for matching image regions. Applications include structure from motion and object and category detection and recognition.

We strive to make the library free of clutter, portable (VLFeat is largely C-89 compatible), and self- documented. Different parts of the library are weakly interdependent, simplifying understanding and extraction of code.

Contents

 
vlfeat-0.9.14/doc/api/kdtree.html000066400000000000000000000171251167540017400165630ustar00rootroot00000000000000 VLFeat - Documentation - C API
Documentation - C API
KD-trees and forests
Author:
Andrea Vedaldi

kdtree.h implements a KD-tree object, a data structure that can efficiently index moderately dimensional vector spaces. Both best-bin-first [3] and randomized KD-tree forests are implemented [10] , [8] . Applications include fast matching of feature descriptors.

Overview

To create a VlKDForest object use vl_kdforest_new specifying the dimensionality of the data and the number of trees in the forest. With one tree only, the algorithm is analogous to [3] (best-bin KDTree). Multiple trees correspond to the randomized KDTree forest as in [10] , [8] .

To let the KD-tree index some data use vl_kdforest_build. Note that for efficiency KD-tree does not copy the data but retains a pointer to it. Therefore the data must exist (and not change) until the KD-tree is deleted. To delete the KD-tree object, use vl_kdforest_delete.

To find the N nearest neighbors to a query point use vl_kdforest_query. To set a maximum number of comparisons per query and calculate approximate nearest neighbors use vl_kdforest_set_max_num_comparisons.

Technical details

VlKDForest implements the best-bin-first kd-tree of [3] .

Construction. Given a set of points $ x_1,\dots,x_n \in \mathbb{R}^d $, the algorithm recursively partitions the d dimensional Euclidean space $ \mathbb{R}^d $ into (hyper-) rectangles.

Partitions are organized into a binary tree with the root corresponding to the whole space $ \mathbb{R}^d $. The algorithm refines each partition by dividing it into two halves by thresholding along a given dimension. Both the splitting dimension and the threshold are determined as a statistic of the data points contained in the partition. The splitting dimension is the one which has largest sample variance and the splitting threshold is either the sample mean or the median. Leaves are atomic partitions and they contain a list of zero or more data points (typically one).

Querying. Querying amounts to finding the N data points closer to a given query point $ x_q \in \mathbb{R}^d $. This is done by branch-and-bound. A search state is an active partition (initially the root) and it is weighed by the lower bound on the distance of any point in the partition and the query point. Such a lower bound is trivial to compute because partitions are hyper-rectangles.

 
vlfeat-0.9.14/doc/api/kdtree_8c.html000066400000000000000000000616551167540017400171640ustar00rootroot00000000000000 VLFeat - Documentation - C API
Documentation - C API
Functions
kdtree.c File Reference

KD-tree - Definition. More...

#include "kdtree.h"
#include "generic.h"
#include "random.h"
#include "mathop.h"
#include <stdlib.h>
#include "heap-def.h"

Functions

static vl_uindex vl_kdtree_node_new (VlKDTree *tree, vl_uindex parentIndex)
 Allocate a new node from the tree pool.
int vl_kdtree_compare_index_entries (void const *a, void const *b)
 Compare KDTree index entries for sorting.
static void vl_kdtree_build_recursively (VlKDForest *forest, VlKDTree *tree, vl_uindex nodeIndex, vl_uindex dataBegin, vl_uindex dataEnd, unsigned int depth)
 Build KDTree recursively.
VlKDForest * vl_kdforest_new (vl_type dataType, vl_size dimension, vl_size numTrees)
 Create new KDForest object.
void vl_kdforest_delete (VlKDForest *self)
 Delete KDForest object.
void vl_kdforest_build (VlKDForest *self, vl_size numData, void const *data)
 Build KDTree from data.
int vl_kdforest_query_recursively (VlKDForest *self, VlKDTree *tree, vl_uindex nodeIndex, VlKDForestNeighbor *neighbors, vl_size numNeighbors, vl_size *numAddedNeighbors, double dist, void const *query)
static void vl_kdtree_calc_bounds_recursively (VlKDTree *tree, vl_uindex nodeIndex, double *searchBounds)
 Compute tree bounds recursively.
vl_size vl_kdforest_query (VlKDForest *self, VlKDForestNeighbor *neighbors, vl_size numNeighbors, void const *query)
 Query operation.

Detailed Description

Author:
Andrea Vedaldi

Function Documentation

void vl_kdforest_build ( VlKDForest *  self,
vl_size  numData,
void const *  data 
)
Parameters:
selfKDTree object
numDatanumber of data points.
datapointer to the data.

The function builds the KDTree by processing the data data. For efficiency, KDTree does not copy the data, but retains a pointer to it. Therefore the data must survive (and not change) until the KDTree is deleted.

void vl_kdforest_delete ( VlKDForest *  self)
Parameters:
selfKDForest object to delete
See also:
vl_kdforest_new
VlKDForest* vl_kdforest_new ( vl_type  dataType,
vl_size  dimension,
vl_size  numTrees 
)
Parameters:
dataTypetype of data (VL_TYPE_FLOAT or VL_TYPE_DOUBLE)
dimensiondata dimensionality.
numTreesnumber of trees in the forest.
Returns:
new KDForest.
vl_size vl_kdforest_query ( VlKDForest *  self,
VlKDForestNeighbor *  neighbors,
vl_size  numNeighbors,
void const *  query 
)
Parameters:
selfKDTree object instance.
neighborslist of nearest neighbors found (output).
numNeighborsnumber of nearest neighbors to find.
queryquery point.
Returns:
number of tree leaves visited.

A neighbor is represented by an instance of the structure VlKDForestNeighbor. Each entry contains the index of the neighbor (this is an index into the KDTree data) and its distance to the query point. Neighbors are sorted by increasing distance.

int vl_kdforest_query_recursively ( VlKDForest *  self,
VlKDTree *  tree,
vl_uindex  nodeIndex,
VlKDForestNeighbor *  neighbors,
vl_size  numNeighbors,
vl_size *  numAddedNeighbors,
double  dist,
void const *  query 
)
static void vl_kdtree_build_recursively ( VlKDForest *  forest,
VlKDTree *  tree,
vl_uindex  nodeIndex,
vl_uindex  dataBegin,
vl_uindex  dataEnd,
unsigned int  depth 
) [static]
Parameters:
forestforest to which the tree belongs.
treetree being built.
nodeIndexnode to process.
dataBeginbegin of data for this node.
dataEndend of data for this node.
depthdepth of this node.
static void vl_kdtree_calc_bounds_recursively ( VlKDTree *  tree,
vl_uindex  nodeIndex,
double *  searchBounds 
) [static]
Parameters:
treeKDTree object instance.
nodeIndexnode index to start from.
searchBounds2 x numDimension array of bounds.
int vl_kdtree_compare_index_entries ( void const *  a,
void const *  b 
) [inline]
static vl_uindex vl_kdtree_node_new ( VlKDTree *  tree,
vl_uindex  parentIndex 
) [static]
 
vlfeat-0.9.14/doc/api/kdtree_8h.html000066400000000000000000000760511167540017400171650ustar00rootroot00000000000000 VLFeat - Documentation - C API
Documentation - C API
Data Structures | Enumerations
kdtree.h File Reference

KD-tree (KD-trees and forests) More...

#include "generic.h"
#include "mathop.h"

Data Structures

struct  VlKDForestNeighbor
 Neighbor of a query point. More...
struct  VlKDForest
 KDForest object. More...

Enumerations

enum  VlKDTreeThresholdingMethod
 Thresholding method.

Functions

Creatind and disposing
VlKDForest * vl_kdforest_new (vl_type dataType, vl_size dimension, vl_size numTrees)
 Create new KDForest object.
void vl_kdforest_delete (VlKDForest *self)
 Delete KDForest object.
Building and querying
void vl_kdforest_build (VlKDForest *self, vl_size numData, void const *data)
 Build KDTree from data.
vl_size vl_kdforest_query (VlKDForest *self, VlKDForestNeighbor *neighbors, vl_size numNeighbors, void const *query)
 Query operation.
Retrieving and setting parameters
vl_size vl_kdforest_get_depth_of_tree (VlKDForest const *self, vl_uindex treeIndex)
 Get the detph of a given tree.
vl_size vl_kdforest_get_num_nodes_of_tree (VlKDForest const *self, vl_uindex treeIndex)
 Get the number of nodes of a given tree.
vl_size vl_kdforest_get_num_trees (VlKDForest const *self)
 Get the number of trees in the forest.
vl_size vl_kdforest_get_data_dimension (VlKDForest const *self)
 Get the dimension of the data.
vl_type vl_kdforest_get_data_type (VlKDForest const *self)
 Get the data type.
void vl_kdforest_set_max_num_comparisons (VlKDForest *self, vl_size n)
 Set the maximum number of comparisons for a search.
vl_size vl_kdforest_get_max_num_comparisons (VlKDForest *self)
 Get the maximum number of comparisons for a search.
void vl_kdforest_set_thresholding_method (VlKDForest *self, VlKDTreeThresholdingMethod method)
 Set the thresholding method.
VlKDTreeThresholdingMethod vl_kdforest_get_thresholding_method (VlKDForest const *self)
 Get the thresholding method.

Detailed Description

Author:
Andrea Vedaldi

Function Documentation

void vl_kdforest_build ( VlKDForest *  self,
vl_size  numData,
void const *  data 
)
Parameters:
selfKDTree object
numDatanumber of data points.
datapointer to the data.

The function builds the KDTree by processing the data data. For efficiency, KDTree does not copy the data, but retains a pointer to it. Therefore the data must survive (and not change) until the KDTree is deleted.

void vl_kdforest_delete ( VlKDForest *  self)
Parameters:
selfKDForest object to delete
See also:
vl_kdforest_new
vl_size vl_kdforest_get_data_dimension ( VlKDForest const *  self) [inline]
Parameters:
selfKDForest object.
Returns:
dimension of the data.
vl_type vl_kdforest_get_data_type ( VlKDForest const *  self) [inline]
Parameters:
selfKDForest object.
Returns:
data type (one of VL_TYPE_FLOAT, VL_TYPE_DOUBLE).
vl_size vl_kdforest_get_depth_of_tree ( VlKDForest const *  self,
vl_uindex  treeIndex 
) [inline]
Parameters:
selfKDForest object.
treeIndexindex of the tree.
Returns:
number of trees.
vl_size vl_kdforest_get_max_num_comparisons ( VlKDForest *  self) [inline]
Parameters:
selfKDForest object.
Returns:
maximum number of leaves.
See also:
vl_kdforest_set_max_num_comparisons.
vl_size vl_kdforest_get_num_nodes_of_tree ( VlKDForest const *  self,
vl_uindex  treeIndex 
) [inline]
Parameters:
selfKDForest object.
treeIndexindex of the tree.
Returns:
number of trees.
vl_size vl_kdforest_get_num_trees ( VlKDForest const *  self) [inline]
Parameters:
selfKDForest object.
Returns:
number of trees.
VlKDTreeThresholdingMethod vl_kdforest_get_thresholding_method ( VlKDForest const *  self) [inline]
Parameters:
selfKDForest object.
Returns:
thresholding method.
See also:
vl_kdforest_set_thresholding_method
VlKDForest* vl_kdforest_new ( vl_type  dataType,
vl_size  dimension,
vl_size  numTrees 
)
Parameters:
dataTypetype of data (VL_TYPE_FLOAT or VL_TYPE_DOUBLE)
dimensiondata dimensionality.
numTreesnumber of trees in the forest.
Returns:
new KDForest.
vl_size vl_kdforest_query ( VlKDForest *  self,
VlKDForestNeighbor *  neighbors,
vl_size  numNeighbors,
void const *  query 
)
Parameters:
selfKDTree object instance.
neighborslist of nearest neighbors found (output).
numNeighborsnumber of nearest neighbors to find.
queryquery point.
Returns:
number of tree leaves visited.

A neighbor is represented by an instance of the structure VlKDForestNeighbor. Each entry contains the index of the neighbor (this is an index into the KDTree data) and its distance to the query point. Neighbors are sorted by increasing distance.

void vl_kdforest_set_max_num_comparisons ( VlKDForest *  self,
vl_size  n 
) [inline]
Parameters:
selfKDForest object.
nmaximum number of leaves.

This function sets the maximum number of comparisons for a nearest neighbor search. Setting it to 0 means unbounded comparisons.

See also:
vl_kdforest_query, vl_kdforest_get_max_num_comparisons.
void vl_kdforest_set_thresholding_method ( VlKDForest *  self,
VlKDTreeThresholdingMethod  method 
) [inline]
Parameters:
selfKDForest object.
methodone of VlKDTreeThresholdingMethod.
See also:
vl_kdforest_get_thresholding_method
 
vlfeat-0.9.14/doc/api/kmeans.html000066400000000000000000000340621167540017400165620ustar00rootroot00000000000000 VLFeat - Documentation - C API
Documentation - C API
K-means clustering
Author:
Andrea Vedaldi

kmeans.h implements a number of algorithm for k-means quantisation. It supports

  • data of type float or double;
  • l1 and l2 distances;
  • random selection and k-means++ [2] initialization methods;
  • the basic Lloyd [5] and the accelerated Elkan [4] optimization methods.

Usage

To use kmeans.h to learn clusters from some training data, instantiate a VlKMeans object, set the configuration parameters, initialise the cluster centers, and run the trainig code. For instance, to learn numCenters clusters from numData vectors of dimension dimension and storage type float using L2 distance and at most 100 Lloyd iterations of the Lloyd algorithm use:

#include <vl/kmeans.h>

VlKMeansAlgorithm algorithm = VlKMeansLloyd ;
VlVectorComparisonType distance = VlDistanceL2 ;
KMeans * kmeans = vl_kmeans_new (algorithm, distance, VL_TYPE_FLOAT) ;
vl_kmeans_seed_centers_with_rand_data (kmeans, data, dimension, numData, numCenters) ;
vl_kmeans_set_max_num_iterations (kmeans, 100) ;
vl_kmeans_refine_centers (kmeans, data, numData) ;

Use vl_kmeans_get_energy to get the solution energy (or an upper bound for the Elkan algorithm) and vl_kmeans_get_centers to obtain the numCluster cluster centers. Use vl_kmeans_quantize to quantize new data points.

Initialization algorithms

kmeans.h supports the following cluster initialization algorithms:

Optimization algorithms

kmeans.h supports the following optimization algorithms:

  • Lloyd [5] (VlKMeansLloyd). This is the standard k-means algorithm, alternating the estimation of the point-to-cluster memebrship and of the cluster centers (means in the Euclidean case). Estimating membership requires computing the distance of each point to all cluster centers, which can be extremely slow.
  • Elkan [4] (VlKMeansElkan). This is a variation of [5] that uses the triangular inequality to avoid many distance calculations when assigning points to clusters and is typically much faster than [5] . However, it uses storage proportional to the square of the number of clusters, which makes it unpractical for a very large number of clusters.

Technical details

Given data points $ x_1, \dots, x_n \in \mathbb{R}^d $, k-means searches for $ k $ vectors $ c_1, \dots, c_n \in \mathbb{R}^d $ (cluster centers) and a function $ \pi : \{1, \dots, n\} \rightarrow \{1, \dots, k\} $ (cluster memberships) that minimize the objective:

\[ E(c_1,\dots,c_n,\pi) = \sum_{i=1}^n d^2(x_i, c_{\pi(i)}) \]

A simple procedure due to Lloyd [5] to locally optimize this objective alternates estimating the cluster centers and the membeship function. Specifically, given the membership function $ \pi $, the objective can be minimized independently for eac $ c_k $ by minimizing

\[ \sum_{i : \pi(i) = k} d^2(x_i, c_k) \]

For the Euclidean distance, the minimizer is simply the mean of the points assigned to that cluster. For other distances, the minimizer is a generalized average. For instance, for the $ l^1 $ distance, this is the median. Assuming that computing the average is linear in the number of points and the data dimension, this step requires $ O(nd) $ operations.

Similarly, given the centers $ c_1, \dots, c_k $, the objective can be optimized independently for the membership $ \pi(i) $ of each point $ x_i $ by minimizing $ d^2(x_i, c_{\pi(i)}) $ over $ \pi(i) \in \{1, \dots, k\} $. Assuming that computing a distance is $ O(d) $, this step requires $ O(ndk) $ operations and dominates the other.

The algorithm usually starts by initializing the centers from a random selection of the data point.

Initialization by k-means++

[2] proposes a randomized initialization of the centers which improves upon random selection. The first center $ c_1 $ is selected at random from the data points $ x_1, \dots, x_n $ and the distance from this center to all points $ d^2(x_i, c_1) $ is computed. Then the second center $ c_2 $ is selected at random from the data points with probability proportional to the distance, and the procedure is repeated using the minimum distance to the centers collected so far.

Speeding up by using the triangular inequality

[3] proposes to use the triangular inequality to avoid most distances calculations when computing point-to-cluster membership and the cluster centers did not change much from the previous iteration.

This uses two key ideas:

  • If a point $ x_i $ is very close to its current center $ c_{\pi(i)} $ and this center is very far from another center $ c $, then the point cannot be assigned to $ c $. Specifically, if $ d(x_i, c_{\pi(i)}) \leq d(c_{\pi(i)}, c) / 2 $, then also $ d(x_i, c_{\pi(i)}) \leq d(x_i, c) $.
  • If a center $ c $ is updated to $ \hat c $, then the variation of the distance of the center to any point can be bounded by $ d(x, c) - d(c, \hat c) \leq d(x, \hat c) \leq d(x,c) + d(c, \hat c) $.

The first idea is used by keeping track of the inter-center distances and exlcuding reassigments to centers too far away from the current assigned center. The second idea is used by keeping for each point an upper bound to the distance to the currently assigned center and a lower bound to the distance to all the other centers. Unless such bounds do not intersect, then a point need not to be reassigned. See [3] for details.

 
vlfeat-0.9.14/doc/api/kmeans_8c.html000066400000000000000000000672171167540017400171640ustar00rootroot00000000000000 VLFeat - Documentation - C API
Documentation - C API
Functions
kmeans.c File Reference

K-means - Declaration. More...

#include "kmeans.h"
#include "generic.h"
#include "mathop.h"
#include <string.h>
#include "shuffle-def.h"

Functions

void vl_kmeans_reset (VlKMeans *self)
 Reset state.
VlKMeans * vl_kmeans_new (vl_type dataType, VlVectorComparisonType distance)
 Create a new KMeans object.
VlKMeans * vl_kmeans_new_copy (VlKMeans const *kmeans)
 Create a new KMeans object by copy.
void vl_kmeans_delete (VlKMeans *self)
 Deletes a KMeans object.
void vl_kmeans_set_centers (VlKMeans *self, void const *centers, vl_size dimension, vl_size numCenters)
 Set centers.
void vl_kmeans_seed_centers_with_rand_data (VlKMeans *self, void const *data, vl_size dimension, vl_size numData, vl_size numCenters)
 Seed centers by randomly sampling data.
void vl_kmeans_seed_centers_plus_plus (VlKMeans *self, void const *data, vl_size dimension, vl_size numData, vl_size numCenters)
 Seed centers by the KMeans++ algorithm.
void vl_kmeans_quantize (VlKMeans *self, vl_uint32 *assignments, void *distances, void const *data, vl_size numData)
 Quantize data.
double vl_kmeans_refine_centers (VlKMeans *self, void const *data, vl_size numData)
 Refine center locations.
double vl_kmeans_cluster (VlKMeans *self, void const *data, vl_size dimension, vl_size numData, vl_size numCenters)
 Cluster data.

Detailed Description

Author:
Andrea Vedaldi

Function Documentation

double vl_kmeans_cluster ( VlKMeans *  self,
void const *  data,
vl_size  dimension,
vl_size  numData,
vl_size  numCenters 
)
Parameters:
selfKMeans object.
datadata to quantize.
dimensiondata dimension.
numDatanumber of data points.
numCentersnumber of clusters.
Returns:
K-means energy at the end of optimization.

The function initializes the centers by using the initialization algorithm set by vl_kmeans_set_initialization and refines them by the quantization algorithm set by vl_kmeans_set_algorithm. The process is repeated one or more times (see vl_kmeans_set_num_repetitions) and the resutl with smaller energy is retained.

void vl_kmeans_delete ( VlKMeans *  self)
Parameters:
selfKMeans object instance.

The function deletes the KMeans object instance created by vl_kmeans_new.

VlKMeans* vl_kmeans_new ( vl_type  dataType,
VlVectorComparisonType  distance 
)
Parameters:
dataTypetype of data (VL_TYPE_FLOAT or VL_TYPE_DOUBLE)
distancedistance.
Returns:
new KMeans object instance.
VlKMeans* vl_kmeans_new_copy ( VlKMeans const *  kmeans)
Parameters:
kmeansKMeans object to copy.
Returns:
new copy.
void vl_kmeans_quantize ( VlKMeans *  self,
vl_uint32 *  assignments,
void *  distances,
void const *  data,
vl_size  numData 
)
Parameters:
selfKMeans object.
assignmentsdata to centers assignments.
distancesdata to closes center distance/
datadata to quantize.
numDatanumber of data points.
double vl_kmeans_refine_centers ( VlKMeans *  self,
void const *  data,
vl_size  numData 
)
Parameters:
selfKMeans object.
datadata to quantize.
numDatanumber of data points.
Returns:
K-means energy at the end of optimization.

The function calls the underlying K-means quantization algorithm (VlKMeansAlgorithm) to quantize the specified data data. The function assumes that the cluster centers have already been assigned by using one of the seeding functions, or by setting them.

void vl_kmeans_reset ( VlKMeans *  self)

The function reset the state of the KMeans object. It deletes any stored centers, releasing the corresponding memory. This cancels the effect of seeding or setting the centers, but does not change the other configuration parameters.

void vl_kmeans_seed_centers_plus_plus ( VlKMeans *  self,
void const *  data,
vl_size  dimension,
vl_size  numData,
vl_size  numCenters 
)
Parameters:
selfKMeans object.
datadata to sample from.
dimensiondata dimension.
numDatanmber of data points.
numCentersnumber of centers.
void vl_kmeans_seed_centers_with_rand_data ( VlKMeans *  self,
void const *  data,
vl_size  dimension,
vl_size  numData,
vl_size  numCenters 
)
Parameters:
selfKMeans object.
datadata to sample from.
dimensiondata dimension.
numDatanmber of data points.
numCentersnumber of centers.

The function seeds the KMeans centers by randomly sampling the data data.

void vl_kmeans_set_centers ( VlKMeans *  self,
void const *  centers,
vl_size  dimension,
vl_size  numCenters 
)
Parameters:
selfKMeans object.
centerscenters to copy.
dimensiondata dimension.
numCentersnumber of centers.
 
vlfeat-0.9.14/doc/api/kmeans_8h.html000066400000000000000000001612731167540017400171660ustar00rootroot00000000000000 VLFeat - Documentation - C API
Documentation - C API
Data Structures | Enumerations
kmeans.h File Reference

K-means (K-means clustering) More...

#include "generic.h"
#include "random.h"
#include "mathop.h"

Data Structures

struct  VlKMeans
 K-means quantizer. More...

Enumerations

enum  VlKMeansAlgorithm { VlKMeansLloyd, VlKMeansElkan, VlKMeansANN }
 K-means algorithms. More...
enum  VlKMeansInitialization { VlKMeansRandomSelection, VlKMeansPlusPlus }
 K-means initialization algorithms. More...

Functions

Create and destroy
VlKMeans * vl_kmeans_new (vl_type dataType, VlVectorComparisonType distance)
 Create a new KMeans object.
VlKMeans * vl_kmeans_new_copy (VlKMeans const *kmeans)
 Create a new KMeans object by copy.
void vl_kmeans_delete (VlKMeans *self)
 Deletes a KMeans object.
Basic data processing
void vl_kmeans_reset (VlKMeans *self)
 Reset state.
double vl_kmeans_cluster (VlKMeans *self, void const *data, vl_size dimension, vl_size numData, vl_size numCenters)
 Cluster data.
void vl_kmeans_quantize (VlKMeans *self, vl_uint32 *assignments, void *distances, void const *data, vl_size numData)
 Quantize data.
Advanced data processing
void vl_kmeans_set_centers (VlKMeans *self, void const *centers, vl_size dimension, vl_size numCenters)
 Set centers.
void vl_kmeans_seed_centers_with_rand_data (VlKMeans *self, void const *data, vl_size dimensions, vl_size numData, vl_size numCenters)
 Seed centers by randomly sampling data.
void vl_kmeans_seed_centers_plus_plus (VlKMeans *self, void const *data, vl_size dimensions, vl_size numData, vl_size numCenters)
 Seed centers by the KMeans++ algorithm.
double vl_kmeans_refine_centers (VlKMeans *self, void const *data, vl_size numData)
 Refine center locations.
Retrieve data and parameters
vl_type vl_kmeans_get_data_type (VlKMeans const *self)
 Get data type.
VlVectorComparisonType vl_kmeans_get_distance (VlKMeans const *self)
 Get data type.
VlKMeansAlgorithm vl_kmeans_get_algorithm (VlKMeans const *self)
 Get K-means algorithm.
VlKMeansInitialization vl_kmeans_get_initialization (VlKMeans const *self)
 Get K-means initialization algorithm.
vl_size vl_kmeans_get_num_repetitions (VlKMeans const *self)
 Get maximum number of repetitions.
vl_size vl_kmeans_get_dimension (VlKMeans const *self)
 Get data dimension.
vl_size vl_kmeans_get_num_centers (VlKMeans const *self)
 Get the number of centers (K)
int vl_kmeans_get_verbosity (VlKMeans const *self)
 Get verbosity level.
vl_size vl_kmeans_get_max_num_iterations (VlKMeans const *self)
 Get maximum number of iterations.
double vl_kmeans_get_energy (VlKMeans const *self)
 Get the number energy of the current fit.
void const * vl_kmeans_get_centers (VlKMeans const *self)
 Get centers.
Set parameters
void vl_kmeans_set_algorithm (VlKMeans *self, VlKMeansAlgorithm algorithm)
 Set K-means algorithm.
void vl_kmeans_set_initialization (VlKMeans *self, VlKMeansInitialization initialization)
 Set K-means initialization algorithm.
void vl_kmeans_set_num_repetitions (VlKMeans *self, vl_size numRepetitions)
 Set maximum number of repetitions.
void vl_kmeans_set_max_num_iterations (VlKMeans *self, vl_size maxNumIterations)
 Set maximum number of iterations.
void vl_kmeans_set_verbosity (VlKMeans *self, int verbosity)
 Set verbosity level.

Detailed Description

Author:
Andrea Vedaldi

Enumeration Type Documentation

enum VlKMeansAlgorithm
Enumerator:
VlKMeansLloyd 

Lloyd algorithm

VlKMeansElkan 

Elkan algorithm

VlKMeansANN 

Approximate nearest neighbors

enum VlKMeansInitialization
Enumerator:
VlKMeansRandomSelection 

Randomized selection

VlKMeansPlusPlus 

Plus plus raondomized selection

Function Documentation

double vl_kmeans_cluster ( VlKMeans *  self,
void const *  data,
vl_size  dimension,
vl_size  numData,
vl_size  numCenters 
)
Parameters:
selfKMeans object.
datadata to quantize.
dimensiondata dimension.
numDatanumber of data points.
numCentersnumber of clusters.
Returns:
K-means energy at the end of optimization.

The function initializes the centers by using the initialization algorithm set by vl_kmeans_set_initialization and refines them by the quantization algorithm set by vl_kmeans_set_algorithm. The process is repeated one or more times (see vl_kmeans_set_num_repetitions) and the resutl with smaller energy is retained.

void vl_kmeans_delete ( VlKMeans *  self)
Parameters:
selfKMeans object instance.

The function deletes the KMeans object instance created by vl_kmeans_new.

VlKMeansAlgorithm vl_kmeans_get_algorithm ( VlKMeans const *  self) [inline]
Parameters:
selfKMeans object.
Returns:
algorithm.
void const * vl_kmeans_get_centers ( VlKMeans const *  self) [inline]
Parameters:
selfKMeans object instance.
Returns:
cluster centers.
vl_type vl_kmeans_get_data_type ( VlKMeans const *  self) [inline]
Parameters:
selfKMeans object instance.
Returns:
data type.
vl_size vl_kmeans_get_dimension ( VlKMeans const *  self) [inline]
Parameters:
selfKMeans object instance.
Returns:
data dimension.
VlVectorComparisonType vl_kmeans_get_distance ( VlKMeans const *  self) [inline]
Parameters:
selfKMeans object instance.
Returns:
data type.
double vl_kmeans_get_energy ( VlKMeans const *  self) [inline]
Parameters:
selfKMeans object instance.
Returns:
energy.
VlKMeansInitialization vl_kmeans_get_initialization ( VlKMeans const *  self) [inline]
Parameters:
selfKMeans object.
Returns:
algorithm.
vl_size vl_kmeans_get_max_num_iterations ( VlKMeans const *  self) [inline]
Parameters:
selfKMeans object instance.
Returns:
maximum number of iterations.
vl_size vl_kmeans_get_num_centers ( VlKMeans const *  self) [inline]
Parameters:
selfKMeans object instance.
Returns:
number of centers.
vl_size vl_kmeans_get_num_repetitions ( VlKMeans const *  self) [inline]
Parameters:
selfKMeans object instance.
Returns:
current number of repretitions for quantization.
int vl_kmeans_get_verbosity ( VlKMeans const *  self) [inline]
Parameters:
selfKMeans object instance.
Returns:
verbosity level.
VlKMeans* vl_kmeans_new ( vl_type  dataType,
VlVectorComparisonType  distance 
)
Parameters:
dataTypetype of data (VL_TYPE_FLOAT or VL_TYPE_DOUBLE)
distancedistance.
Returns:
new KMeans object instance.
VlKMeans* vl_kmeans_new_copy ( VlKMeans const *  kmeans)
Parameters:
kmeansKMeans object to copy.
Returns:
new copy.
void vl_kmeans_quantize ( VlKMeans *  self,
vl_uint32 *  assignments,
void *  distances,
void const *  data,
vl_size  numData 
)
Parameters:
selfKMeans object.
assignmentsdata to centers assignments.
distancesdata to closes center distance/
datadata to quantize.
numDatanumber of data points.
double vl_kmeans_refine_centers ( VlKMeans *  self,
void const *  data,
vl_size  numData 
)
Parameters:
selfKMeans object.
datadata to quantize.
numDatanumber of data points.
Returns:
K-means energy at the end of optimization.

The function calls the underlying K-means quantization algorithm (VlKMeansAlgorithm) to quantize the specified data data. The function assumes that the cluster centers have already been assigned by using one of the seeding functions, or by setting them.

void vl_kmeans_reset ( VlKMeans *  self)

The function reset the state of the KMeans object. It deletes any stored centers, releasing the corresponding memory. This cancels the effect of seeding or setting the centers, but does not change the other configuration parameters.

void vl_kmeans_seed_centers_plus_plus ( VlKMeans *  self,
void const *  data,
vl_size  dimension,
vl_size  numData,
vl_size  numCenters 
)
Parameters:
selfKMeans object.
datadata to sample from.
dimensiondata dimension.
numDatanmber of data points.
numCentersnumber of centers.
void vl_kmeans_seed_centers_with_rand_data ( VlKMeans *  self,
void const *  data,
vl_size  dimension,
vl_size  numData,
vl_size  numCenters 
)
Parameters:
selfKMeans object.
datadata to sample from.
dimensiondata dimension.
numDatanmber of data points.
numCentersnumber of centers.

The function seeds the KMeans centers by randomly sampling the data data.

void vl_kmeans_set_algorithm ( VlKMeans *  self,
VlKMeansAlgorithm  algorithm 
) [inline]
Parameters:
selfKMeans object.
algorithmK-means algorithm.
void vl_kmeans_set_centers ( VlKMeans *  self,
void const *  centers,
vl_size  dimension,
vl_size  numCenters 
)
Parameters:
selfKMeans object.
centerscenters to copy.
dimensiondata dimension.
numCentersnumber of centers.
void vl_kmeans_set_initialization ( VlKMeans *  self,
VlKMeansInitialization  initialization 
) [inline]
Parameters:
selfKMeans object.
initializationinitialization.
void vl_kmeans_set_max_num_iterations ( VlKMeans *  self,
vl_size  maxNumIterations 
) [inline]
Parameters:
selfKMeans filter.
maxNumIterationsmaximum number of iterations.
void vl_kmeans_set_num_repetitions ( VlKMeans *  self,
vl_size  numRepetitions 
) [inline]
Parameters:
selfKMeans object instance.
numRepetitionsmaximum number of repetitions. The number of repetitions cannot be smaller than 1.
void vl_kmeans_set_verbosity ( VlKMeans *  self,
int  verbosity 
) [inline]
Parameters:
selfKMeans object instance.
verbosityverbosity level.
 
vlfeat-0.9.14/doc/api/lbp_8c.html000066400000000000000000000262311167540017400164520ustar00rootroot00000000000000 VLFeat - Documentation - C API
Documentation - C API
Functions
lbp.c File Reference

Local Binary Patterns - Definition. More...

#include "lbp.h"
#include "mathop.h"

Functions

VlLbp * vl_lbp_new (VlLbpMappingType type, vl_bool transposed)
 Create a new LBP object.
void vl_lbp_delete (VlLbp *self)
 Delete VlLbp object.
void vl_lbp_process (VlLbp *self, float *features, float *image, vl_size width, vl_size height, vl_size cellSize)
 Extract LBP features.

Detailed Description

Author:
Andrea Vedaldi

Function Documentation

void vl_lbp_delete ( VlLbp *  self)
Parameters:
selfobject to delete.
VlLbp* vl_lbp_new ( VlLbpMappingType  type,
vl_bool  transposed 
)
Parameters:
typetype of LBP features.
transposedif true, then transpose each LBP pattern.
Returns:
new VlLbp object instance.
void vl_lbp_process ( VlLbp *  self,
float *  features,
float *  image,
vl_size  width,
vl_size  height,
vl_size  cellSize 
)
Parameters:
selfLBP object.
featuresbuffer to write the features to.
imageimage.
widthimage width.
heightimage height.
cellSizesize of the LBP cells.

features is a numColumns x numRows x dimension where dimension is the dimension of a LBP feature obtained from vl_lbp_get_dimension, numColumns is equal to floor(width / cellSize), and similarly for numRows.

 
vlfeat-0.9.14/doc/api/lbp_8h.html000066400000000000000000000451741167540017400164660ustar00rootroot00000000000000 VLFeat - Documentation - C API
Documentation - C API
Data Structures | Enumerations | Functions
lbp.h File Reference

Local Binary Patterns. More...

#include "generic.h"

Data Structures

struct  VlLbp
 Local Binary Pattern extractor. More...

Enumerations

enum  VlLbpMappingType { VlLbpUniform }
 Type of quantization for LBP features. More...

Functions

VlLbp * vl_lbp_new (VlLbpMappingType type, vl_bool transposed)
 Create a new LBP object.
void vl_lbp_delete (VlLbp *self)
 Delete VlLbp object.
void vl_lbp_process (VlLbp *self, float *features, float *image, vl_size width, vl_size height, vl_size cellSize)
 Extract LBP features.
vl_size vl_lbp_get_dimension (VlLbp *self)
 Get the dimension of the LBP histograms.

Detailed Description

VlLbp implements the local binary pattern features.

An LBP feature [1] is a l2-normalized local histogram of quantized local binary patterns (LBP).

Local Binary Patterns

A LBP is a string of bit obtained by binarizing a local neighbourhood of pixels with respect to the brightness of the central pixel. VlLbp implements only the case of 3x3 pixel neighbourhoods (this setting perform best in applications). Thus the LBP at location $ (x,y) $ is a string of eight bits. Each bit is equal to one if the corresponding pixel is brighter than the central one. Pixels are scanned starting from the one to the right in anti-clockwise sense. For example the first bit is one if, and only if, $ I(x+1,y) > I(x,y), and the second bit if, and only if, $ I(x+1,y-1) > I(x,y).

Quantized LBP

For a 3x3 neighborhood, an LBP is a string of eight bits and so there are 256 possible LBPs. These are usually too many for a reliable statistics (histogram) to be computed. Therefore the 256 patterns are further quantized into a smaller number of patterns according to one of the following rules:

  • Uniform (VlLbpUniform) There is one quantized pattern for each LBP that has exactly a transitions from 0 to 1 and one from 1 to 0 when scanned in anti-clockwise order, plus one quantized pattern comprising the two uniform LBPs, and one quantized pattern comprising all the other LBPs. This yields a total of 58 quantized patterns.

The number of quantized LBPs, which depends on the quantization type, can be obtained by vl_lbp_get_dimension.

Histograms of LBPs

The quantized LBP patterns are further grouped into local histograms. The image is divided into a number of cells of a prescribed size (cellSize). Then the quantized LBPs are aggregated into histogram by using bilinear interpolation along the two spatial dimensions (similar to HOG and SIFT).

Usage

First, one creates a VlLbp object instance by specifying the type of quantization (this initializes some internal tables to speedup the computation). Then, one obtains all the quantized LBPs histograms by calling vl_lbp_process. This function expects as input a buffer large enough to contain the computed features. If the image has size width x height, there are exactly floor(width/cellSize) x floor(height/cellSize) cells, each of which has a histogram of LBPs of size dimension (as returned by vl_lbp_get_dimension). Thus the required buffer has size floor(width/cellSize) x floor(height/cellSize) x dimension.

VlLbp supports computing transposed LPBs as well. A transposed LBP is the LBP obtained by transposing the underlying image patch (regarded as a matrix). This can be useful to compute the features when the image is column major rather than row major.

References

[1] T. Ojala, M. Pietikainen, and M. Maenpaa. "Multiresolution gray-scale and rotation invariant texture classification with local binary patterns". PAMI, 2010.

Author:
Andrea Vedaldi

Enumeration Type Documentation

enum VlLbpMappingType
Enumerator:
VlLbpUniform 

Uniform patterns

Function Documentation

void vl_lbp_delete ( VlLbp *  self)
Parameters:
selfobject to delete.
vl_size vl_lbp_get_dimension ( VlLbp *  self) [inline]
Returns:
dimension of the LBP histograms. The dimension depends on the type of quantization used.
See also:
vl_lbp_new().
VlLbp* vl_lbp_new ( VlLbpMappingType  type,
vl_bool  transposed 
)
Parameters:
typetype of LBP features.
transposedif true, then transpose each LBP pattern.
Returns:
new VlLbp object instance.
void vl_lbp_process ( VlLbp *  self,
float *  features,
float *  image,
vl_size  width,
vl_size  height,
vl_size  cellSize 
)
Parameters:
selfLBP object.
featuresbuffer to write the features to.
imageimage.
widthimage width.
heightimage height.
cellSizesize of the LBP cells.

features is a numColumns x numRows x dimension where dimension is the dimension of a LBP feature obtained from vl_lbp_get_dimension, numColumns is equal to floor(width / cellSize), and similarly for numRows.

 
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Glossary
  • Column-major. A M x N matrix A is stacked with column-major order as the sequence $(A_{11}, A_{21}, \dots, A_{12}, \dots)$. More in general, when stacking a multi dimensional array this indicates that the first index is the one varying most quickly, with the other followed in the natural order.
  • Opaque structure. A structure is opaque if the user is not supposed to access its member directly, but through appropriate interface functions. Opaque structures are commonly used to define objects.
  • Row-major. A M x N matrix A is stacked with row-major order as the sequence $(A_{11}, A_{12}, \dots, A_{21}, \dots)$. More in general, when stacking a multi dimensional array this indicates that the last index is the one varying most quickly, with the other followed in reverse order.
  • Feature frame. A feature frame is the geometrical description of a visual features. For instance, the frame of a SIFT feature is oriented disk and the frame of MSER feature is either a compact and connected set or a disk.
  • Feature descriptor. A feature descriptor is a quantity (usually a vector) which describes compactly the appearance of an image region (usually corresponding to a feature frame).
 
vlfeat-0.9.14/doc/api/mathop_8c.html000066400000000000000000000106071167540017400171650ustar00rootroot00000000000000 VLFeat - Documentation - C API
Documentation - C API
mathop.c File Reference

Math operations - Definition. More...

#include "mathop.h"
#include "mathop_sse2.h"
#include <math.h>
#include "mathop.c"

Detailed Description

Author:
Andrea Vedaldi
 
vlfeat-0.9.14/doc/api/mathop_8h.html000066400000000000000000002033421167540017400171720ustar00rootroot00000000000000 VLFeat - Documentation - C API
Documentation - C API
Defines | Typedefs | Enumerations | Functions | Variables
mathop.h File Reference

Math operations. More...

#include "generic.h"
#include <math.h>

Defines

#define VL_LOG_OF_2   0.693147180559945
 Logarithm of 2 (math constant)
#define VL_PI   3.141592653589793
 Pi (math constant)
#define VL_EPSILON_F   1.19209290E-07F
 IEEE single precision epsilon (math constant)
#define VL_EPSILON_D   2.220446049250313e-16
 IEEE double precision epsilon (math constant)
#define VL_NAN_F   (vl_nan_f.value)
 IEEE single precision NaN (not signaling)
#define VL_INFINITY_F   (vl_infinity_f.value)
 IEEE single precision positive infinity (not signaling)
#define VL_NAN_D   (vl_nan_d.value)
 IEEE double precision NaN (not signaling)
#define VL_INFINITY_D   (vl_infinity_d.value)
 IEEE double precision positive infinity (not signaling)

Typedefs

typedef float(* VlFloatVectorComparisonFunction )(vl_size dimension, float const *X, float const *Y)
 Pointer to a function to compare vectors of floats.
typedef double(* VlDoubleVectorComparisonFunction )(vl_size dimension, double const *X, double const *Y)
 Pointer to a function to compare vectors of doubles.
typedef enum
_VlVectorComparisonType 		VlVectorComparisonType
 Vector comparison types.

Enumerations

enum  _VlVectorComparisonType {
  VlDistanceL1, VlDistanceL2, VlDistanceChi2, VlDistanceHellinger,
  VlDistanceJS, VlKernelL1, VlKernelL2, VlKernelChi2,
  VlKernelHellinger, VlKernelJS
}
 Vector comparison types. More...

Functions

float vl_mod_2pi_f (float x)
 Fast mod(x, 2 * VL_PI)
double vl_mod_2pi_d (double x)
 Fast mod(x, 2 * VL_PI)
long int vl_floor_f (float x)
 Floor and convert to integer.
long int vl_floor_d (double x)
 Floor and convert to integer.
long int vl_ceil_f (float x)
 Ceil and convert to integer.
long int vl_ceil_d (double x)
 Ceil and convert to integer.
long int vl_round_f (float x)
 Round.
long int vl_round_d (double x)
 Round.
float vl_abs_f (float x)
 Fast abs(x)
double vl_abs_d (double x)
 Fast abs(x)
float vl_fast_atan2_f (float y, float x)
 Fast atan2 approximation.
double vl_fast_atan2_d (double y, double x)
 Fast atan2 approximation.
float vl_fast_resqrt_f (float x)
 Fast resqrt approximation.
double vl_fast_resqrt_d (double x)
 Fast resqrt approximation.
float vl_fast_sqrt_f (float x)
 Fast sqrt approximation.
double vl_fast_sqrt_d (float x)
 Fast sqrt approximation.
vl_uint32 vl_fast_sqrt_ui32 (vl_uint32 x)
 Fast sqrt approximation.
vl_uint16 vl_fast_sqrt_ui16 (vl_uint16 x)
 Fast sqrt approximation.
vl_uint8 vl_fast_sqrt_ui8 (vl_uint8 x)
 Fast sqrt approximation.
char const * vl_get_vector_comparison_type_name (int type)
 Get the symbolic name of a vector comparison type.
VlFloatVectorComparisonFunction vl_get_vector_comparison_function_f (VlVectorComparisonType type)
 Get vector comparison function from comparison type.
VlDoubleVectorComparisonFunction vl_get_vector_comparison_function_d (VlVectorComparisonType type)
 Get vector comparison function from comparison type.
void vl_eval_vector_comparison_on_all_pairs_f (float *result, vl_size dimension, float const *X, vl_size numDataX, float const *Y, vl_size numDataY, VlFloatVectorComparisonFunction function)
 Evaluate vector comparison function on all vector pairs.
void vl_eval_vector_comparison_on_all_pairs_d (double *result, vl_size dimension, double const *X, vl_size numDataX, double const *Y, vl_size numDataY, VlDoubleVectorComparisonFunction function)
 Evaluate vector comparison function on all vector pairs.

Variables

union {
} vl_nan_f
 IEEE single precision quiet NaN constant.
union {
} vl_infinity_f
 IEEE single precision infinity constant.
union {
} vl_nan_d
 IEEE double precision quiet NaN constant.
union {
} vl_infinity_d
 IEEE double precision infinity constant.

Detailed Description

Comparing vectors

mathop.h includes a number of functions to quickly compute distances or similarity of pairs of vector. Applications include clustering and evaluation of SVM-like classifiers.

Use vl_get_vector_comparison_function_f or vl_get_vector_comparison_function_d obtain an approprite function to comprare vectors of floats or doubles, respectively. Such functions are usually optimized (for instance, on X86 platforms they use the SSE vector extension) and are several times faster than a naive implementation. vl_eval_vector_comparison_on_all_pairs_f and vl_eval_vector_comparison_on_all_pairs_d can be used to evaluate the comparison function on all pairs of one or two sequences of vectors.

Let $ \mathbf{x} = (x_1,\dots,x_d) $ and $ \mathbf{y} = (y_1,\dots,y_d) $ be two vectors. The following comparison functions are supported:

$ l^1 $ VlDistanceL1 $ \sum_{i=1}^d |x_i - y_i| $ l1 distance (squared intersection metric)
$ l^2 $ VlDistanceL2 $\sum_{i=1}^d (x_i - y_i)^2$ Squared Euclidean disance
$ \chi^2 $ VlDistanceChi2 $\sum_{i=1}^d \frac{(x_i - y_i)^2}{x_i + y_i}$ Squared chi-square distance
- VlDistanceHellinger $\sum_{i=1}^d (\sqrt{x_i} - \sqrt{y_i})^2$ Squared Hellinger's distance
- VlDistanceJS $ \sum_{i=1}^d \left( x_i \log\frac{2x_i}{x_i+y_i} + y_i \log\frac{2y_i}{x_i+y_i} \right) $ Squared Jensen-Shannon distance
$ l^1 $ VlKernelL1 $ \sum_{i=1}^d \min\{ x_i, y_i \} $ intersection kernel
$ l^2 $ VlKernelL2 $\sum_{i=1}^d x_iy_i $ linear kernel
$ \chi^2 $ VlKernelChi2 $\sum_{i=1}^d 2 \frac{x_iy_i}{x_i + y_i}$ chi-square kernel
- VlKernelHellinger $\sum_{i=1}^d 2 \sqrt{x_i y_i}$ Hellinger's kernel (Bhattacharya coefficient)
- VlKernelJS $ \sum_{i=1}^d \left( \frac{x_i}{2} \log_2\frac{x_i+y_i}{x_i} + \frac{y_i}{2} \log_2\frac{x_i+y_i}{y_i} \right) $ Jensen-Shannon kernel
Remarks:
The definitions have been choosen so that corresponding kernels and distances are related by the equation:

\[ d^2(\mathbf{x},\mathbf{y}) = k(\mathbf{x},\mathbf{x}) +k(\mathbf{y},\mathbf{y}) -k(\mathbf{x},\mathbf{y}) -k(\mathbf{y},\mathbf{x}) \]

This means that each of these distances can be interpreted as a squared distance or metric in the corresponding reproducing kernel Hilbert space. Notice in particular that the $ l^1 $ or Manhattan distance is also a squared distance in this sense.
Author:
Andrea Vedaldi

Define Documentation

#define VL_EPSILON_D   2.220446049250313e-16

1.0 + VL_EPSILON_D is the smallest representable double precision number greater than 1.0. Numerically, VL_EPSILON_D is equal to $ 2^{-52} $.

#define VL_EPSILON_F   1.19209290E-07F

1.0F + VL_EPSILON_F is the smallest representable single precision number greater than 1.0F. Numerically, VL_EPSILON_F is equal to $ 2^{-23} $.

Enumeration Type Documentation

enum _VlVectorComparisonType
Enumerator:
VlDistanceL1 

l1 distance (squared intersection metric)

VlDistanceL2 

squared l2 distance

VlDistanceChi2 

squared Chi2 distance

VlDistanceHellinger 

squared Hellinger's distance

VlDistanceJS 

squared Jensen-Shannon distance

VlKernelL1 

intersection kernel

VlKernelL2 

l2 kernel

VlKernelChi2 

Chi2 kernel

VlKernelHellinger 

Hellinger's kernel

VlKernelJS 

Jensen-Shannon kernel

Function Documentation

double vl_abs_d ( double  x) [inline]
See also:
vl_abs_f
float vl_abs_f ( float  x) [inline]
Parameters:
xargument.
Returns:
abs(x)
long int vl_ceil_d ( double  x) [inline]
See also:
vl_ceil_f
long int vl_ceil_f ( float  x) [inline]
Parameters:
xargument.
Returns:
lceilf(x)
vl_eval_vector_comparison_on_all_pairs_d ( double *  result,
vl_size  dimension,
double const *  X,
vl_size  numDataX,
double const *  Y,
vl_size  numDataY,
VlDoubleVectorComparisonFunction  function 
)
See also:
vl_eval_vector_comparison_on_all_pairs_f
vl_eval_vector_comparison_on_all_pairs_f ( float *  result,
vl_size  dimension,
float const *  X,
vl_size  numDataX,
float const *  Y,
vl_size  numDataY,
VlFloatVectorComparisonFunction  function 
)
Parameters:
resultcomparison matrix (output).
dimensionnumber of vector components (rows of X and Y).
Xdata matrix X.
Ydata matrix Y.
numDataXnumber of vectors in X (columns of X)
numDataYnumber of vectros in Y (columns of Y)
functionvector comparison function.

The function evaluates function on all pairs of columns from matrices X and Y, filling a numDataX by numDataY matrix.

If Y is a null pointer the function compares all columns from X with themselves.

double vl_fast_atan2_d ( double  y,
double  x 
) [inline]
See also:
vl_fast_atan2_f
float vl_fast_atan2_f ( float  y,
float  x 
) [inline]
Parameters:
yargument.
xargument.

The function computes a relatively rough but fast approximation of atan2(y,x).

Algorithm

The algorithm approximates the function $ f(r)=atan((1-r)/(1+r)) $, $ r \in [-1,1] $ with a third order polynomial $ f(r)=c_0 + c_1 r + c_2 r^2 + c_3 r^3 $. To fit the polynomial we impose the constraints

\begin{eqnarray*} f(+1) &=& c_0 + c_1 + c_2 + c_3 = atan(0) = 0,\\ f(-1) &=& c_0 - c_1 + c_2 - c_3 = atan(\infty) = \pi/2,\\ f(0) &=& c_0 = atan(1) = \pi/4. \end{eqnarray*}

The last degree of freedom is fixed by minimizing the $ l^{\infty} $ error, which yields

\[ c_0=\pi/4, \quad c_1=-0.9675, \quad c_2=0, \quad c_3=0.1821, \]

with maximum error of 0.0061 radians at 0.35 degrees.

Returns:
Approximation of atan2(y,x).
double vl_fast_resqrt_d ( double  x) [inline]
See also:
vl_fast_resqrt_d
float vl_fast_resqrt_f ( float  x) [inline]
Parameters:
xargument.
Returns:
approximation of resqrt(x).

The function quickly computes an approximation of $ x^{-1/2} $.

Algorithm

The goal is to compute $ y = x^{-1/2} $, which we do by finding the solution of $ 0 = f(y) = y^{-2} - x $ by two Newton steps. Each Newton iteration is given by

\[ y \leftarrow y - \frac{f(y)}{\frac{df(y)}{dy}} = y + \frac{1}{2} (y-xy^3) = \frac{y}{2} \left( 3 - xy^2 \right) \]

which yields a simple polynomial update rule.

The clever bit (attributed to either J. Carmack or G. Tarolli) is the way an initial guess $ y \approx x^{-1/2} $ is chosen.

See also:
Inverse Sqare Root.
double vl_fast_sqrt_d ( float  x) [inline]
See also:
vl_fast_sqrt_f
float vl_fast_sqrt_f ( float  x) [inline]
Parameters:
xargument.
Returns:
approximation of sqrt(x).

The function computes a cheap approximation of sqrt(x).

Floating-point algorithm

For the floating point cases, the function uses vl_fast_resqrt_f (or vl_fast_resqrt_d) to compute x * vl_fast_resqrt_f(x).

Integer algorithm

We seek for the largest integer y such that $ y^2 \leq x $. Write $ y = w + b_k 2^k + z $ where the binary expansion of the various variable is

\[ x = \sum_{i=0}^{n-1} 2^i a_i, \qquad w = \sum_{i=k+1}^{m-1} 2^i b_i, \qquad z = \sum_{i=0}^{k-1} 2^i b_i. \]

Assume w known. Expanding the square and using the fact that $ b_k^2=b_k $, we obtain the following constraint for $ b_k $ and z:

\[ x - w^2 \geq 2^k ( 2 w + 2^k ) b_k + z (z + 2wz + 2^{k+1}z b_k) \]

A necessary condition for $ b_k = 1 $ is that this equation is satisfied for $ z = 0 $ (as the second term is always non-negative). In fact, this condition is also sufficient, since we are looking for the largest solution y.

This yields the following iterative algorithm. First, note that if x is stored in n bits, where n is even, then the integer square root does not require more than $ m = n / 2 $ bit to be stored. Thus initially, $ w = 0 $ and $ k = m - 1 = n/2 - 1 $. Then, at each iteration the equation is tested, determining $ b_{m-1}, b_{m-2}, ... $ in this order.

vl_uint16 vl_fast_sqrt_ui16 ( vl_uint16  x) [inline]
See also:
vl_fast_sqrt_f
vl_uint32 vl_fast_sqrt_ui32 ( vl_uint32  x) [inline]
See also:
vl_fast_sqrt_f
vl_uint8 vl_fast_sqrt_ui8 ( vl_uint8  x) [inline]
See also:
vl_fast_sqrt_f
long int vl_floor_d ( double  x) [inline]
See also:
vl_floor_f
long int vl_floor_f ( float  x) [inline]
Parameters:
xargument.
Returns:
Similar to (int) floor(x)
vl_get_vector_comparison_function_d ( VlVectorComparisonType  type)
See also:
vl_get_vector_comparison_function_f
vl_get_vector_comparison_function_f ( VlVectorComparisonType  type)
Parameters:
typevector comparison type.
Returns:
comparison function.
char const* vl_get_vector_comparison_type_name ( int  type) [inline]
Parameters:
typevector comparison type.
Returns:
data symbolic name.
double vl_mod_2pi_d ( double  x) [inline]
See also:
vl_mod_2pi_f
float vl_mod_2pi_f ( float  x) [inline]
Parameters:
xinput value.
Returns:
mod(x, 2 * VL_PI)

The function is optimized for small absolute values of x.

The result is guaranteed to be not smaller than 0. However, due to finite numerical precision and rounding errors, the result can be equal to 2 * VL_PI (for instance, if x is a very small negative number).

long int vl_round_d ( double  x) [inline]
Parameters:
xargument.
Returns:
lround(x) This function is either the same or similar to C99 lround().
long int vl_round_f ( float  x) [inline]
Parameters:
xargument.
Returns:
lroundf(x) This function is either the same or similar to C99 lroundf().

Variable Documentation

union { ... } vl_infinity_d [static]
union { ... } vl_infinity_f [static]
union { ... } vl_nan_d [static]
union { ... } vl_nan_f [static]
 
vlfeat-0.9.14/doc/api/mathop__sse2_8c.html000066400000000000000000000107651167540017400202650ustar00rootroot00000000000000 VLFeat - Documentation - C API
Documentation - C API
mathop_sse2.c File Reference

mathop for SSE2 - Definition More...

#include "generic.h"
#include "mathop.h"
#include "mathop_sse2.h"
#include <emmintrin.h>
#include "mathop_sse2.c"

Detailed Description

Author:
Andrea Vedaldi
 
vlfeat-0.9.14/doc/api/mathop__sse2_8h.html000066400000000000000000000102441167540017400202620ustar00rootroot00000000000000 VLFeat - Documentation - C API
Documentation - C API
mathop_sse2.h File Reference

mathop for sse2 More...

#include "mathop_sse2.h"

Detailed Description

Author:
Andrea Vedaldi
 
vlfeat-0.9.14/doc/api/mexutils_8h.html000066400000000000000000001433551167540017400175630ustar00rootroot00000000000000 VLFeat - Documentation - C API
Documentation - C API
Data Structures | Defines | Functions
mexutils.h File Reference

MEX utilities. More...

#include "mex.h"
#include <vl/generic.h>
#include <vl/array.h>
#include <ctype.h>
#include <string.h>
#include <stdio.h>
#include <stdarg.h>

Data Structures

struct  vlmxOption
 MEX option. More...

Defines

#define IN(x)   (in[IN_ ## x])
 Access MEX input argument.
#define OUT(x)   (out[OUT_ ## x])
 Access MEX output argument.
#define VL_USE_MATLAB_ENV
 Setup VLFeat to be used in a MEX file.

Functions

static mxArray * vlmxCreatePlainScalar (double x)
 Create a MATLAB array which is a plain scalar.
static mxArray * vlmxCreateArrayFromVlArray (VlArray const *x)
 Create a MATLAB array from a VlArray.
static VlArray * vlmxEnvelopeArrayInVlArray (VlArray *v, mxArray *x)
 Envelope a MATLAB array in a VlArray instance.
static int uStrICmp (const char *s1, const char *s2)
 Case insensitive string comparison.
static int vlmxNextOption (mxArray const *args[], int nargs, vlmxOption const *options, int *next, mxArray const **optarg)
 Parse the next option.
Check for array attributes
vl_bool vlmxIsOfClass (mxArray const *array, mxClassID classId)
 Check if a MATLAB array is of a prescribed class.
vl_bool vlmxIsReal (mxArray const *array)
 Check if a MATLAB array is real.
Check for scalar, vector and matrix arrays
vl_bool vlmxIsScalar (mxArray const *array)
 Check if a MATLAB array is scalar.
static vl_bool vlmxIsVector (mxArray const *array, vl_index numElements)
 Check if a MATLAB array is a vector.
static vl_bool vlmxIsMatrix (mxArray const *array, vl_index M, vl_index N)
 Check if a MATLAB array is a matrix.
static vl_bool vlmxIsArray (mxArray const *array, vl_index numDimensions, vl_index *dimensions)
 Check if the MATLAB array has the specified dimensions.
Check for plain arrays
vl_bool vlmxIsPlain (mxArray const *array)
 Check if a MATLAB array is plain.
vl_bool vlmxIsPlainScalar (mxArray const *array)
 Check if a MATLAB array is plain scalar.
vl_bool vlmxIsPlainVector (mxArray const *array, vl_index numElements)
 Check if a MATLAB array is a plain vector.
vl_bool vlmxIsPlainMatrix (mxArray const *array, vl_index M, vl_index N)
 Check if a MATLAB array is a plain matrix.
static int vlmxIsString (const mxArray *array, vl_index length)
 Check if the array is a string.

Error handling

enum  VlmxErrorId
 VLFeat MEX errors.
static void vlmxError (VlmxErrorId errorId, char const *errorMessage,...)
 Raise a MEX error with VLFeat format.
static void vlmxWarning (VlmxErrorId errorId, char const *errorMessage,...)
 Raise a MEX warning with VLFeat format.

Detailed Description

Author:
Andrea Vedaldi

This header file provides helper functions for writing MATLAB MEX files.

VLFeat environment

When the VLFeat DLL is linked to a MATLAB MEX files, at run time the MEX file must configure VLFeat to use MATLAB memory allocation and logging functions. This can be obtained by calling the macro VL_USE_MATLAB_ENV as the first line of each MEX file which is linked to the VLFeat DLL.

Array tests

MATLAB supports a variety of array types. Most MEX file arguments are restricted to a few types and must be properly checked at run time. mexutils.h provides some helper functions to make it simpler to check such arguments. MATLAB basic array types are:

  • Numeric array: mxDOUBLE_CLASS, mxSINGLE_CLASS, mxINT8_CLASS, mxUINT8_CLASS, mxINT16_CLASS, mxUINT16_CLASS, mxINT32_CLASS, mxUINT32_CLASS. Moreover:
    • all such types have a real component
    • all such types may have a imaginary component
    • mxDOUBLE_LCASS arrays with two dimensions can be sparse.
  • Logical array (mxLOGICAL_CLASS).
  • Character array (mxCHAR_CLASS).

The other MATLAB array types are:

  • Struct array (mxSTRUCT_CLASS).
  • Cell array (mxCELL_CLASS).
  • Custom class array (mxCLASS_CLASS).
  • Unkown type array (mxUNKNOWN_CLASS).

VLFeat defines a number of common classes of arrays and corresponding tests.

  • Scalar array is a non-sparse array with exactly one element. Note that the array may have an arbitrary number of dimensions, and be of any numeric or other type. All dimensions are singleton (which is implied by having exactly one element). Use vlmxIsScalar to test if an array is scalar.
  • Vector array is a non-sparse array which is either empty (empty vector) or has at most one non-singleton dimension. The array can be of any numeric or other type. The elements of such a MATLAB array are stored as a plain C array with a number of elements equal to the number of elements in the array (obtained with mxGetNumberOfElements). Use vlmxIsVector to test if an array is a vector.
  • Matrix array is a non-sparse array for which all dimensions beyond the first two are singleton, or a non-sparse array which is empty and for which at least one of the first two dimensions is zero. The array can be of any numeric or other type. The non-singleton dimensions can be zero (empty matrix), one, or more. The element of such a MATLAB array are stored as a C array in column major order and its dimensions can be obtained by mxGetM and mxGetN. Use vlmxIsMatrix to test if an array is a matrix.
  • Real array is a numeric array (as for mxIsNumeric) without a complex component. Use vlmxIsReal to check if an array is real.
  • Use vlmxIsOfClass to check if an array is of a prescribed (storage) class, such as mxDOUBLE_CLASS.

Parsing options

It is common to pass optional arguments to a MEX file as option type-value pairs. Here type is a string identifying the option and value is a MATLAB array specifing its value. The function vlmxNextOption can be used to simplify parsing a list of such arguments (similar to UNIX getopt). The functions vlmxError and vlmxWarning are shortcuts to specify VLFeat formatted errors.

Define Documentation

#define VL_USE_MATLAB_ENV
Value:
vl_set_alloc_func (mxMalloc, mxRealloc, mxCalloc, mxFree) ; \
  vl_set_printf_func ((printf_func_t)mexPrintf) ;

This makes VLFeat use MATLAB version of the memory allocation and logging functions.

Function Documentation

static int uStrICmp ( const char *  s1,
const char *  s2 
) [static]
Parameters:
s1first string.
s2second string.
Returns:
0 if the strings are equal, >0 if the first string is greater (in lexicographical order) and <0 otherwise.
static mxArray* vlmxCreateArrayFromVlArray ( VlArray const *  x) [static]
Parameters:
xVlArray instance.
Returns:
the new array.
static mxArray* vlmxCreatePlainScalar ( double  x) [static]
Parameters:
xscalar value.
Returns:
the new array.
static VlArray* vlmxEnvelopeArrayInVlArray ( VlArray *  v,
mxArray *  x 
) [static]
Parameters:
vVlArray instance (out)
xMATALB array.
Returns:
v.
static void vlmxError ( VlmxErrorId  errorId,
char const *  errorMessage,
  ... 
) [static]
Parameters:
errorIderror ID string.
errorMessageerror message C-style format string.
...format string arguments.

The function internally calls mxErrMsgTxtAndId, which causes the MEX file to abort.

static vl_bool vlmxIsArray ( mxArray const *  array,
vl_index  numDimensions,
vl_index *  dimensions 
) [static]
Parameters:
arrayarray to check.
numDimensionsnumber of dimensions.
dimensionsdimensions.
Returns:
true the test succeeds.

The test is true if numDimensions < 0. If not, it is false if the array has not numDimensions. Otherwise it is true is dimensions is NULL or if each entry of dimensions is either negative or equal to the corresponding array dimension.

static vl_bool vlmxIsMatrix ( mxArray const *  array,
vl_index  M,
vl_index  N 
) [static]
Parameters:
arrayMATLAB array.
Mnumber of rows (negative for any).
Nnumber of columns (negative for any).
Returns:
VL_TRUE if the array is a matrix of the prescribed size.
See also:
Array tests
vl_bool vlmxIsOfClass ( mxArray const *  array,
mxClassID  classId 
) [inline]
Parameters:
arrayMATLAB array.
classIdprescribed class of the array.
Returns:
VL_TRUE if the class is of the array is of the prescribed class.
See also:
Array tests
vl_bool vlmxIsPlain ( mxArray const *  array) [inline]
Parameters:
arrayMATLAB array.
Returns:
VL_TRUE if the array is plain.
See also:
Array tests
vl_bool vlmxIsPlainMatrix ( mxArray const *  array,
vl_index  M,
vl_index  N 
) [inline]
Parameters:
arrayMATLAB array.
Mnumber of rows (negative for any).
Nnumber of columns (negative for any).
Returns:
VL_TRUE if the array is a plain matrix of the prescribed size.
See also:
Array tests
vl_bool vlmxIsPlainScalar ( mxArray const *  array) [inline]
Parameters:
arrayMATLAB array.
Returns:
VL_TRUE if the array is plain scalar.
See also:
Array tests
vl_bool vlmxIsPlainVector ( mxArray const *  array,
vl_index  numElements 
) [inline]
Parameters:
arrayMATLAB array.
numElementsnumber of elements (negative for any).
Returns:
VL_TRUE if the array is a plain vecotr of the prescribed size.
See also:
Array tests
vl_bool vlmxIsReal ( mxArray const *  array) [inline]
Parameters:
arrayMATLAB array.
Returns:
VL_TRUE if the array is real.
See also:
Array tests
vl_bool vlmxIsScalar ( mxArray const *  array) [inline]
Parameters:
arrayMATLAB array.
Returns:
VL_TRUE if the array is scalar.
See also:
Array tests
static int vlmxIsString ( const mxArray *  array,
vl_index  length 
) [static]
Parameters:
arrayarray to test.
lengthstring length.
Returns:
true if the array is a string of the specified length

The array array satisfies the test if:

  • its storage class is CHAR;
  • it has two dimensions but only one row;
  • length < 0 or the array has length columns.
static vl_bool vlmxIsVector ( mxArray const *  array,
vl_index  numElements 
) [static]
Parameters:
arrayMATLAB array.
numElementsnumber of elements (negative for any).
Returns:
VL_TRUE if the array is a vecotr of the prescribed size.
See also:
Array tests
static int vlmxNextOption ( mxArray const *  args[],
int  nargs,
vlmxOption const *  options,
int *  next,
mxArray const **  optarg 
) [static]
Parameters:
argsMEX argument array.
nargsMEX argument array length.
optionsList of option definitions.
nextPointer to the next option (input and output).
optargPointer to the option optional argument (output).
Returns:
the code of the next option, or -1 if there are no more options.

The function parses the array args for options. args is expected to be a sequence alternating option names and option values, in the form of nargs instances of mxArray. The function then scans the option starting at position next in the array. The option name is matched (case insensitive) to the table of options options, a pointer to the option value is stored in optarg, next is advanced to the next option, and the option code is returned.

The function is typically used in a loop to parse all the available options. next is initialized to zero, and then the function is called until the special code -1 is returned.

If the option name cannot be matched to the available options, either because the option name is not a string array or because the name is unknown, the function exits the MEX file with an error.

static void vlmxWarning ( VlmxErrorId  errorId,
char const *  errorMessage,
  ... 
) [static]
Parameters:
errorIderror ID string.
errorMessageerror message C-style format string.
...format string arguments.

The function internally calls mxWarnMsgTxtAndId.

 
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Documentation - C API
vlfeat-0.9.14/doc/api/mser_8c.html000066400000000000000000000337421167540017400166500ustar00rootroot00000000000000 VLFeat - Documentation - C API
Documentation - C API
Functions
mser.c File Reference

MSER - Definition. More...

#include "mser.h"
#include <stdlib.h>
#include <string.h>
#include <assert.h>

Functions

void adv (int ndims, int const *dims, int *subs)
 Advance N-dimensional subscript.
vl_uint climb (VlMserReg *r, vl_uint idx)
 Climb the region forest to reach aa root.
VlMserFilt * vl_mser_new (int ndims, int const *dims)
 Create a new MSER filter.
void vl_mser_delete (VlMserFilt *f)
 Delete MSER filter.
void vl_mser_process (VlMserFilt *f, vl_mser_pix const *im)
 Process image.
void vl_mser_ell_fit (VlMserFilt *f)
 Fit ellipsoids.

Detailed Description

Author:
Andrea Vedaldi

Function Documentation

void adv ( int  ndims,
int const *  dims,
int *  subs 
) [inline]

The function increments by one the subscript subs indexing an array the ndims dimensions dims.

Parameters:
ndimsnumber of dimensions.
dimsdimensions.
subssubscript to advance.
vl_uint climb ( VlMserReg *  r,
vl_uint  idx 
) [inline]

The function climbs the regions forest r starting from the node idx to the corresponding root.

To speed-up the operation, the function uses the VlMserReg::shortcut field to quickly jump to the root. After the root is reached, all the used shortcut are updated.

Parameters:
rregions' forest.
idxstating node.
Returns:
index of the reached root.
void vl_mser_delete ( VlMserFilt *  f)

The function releases the MSER filter f and all its resources.

Parameters:
fMSER filter to be deleted.
void vl_mser_ell_fit ( VlMserFilt *  f)
Parameters:
fMSER filter.
See also:
Ellipsoids
VlMserFilt* vl_mser_new ( int  ndims,
int const *  dims 
)

Initializes a new MSER filter for images of the specified dimensions. Images are ndims -dimensional arrays of dimensions dims.

Parameters:
ndimsnumber of dimensions.
dimsdimensions.
void vl_mser_process ( VlMserFilt *  f,
vl_mser_pix const *  im 
)

The functions calculates the Maximally Stable Extremal Regions (MSERs) of image im using the MSER filter f.

The filter f must have been initialized to be compatible with the dimensions of im.

Parameters:
fMSER filter.
imimage data.
 
vlfeat-0.9.14/doc/api/mser_8h.html000066400000000000000000001371431167540017400166550ustar00rootroot00000000000000 VLFeat - Documentation - C API
Documentation - C API
Data Structures | Defines | Typedefs
mser.h File Reference

MSER (Maximally Stable Extremal Regions) More...

#include "generic.h"

Data Structures

struct  VlMserStats
 MSER filter statistics definition. More...
struct  VlMserReg
 MSER: basic region (declaration) More...
struct  VlMserExtrReg
 MSER: extremal region (declaration) More...
struct  VlMserFilt
 MSER filter. More...

Defines

#define VL_MSER_PIX_MAXVAL   256
 Maximum value.
#define VL_MSER_VOID_NODE   ((1ui64<<32) - 1)
 Basic region flag: null region.

Typedefs

typedef vl_uint8 vl_mser_pix
 MSER image data type.
typedef float vl_mser_acc
 MSER accumulator data type.

Functions

Construction and Destruction
VlMserFilt * vl_mser_new (int ndims, int const *dims)
 Create a new MSER filter.
void vl_mser_delete (VlMserFilt *f)
 Delete MSER filter.
Processing
void vl_mser_process (VlMserFilt *f, vl_mser_pix const *im)
 Process image.
void vl_mser_ell_fit (VlMserFilt *f)
 Fit ellipsoids.
Retrieving data
vl_uint vl_mser_get_regions_num (VlMserFilt const *f)
 Get number of maximally stable extremal regions.
vl_uint const * vl_mser_get_regions (VlMserFilt const *f)
 Get maximally stable extremal regions.
float const * vl_mser_get_ell (VlMserFilt const *f)
 Get ellipsoids.
vl_uint vl_mser_get_ell_num (VlMserFilt const *f)
 Get number of ellipsoids.
vl_uint vl_mser_get_ell_dof (VlMserFilt const *f)
 Get number of degrees of freedom of ellipsoids.
VlMserStats const * vl_mser_get_stats (VlMserFilt const *f)
 Get statistics.
Retrieving parameters
vl_mser_pix vl_mser_get_delta (VlMserFilt const *f)
 Get delta.
double vl_mser_get_min_area (VlMserFilt const *f)
 Get minimum region area.
double vl_mser_get_max_area (VlMserFilt const *f)
 Get maximum region area.
double vl_mser_get_max_variation (VlMserFilt const *f)
 Get maximum region variation.
double vl_mser_get_min_diversity (VlMserFilt const *f)
 Get minimum diversity.
Setting parameters
void vl_mser_set_delta (VlMserFilt *f, vl_mser_pix x)
 Set delta.
void vl_mser_set_min_area (VlMserFilt *f, double x)
 Set minimum region area.
void vl_mser_set_max_area (VlMserFilt *f, double x)
 Set maximum region area.
void vl_mser_set_max_variation (VlMserFilt *f, double x)
 Set maximum region variation.
void vl_mser_set_min_diversity (VlMserFilt *f, double x)
 Set minimum diversity.

Detailed Description

implements the Maximally Stable Extremal Regions (MSER) feature detetctor [7] .

Maximally Stable Extremal Regions Overview

Running the MSER filter usually involves the following steps:

MSER definition

An extremal region $R_l$ of an image is a connected component of the level set $S_l = \{ x : I(x) \leq l \}$.

mser-er.png

For each intensity $l$, one has multiple disjoint extremal regions in the level set $S_l$. Let $l$ span a finite number of values $\mathcal{L}=\{0,\dots,M-1\}$ (a sampling of the image range). One obtains a family of regions $R_l$; by connecting two regions $R_l$and $R_{l+1}$ if, and only if, $R_l\subset R_{l+1}$, regions form a tree:

mser-tree.png

The maximally stable extremal regions are extremal regions which satisfy a stability criterion. Here we use a criterion which is similar but not identical to the original paper. This definition is somewhat simpler both to understand and code (it also runs faster).

Let $B(R_l)=(R_l,R_{l+1},\dots,R_{l+\Delta})$ be the branch of the tree rooted at $R_l$. We associate to the branch the (in)stability score

\[ v(R_l) = \frac{|R_{l+\Delta} - R_l|}{|R_l|}. \]

The score is low if the regions along the branch have similar area (and thus similar shape). We aim to select maximally stable branches; then a maximally stable region is just a representative region selected from a maximally stable branch (for simplicity we select $R_l$, but one could choose for example $R_{l+\Delta/2}$).

Roughly speaking, a branch is maximally stable if it is a local minimum of the (in)stability score. More accurately, we start by assuming that all branches are maximally stable. Then we consider each branch $B(R_{l})$ and its parent branch $B(R_{l+1}):R_{l+1}\supset R_l$ (notice that, due to the discrete nature of the calculations, they might be geometrically identical) and we mark as unstable the less stable one, i.e.:

  • if $v(R_l)<v(R_{l+1})$, mark $R_{l+1}$ as unstable;
  • if $v(R_l)>v(R_{l+1})$, mark $R_{l}$ as unstable;
  • otherwise, do nothing.

This criterion selects among nearby regions the ones that are more stable. We optionally refine the selection by running (starting from the bigger and going to the smaller regions) the following tests:

  • $a_- \leq |R_{l}|/|R_{\infty}| \leq a_+$: exclude MSERs too small or too big ( $|R_{\infty}|$ is the area of the image).
  • $v(R_{l}) < v_+$: exclude MSERs too unstable.
  • For any MSER $R_l$, find the parent MSER $R_{l'}$ and check if $|R_{l'} - R_l|/|R_l'| < d_+$: remove duplicated MSERs.
parameter alt. name standard value set by
$\Delta$ delta 5 vl_mser_set_delta()
$a_+$ max_area 0.75 vl_mser_set_max_area()
$a_-$ min_area 3.0/ $|R_\infty|$ vl_mser_set_min_area()
$v_+$ max_var 0.25 vl_mser_set_max_variation()
$d_+$ min_diversity 0.2 vl_mser_set_min_diversity()

Volumetric images

The code supports images of arbitrary dimension. For instance, it is possible to find the MSER regions of volumetric images or time sequences. See vl_mser_new() for further details

Ellipsoids

Usually extremal regions are returned as a set of ellipsoids fitted to the actual regions (which have arbitrary shape). The fit is done by calculating the mean and variance of the pixels composing the region:

\[ \mu_l = \frac{1}{|R_l|}\sum_{x\in R_l}x, \qquad \Sigma_l = \frac{1}{|R_l|}\sum_{x\in R_l} (x-\mu_l)^\top(x-\mu_l) \]

Ellipsoids are fitted by vl_mser_ell_fit(). Notice that for a n dimensional image, the mean has n components and the variance has n(n+1)/2 independent components. The total number of components is obtained by vl_mser_get_ell_dof() and the total number of fitted ellipsoids by vl_mser_get_ell_num(). A matrix with an ellipsoid per column is returned by vl_mser_get_ell(). The column is the stacking of the mean and of the independent components of the variance, in the order (1,1),(1,2),..,(1,n), (2,2),(2,3).... In the calculations, the pixel coordinate $x=(x_1,...,x_n)$ use the standard index order and ranges.

Algorithm

The algorithm is quite efficient. While some details may be tricky, the overall idea is easy to grasp.

  • Pixels are sorted by increasing intensity.
  • Pixels are added to a forest by increasing intensity. The forest has the following properties:
    • All the descendent of a certain pixels are subset of an extremal region.
    • All the extremal regions are the descendants of some pixels.
  • Extremal regions are extracted from the region tree and the extremal regions tree is calculated.
  • Stable regions are marked.
  • Duplicates and other bad regions are removed.
Remarks:
The extremal region tree which is calculated is a subset of the actual extremal region tree. In particular, it does not contain redundant entries extremal regions that coincide as sets. So, for example, in the calculated extremal region tree, the parent $R_q$ of an extremal region $R_{l}$ may or may not correspond to $R_{l+1}$, depending whether $q\leq l+1$ or not. These subtleties are important when calculating the stability tests.
Author:
Andrea Vedaldi

Define Documentation

#define VL_MSER_PIX_MAXVAL   256

Maximum value of the integer type vl_mser_pix.

#define VL_MSER_VOID_NODE   ((1ui64<<32) - 1)

Typedef Documentation

typedef float vl_mser_acc

This is a large integer type. It should be large enough to contain a number equal to the area (volume) of the image by the image width by the image height (for instance, if the image is a square of side 256, the maximum value is 256 x 256 x 256).

typedef vl_uint8 vl_mser_pix

This is the data type of the image pixels. It has to be an integer.

Function Documentation

void vl_mser_delete ( VlMserFilt *  f)

The function releases the MSER filter f and all its resources.

Parameters:
fMSER filter to be deleted.
void vl_mser_ell_fit ( VlMserFilt *  f)
Parameters:
fMSER filter.
See also:
Ellipsoids
vl_mser_pix vl_mser_get_delta ( VlMserFilt const *  f) [inline]
Parameters:
fMSER filter.
Returns:
value of delta.
float const * vl_mser_get_ell ( VlMserFilt const *  f) [inline]
Parameters:
fMSER filter.
Returns:
ellipsoids.
vl_uint vl_mser_get_ell_dof ( VlMserFilt const *  f) [inline]
Parameters:
fMSER filter.
Returns:
number of degrees of freedom.
vl_uint vl_mser_get_ell_num ( VlMserFilt const *  f) [inline]
Parameters:
fMSER filter.
Returns:
number of ellipsoids
double vl_mser_get_max_area ( VlMserFilt const *  f) [inline]
Parameters:
fMSER filter.
Returns:
maximum region area.
double vl_mser_get_max_variation ( VlMserFilt const *  f) [inline]
Parameters:
fMSER filter.
Returns:
maximum region variation.
double vl_mser_get_min_area ( VlMserFilt const *  f) [inline]
Parameters:
fMSER filter.
Returns:
minimum region area.
double vl_mser_get_min_diversity ( VlMserFilt const *  f) [inline]
Parameters:
fMSER filter.
Returns:
value of minimum diversity.
vl_uint const * vl_mser_get_regions ( VlMserFilt const *  f) [inline]
Parameters:
fMSER filter.
Returns:
array of MSER pivots.
vl_uint vl_mser_get_regions_num ( VlMserFilt const *  f) [inline]
Parameters:
fMSER filter.
Returns:
number of MSERs.
VlMserStats const * vl_mser_get_stats ( VlMserFilt const *  f) [inline]
Parameters:
fMSER filter.
Returns:
statistics.
VlMserFilt* vl_mser_new ( int  ndims,
int const *  dims 
)

Initializes a new MSER filter for images of the specified dimensions. Images are ndims -dimensional arrays of dimensions dims.

Parameters:
ndimsnumber of dimensions.
dimsdimensions.
void vl_mser_process ( VlMserFilt *  f,
vl_mser_pix const *  im 
)

The functions calculates the Maximally Stable Extremal Regions (MSERs) of image im using the MSER filter f.

The filter f must have been initialized to be compatible with the dimensions of im.

Parameters:
fMSER filter.
imimage data.
void vl_mser_set_delta ( VlMserFilt *  f,
vl_mser_pix  x 
) [inline]
Parameters:
fMSER filter.
xvalue of delta.
void vl_mser_set_max_area ( VlMserFilt *  f,
double  x 
) [inline]
Parameters:
fMSER filter.
xmaximum region area.
void vl_mser_set_max_variation ( VlMserFilt *  f,
double  x 
) [inline]
Parameters:
fMSER filter.
xmaximum region variation.
void vl_mser_set_min_area ( VlMserFilt *  f,
double  x 
) [inline]
Parameters:
fMSER filter.
xminimum region area.
void vl_mser_set_min_diversity ( VlMserFilt *  f,
double  x 
) [inline]
Parameters:
fMSER filter.
xvalue of minimum diversity.
 
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vlfeat-0.9.14/doc/api/pegasos.html000066400000000000000000000273111167540017400167440ustar00rootroot00000000000000 VLFeat - Documentation - C API
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PEGASOS SVM solver
Author:
Andrea Vedaldi

pegasos.h provides a basic implementation of the PEGASOS [9] linear SVM solver.

Overview

PEGASOS solves the linear SxVM learning problem

\[ \min_{w} \frac{\lambda}{2} \|w\|^2 + \frac{1}{m} \sum_{i=1}^n \ell(w; (x_i,y_i)) \]

where $ x_i $ are data vectors in $ \mathbb{R}^d $, $ y_i \in \{-1,1\} $ are binary labels, $ \lambda > 0 $ is the regularization parameter, and

\[ \ell(w;(x_i,y_i)) = \max\{0, 1 - y_i \langle w,x_i\rangle\}. \]

is the hinge loss. The result of the optimization is a model $ w \in \mathbb{R}^d $ that yields the decision function

\[ F(x) = \operatorname{sign} \langle w, x\rangle. \]

It is well known that the hinge loss is a convex upper bound of the i01-loss of the decision function:

\[ \ell(w;(x,y)) \geq \frac{1}{2}(1 - y F(x)). \]

PEGASOS is accessed by calling vl_pegasos_train_binary_svm_d or vl_pegasos_train_binary_svm_f, operating respectively on double or float data.

Algorithm

PEGASOS [9] is a stochastic subgradient optimizer. At the t-th iteration the algorithm:

  • Samples uniformly at random as subset $ A_t$ of k of training pairs $(x,y)$ from the m pairs provided for training (this subset is called mini batch).
  • Computes a subgradient $ \nabla_t $ of the function $ E_t(w) = \frac{1}{2}\|w\|^2 + \frac{1}{k} \sum_{(x,y) \in A_t} \ell(w;(x,y)) $ (this is the SVM objective function restricted to the minibatch).
  • Compute an intermediate weight vector $ w_{t+1/2} $ by doing a step $ w_{t+1/2} = w_t - \alpha_t \nalba_t $ with learning rate $ \alpha_t = 1/(\eta t) $ along the subgradient. Note that the learning rate is inversely proportional to the iteration numeber.
  • Back projects the weight vector $ w_{t+1/2} $ on the hypersphere of radius $ \sqrt{\lambda} $ to obtain the next model estimate $ w_{t+1} $:

    \[ w_t = \min\{1, \sqrt{\lambda}/\|w\|\} w_{t+1/2}. \]

    The hypersfere is guaranteed to contain the optimal weight vector $ w^* $ [9] .

VLFeat implementation fixes to one the size of the mini batches $ k $.

Bias

PEGASOS SVM formulation does not incorporate a bias. To learn an SVM with bias, the each data vector $ x $ can be extended by a constant component $ B $ (called biasMultiplier in the code). In this case, the model $ w $ has dimension $ D + 1 $ and the SVM discriminat function is given by $ F(x) = \operatorname{sign} (\langle w_{1:d}, x\rangle+ w_{d+1} B) $. If the bias multiplier B is large enough, the weight $ w_{d+1} $ remains small and it has small contribution in the SVM regularization term $ \| w \|^2 $, better approximating the case of an SVM with bias. Unfortunately, setting the bias multiplier $ B $ to a large value makes the optimization harder.

Restarting

VLFeat PEGASOS implementation can be restatred after any given number of iterations. This is useful to compute intermediate statistics or to load new data from disk for large datasets. The state of the algorithm, which is required for restarting, is limited to the current estimate $ w_t $ of the SVM weight vector and the iteration number $ t $.

Permutation

VLFeat PEGASOS can use a user-defined permutation to decide the order in which data points are visited (instead of using random sampling). By specifying a permutation the algorithm is guaranteed to visit each data point exactly once in each loop. The permutation needs not to be bijective. This can be used to visit certain data samples more or less often than others, implicitly reweighting their relative importance in the SVM objective function. This can be used to blanace the data.

Non-linear kernels

PEGASOS can be extended to non-linear kernels, but the algorithm is not particularly efficient in this setting [1]. When possible, it may be preferable to work with explicit feature maps.

Let $ k(x,y) $ be a positive definite kernel. A feature map is a function $ \Psi(x) $ such that $ k(x,y) = \langle \Psi(x), \Psi(y) \rangle $. Using this representation the non-linear SVM learning objective function writes:

\[ \min_{w} \frac{\lambda}{2} \|w\|^2 + \frac{1}{m} \sum_{i=1}^n \ell(w; (\Psi(x)_i,y_i)). \]

Thus the only difference with the linear case is that the feature $ \Psi(x) $ is used in place of the data $ x $.

$ \Psi(x) $ can be learned off-line, for instance by using the incomplete Cholesky decomposition $ V^\top V $ of the Gram matrix $ K = [k(x_i,x_j)] $ (in this case $ \Psi(x_i) $ is the i-th columns of V). Alternatively, for additive kernels (e.g. intersection, Chi2) the explicit feature map computed by homkermap.h can be used.

 
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Documentation - C API
pegasos.c File Reference

PEGASOS - Definition. More...

#include "pegasos.h"
#include "mathop.h"
#include <math.h>
#include "pegasos.c"

Detailed Description

Author:
Andrea Vedaldi
 
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Functions
pegasos.h File Reference

PEGASOS (PEGASOS SVM solver) More...

#include "generic.h"

Functions

void vl_pegasos_train_binary_svm_d (double *model, double const *data, vl_size dimension, vl_size numSamples, vl_int8 const *labels, double regularizer, double biasMultiplier, vl_uindex startingIteration, vl_size numIterations, VlRand *randomGenerator, vl_uint32 const *permutation, vl_size permutationSize, double const *preconditioner)
void vl_pegasos_train_binary_svm_f (float *model, float const *data, vl_size dimension, vl_size numSamples, vl_int8 const *labels, double regularizer, double biasMultiplier, vl_uindex startingIteration, vl_size numIterations, VlRand *randomGenerator, vl_uint32 const *permutation, vl_size permutationSize, float const *preconditioner)

Detailed Description

Author:
Andrea Vedaldi

Function Documentation

vl_pegasos_train_binary_svm_d ( double *  model,
double const *  data,
vl_size  dimension,
vl_size  numSamples,
vl_int8 const *  labels,
double  regularizer,
double  biasMultiplier,
vl_uindex  startingIteration,
vl_size  numIterations,
VlRand *  randomGenerator,
vl_uint32 const *  permutation,
vl_size  permutationSize,
double const *  preconditioner 
)
Parameters:
model(out) the learned model.
datatraining vectors.
dimensiondata dimension.
numSamplesnumber of training data vectors.
labelslabels of the training vectors.
regularizervalue of the regularizer coefficient $ \lambda $.
biasMultipliervalue of the bias multiplier $ B $.
startingIterationnumber of the first iteration.
numIterationsnumber of iterations to perform.
randomGeneratorrandom number generator.
permutationorder in which the data is accessed.
permutationSizelength of permutation.
preconditionerdiagonal precoditioner.

The function runs PEGASOS on the specified data. The vector model must have either dimension equal to dimension if biasMultiplier is zero, or dimension + 1 if biasMultiplier is larger than zero.

The function runs PEGASOS for iterations t in the interval [fistIteration, lastIteration]. Together with the fact that the initial model can be set arbitrarily, this enable restarting PEGASOS from any point.

PEGASOS select the next point for computing the gradient at random. If randomGenerator is NULL, the default random generator (as returned by vl_get_rand()) is used.

Alternatively, if permutation is not NULL, then points are sampled in the order specified by this vector of indexes (this is cycled through). In this way It is an error to set both randomGenerator and permutation to non-null values.

preconditioner specifies a diagonal preconditioner for the minimization problem (it is often useful to slow down the steps for the bias term, if the latter is used). Set preconditioner to NULL to avoid using a preconditioner. The precodnitioner should have the same dimension of the model, plus one if an SVM with bias is learned.

See the Overview overview for details.

vl_pegasos_train_binary_svm_f ( float *  model,
float const *  data,
vl_size  dimension,
vl_size  numSamples,
vl_int8 const *  labels,
double  regularizer,
double  biasMultiplier,
vl_uindex  startingIteration,
vl_size  numIterations,
VlRand *  randomGenerator,
vl_uint32 const *  permutation,
vl_size  permutationSize,
float const *  preconditioner 
)
See also:
vl_pegasos_train_binary_svm_d
 
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Functions
pgm.c File Reference

Portable graymap format (PGM) parser - Definition. More...

#include "pgm.h"
#include <stdio.h>
#include <stdlib.h>
#include <string.h>

Functions

static int remove_line (FILE *f)
 Remove all characters to the next new-line.
static int remove_blanks (FILE *f)
 Remove white-spaces and comments.
int vl_pgm_get_npixels (VlPgmImage const *im)
 Get PGM image number of pixels.
int vl_pgm_get_bpp (VlPgmImage const *im)
 Get PGM image bytes per pixel.
int vl_pgm_extract_head (FILE *f, VlPgmImage *im)
 Extract PGM header from stream.
int vl_pgm_extract_data (FILE *f, VlPgmImage const *im, void *data)
 Extract PGM data from stream.
int vl_pgm_insert (FILE *f, VlPgmImage const *im, void const *data)
 Insert a PGM image into a stream.
int vl_pgm_read_new (char const *name, VlPgmImage *im, vl_uint8 **data)
 Read a PGM file.
int vl_pgm_read_new_f (char const *name, VlPgmImage *im, float **data)
 Read floats from a PGM file.
int vl_pgm_write (char const *name, vl_uint8 const *data, int width, int height)
 Write bytes to a PGM file.
int vl_pgm_write_f (char const *name, float const *data, int width, int height)
 Write floats to PGM file.

Detailed Description

Author:
Andrea Vedaldi

Function Documentation

static int remove_blanks ( FILE *  f) [static]
Parameters:
ffile to strip.
Returns:
number of characters removed.
static int remove_line ( FILE *  f) [static]
Parameters:
ffile to strip.
Returns:
number of characters removed.
int vl_pgm_extract_data ( FILE *  f,
VlPgmImage const *  im,
void *  data 
)
Parameters:
finput file.
imPGM image descriptor.
datadata buffer to fill.

The function extracts from the file f the data section of an image encoded in PGM format. The function fills the buffer data according. The buffer data should be vl_pgm_get_npixels() by vl_pgm_get_bpp() bytes large.

Returns:
error code.
int vl_pgm_extract_head ( FILE *  f,
VlPgmImage *  im 
)
Parameters:
finput file.
imimage structure to fill.

The function extracts from the file f the meta-data section of an image encoded in PGM format. The function fills the structure VlPgmImage accordingly.

Returns:
error code. The error may be either #VL_PGM_INV_HEAD or #VL_PGM_INV_META depending whether the error occurred in decoding the header or meta section of the PGM file.
int vl_pgm_get_bpp ( VlPgmImage const *  im)
Parameters:
imPGM image descriptor.

The function returns the number of bytes for each pixel of the PGM image im.

Returns:
number of bytes per pixel.
int vl_pgm_get_npixels ( VlPgmImage const *  im)
Parameters:
imPGM image descriptor.

The functions returns the number of pixels of the PGM image im.

To calculate the image data size in bytes, this value must be multiplied by the number of byte per pixels (see vl_pgm_get_bpp()).

Returns:
number of pixels of the image.
int vl_pgm_insert ( FILE *  f,
VlPgmImage const *  im,
void const *  data 
)
Parameters:
foutput file.
imPGM image meta-data.
dataimage data.
Returns:
error code.
int vl_pgm_read_new ( char const *  name,
VlPgmImage *  im,
vl_uint8 **  data 
)
Parameters:
namefile name.
ima pointer to the PGM image structure to fill.
dataa pointer to the pointer to the allocated buffer.

The function reads a PGM image from file name and initializes the structure im and the buffer data accordingly.

The ownership of the buffer data is transfered to the caller. data should be freed by means of vl_free().

Bug:
Only PGM files with 1 BPP are supported.
Returns:
error code.
int vl_pgm_read_new_f ( char const *  name,
VlPgmImage *  im,
float **  data 
)
Parameters:
namefile name.
ima pointer to the PGM image structure to fill.
dataa pointer to the pointer to the allocated buffer.

The function reads a PGM image from file name and initializes the structure im and the buffer data accordingly. The buffer data is an array of floats in the range [0, 1].

The ownership of the buffer data is transfered to the caller. data should be freed by means of vl_free().

Bug:
Only PGM files with 1 BPP are supported.
Returns:
error code.
int vl_pgm_write ( char const *  name,
vl_uint8 const *  data,
int  width,
int  height 
)
Parameters:
namefile name.
datadata to write.
widthwidth of the image.
heightheight of the image.

The function dumps the image data to the PGM file of the specified name. This is an helper function simplifying the usage of vl_pgm_insert().

Returns:
error code.
int vl_pgm_write_f ( char const *  name,
float const *  data,
int  width,
int  height 
)
Parameters:
namefile name.
datadata to write.
widthwidth of the image.
heightheight of the image.

The function dumps the image data to the PGM file of the specified name. The data is re-scaled to fit in the range 0-255. This is an helper function simplifying the usage of vl_pgm_insert().

Returns:
error code.
 
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Documentation - C API
Data Structures
pgm.h File Reference

Portable graymap format (PGM) parser. More...

#include "generic.h"
#include "mathop.h"
#include <stdio.h>

Data Structures

struct  VlPgmImage
 PGM image meta data. More...

Defines

PGM parser error codes
#define VL_ERR_PGM_INV_HEAD   101
#define VL_ERR_PGM_INV_META   102
#define VL_ERR_PGM_INV_DATA   103
#define VL_ERR_PGM_IO   104

Functions

Core operations
int vl_pgm_extract_head (FILE *f, VlPgmImage *im)
 Extract PGM header from stream.
int vl_pgm_extract_data (FILE *f, VlPgmImage const *im, void *data)
 Extract PGM data from stream.
int vl_pgm_insert (FILE *f, VlPgmImage const *im, void const *data)
 Insert a PGM image into a stream.
int vl_pgm_get_npixels (VlPgmImage const *im)
 Get PGM image number of pixels.
int vl_pgm_get_bpp (VlPgmImage const *im)
 Get PGM image bytes per pixel.
Helper functions
int vl_pgm_write (char const *name, vl_uint8 const *data, int width, int height)
 Write bytes to a PGM file.
int vl_pgm_write_f (char const *name, float const *data, int width, int height)
 Write floats to PGM file.
int vl_pgm_read_new (char const *name, VlPgmImage *im, vl_uint8 **data)
 Read a PGM file.
int vl_pgm_read_new_f (char const *name, VlPgmImage *im, float **data)
 Read floats from a PGM file.

Detailed Description

This module implements basic input and ouptut of images in PGM format.

Extracting an image encoded in PGM format from an imput file stream involves the following steps:

  • use vl_pgm_extract_head to extract the image meta data (size and bit depth);
  • allocate a buffer to store the image data;
  • use vl_pgm_extract_data to extract the image data to the allocated buffer.

Writing an image in PGM format to an ouptut file stream can be done by using vl_pgm_insert.

To quickly read/write a PGM image from/to a given file, use vl_pgm_read_new() and vl_pgm_write(). To to the same from a buffer in floating point format use vl_pgm_read_new_f() and vl_pgm_write_f().

Author:
Andrea Vedaldi

Define Documentation

#define VL_ERR_PGM_INV_DATA   103

Invalid PGM data section

#define VL_ERR_PGM_INV_HEAD   101

Invalid PGM header section

#define VL_ERR_PGM_INV_META   102

Invalid PGM meta section

#define VL_ERR_PGM_IO   104

Generic I/O error

Function Documentation

int vl_pgm_extract_data ( FILE *  f,
VlPgmImage const *  im,
void *  data 
)
Parameters:
finput file.
imPGM image descriptor.
datadata buffer to fill.

The function extracts from the file f the data section of an image encoded in PGM format. The function fills the buffer data according. The buffer data should be vl_pgm_get_npixels() by vl_pgm_get_bpp() bytes large.

Returns:
error code.
int vl_pgm_extract_head ( FILE *  f,
VlPgmImage *  im 
)
Parameters:
finput file.
imimage structure to fill.

The function extracts from the file f the meta-data section of an image encoded in PGM format. The function fills the structure VlPgmImage accordingly.

Returns:
error code. The error may be either #VL_PGM_INV_HEAD or #VL_PGM_INV_META depending whether the error occurred in decoding the header or meta section of the PGM file.
int vl_pgm_get_bpp ( VlPgmImage const *  im)
Parameters:
imPGM image descriptor.

The function returns the number of bytes for each pixel of the PGM image im.

Returns:
number of bytes per pixel.
int vl_pgm_get_npixels ( VlPgmImage const *  im)
Parameters:
imPGM image descriptor.

The functions returns the number of pixels of the PGM image im.

To calculate the image data size in bytes, this value must be multiplied by the number of byte per pixels (see vl_pgm_get_bpp()).

Returns:
number of pixels of the image.
int vl_pgm_insert ( FILE *  f,
VlPgmImage const *  im,
void const *  data 
)
Parameters:
foutput file.
imPGM image meta-data.
dataimage data.
Returns:
error code.
int vl_pgm_read_new ( char const *  name,
VlPgmImage *  im,
vl_uint8 **  data 
)
Parameters:
namefile name.
ima pointer to the PGM image structure to fill.
dataa pointer to the pointer to the allocated buffer.

The function reads a PGM image from file name and initializes the structure im and the buffer data accordingly.

The ownership of the buffer data is transfered to the caller. data should be freed by means of vl_free().

Bug:
Only PGM files with 1 BPP are supported.
Returns:
error code.
int vl_pgm_read_new_f ( char const *  name,
VlPgmImage *  im,
float **  data 
)
Parameters:
namefile name.
ima pointer to the PGM image structure to fill.
dataa pointer to the pointer to the allocated buffer.

The function reads a PGM image from file name and initializes the structure im and the buffer data accordingly. The buffer data is an array of floats in the range [0, 1].

The ownership of the buffer data is transfered to the caller. data should be freed by means of vl_free().

Bug:
Only PGM files with 1 BPP are supported.
Returns:
error code.
int vl_pgm_write ( char const *  name,
vl_uint8 const *  data,
int  width,
int  height 
)
Parameters:
namefile name.
datadata to write.
widthwidth of the image.
heightheight of the image.

The function dumps the image data to the PGM file of the specified name. This is an helper function simplifying the usage of vl_pgm_insert().

Returns:
error code.
int vl_pgm_write_f ( char const *  name,
float const *  data,
int  width,
int  height 
)
Parameters:
namefile name.
datadata to write.
widthwidth of the image.
heightheight of the image.

The function dumps the image data to the PGM file of the specified name. The data is re-scaled to fit in the range 0-255. This is an helper function simplifying the usage of vl_pgm_insert().

Returns:
error code.
 
vlfeat-0.9.14/doc/api/qsort-def_8h.html000066400000000000000000000416261167540017400176130ustar00rootroot00000000000000 VLFeat - Documentation - C API
Documentation - C API
Defines | Functions
qsort-def.h File Reference

QSort preprocessor metaprogram. More...

#include "host.h"
#include <assert.h>

Defines

#define VL_QSORT_array   VL_QSORT_type*
#define VL_QSORT_prefix   QSortPrefix
#define VL_QSORT_type   QSortType
#define VL_QSORT_array   QSortType*

Functions

VL_QSORT_type VL_QSORT_cmp (VL_QSORT_array_const array, vl_uindex indexA, vl_uindex indexB)
 Compare two array elements.
void VL_QSORT_swap (VL_QSORT_array array, vl_uindex indexA, vl_uindex indexB)
 Swap two array elements.
void VL_QSORT_sort_recursive (VL_QSORT_array array, vl_uindex begin, vl_uindex end)
 Sort portion of an array using quicksort.
void VL_QSORT_sort (VL_QSORT_array array, vl_size size)
 Sort array using quicksort.

Detailed Description

Author:
Andrea Vedaldi

Overview

qsort-def.h is a metaprogram to define specialized instances of the quick-sort algorithm.

Usage

qsort-def.h is used to define a specialization of the VL_QSORT_sort function that operatoes on a given type of array. For instance the code

 #define VL_QSORT_type float
 #define VL_QSORT_prefix my_qsort
 #include <vl/qsort-def.h>

defines a function my_qsort_sort that operates on an array of floats.

Todo:
large array compatibility.

Define Documentation

#define VL_QSORT_array   VL_QSORT_type*

Data type of the qsort container

#define VL_QSORT_array   QSortType*

Data type of the qsort container

#define VL_QSORT_prefix   QSortPrefix

Prefix of the qsort functions

#define VL_QSORT_type   QSortType

Data type of the qsort elements

Function Documentation

VL_QSORT_type VL_QSORT_cmp ( VL_QSORT_array_const  array,
vl_uindex  indexA,
vl_uindex  indexB 
) [inline]
Parameters:
arrayqsort array.
indexAindex of the first element A to compare.
indexBindex of the second element B to comapre.
Returns:
a negative number if A<B, 0 if A==B, and a positive number if if A>B.
void VL_QSORT_sort ( VL_QSORT_array  array,
vl_size  size 
) [inline]
Parameters:
array(in/out) pointer to the array.
sizesize of the array.

The function sorts the array using quick-sort.

void VL_QSORT_sort_recursive ( VL_QSORT_array  array,
vl_uindex  begin,
vl_uindex  end 
) [inline]
Parameters:
array(in/out) pointer to the array.
beginfirst element of the array portion.
endlast element of the array portion.

The function sorts the array using quick-sort. Note that begin must be not larger than end.

void VL_QSORT_swap ( VL_QSORT_array  array,
vl_uindex  indexA,
vl_uindex  indexB 
) [inline]
Parameters:
arrayqsort array.
indexAindex of the first element to swap.
indexBindex of the second element to swap.

The function swaps the two elements a and @ b. The function uses a temporary element of type VL_QSORT_type and the copy operator =.

 
vlfeat-0.9.14/doc/api/quickshift.html000066400000000000000000000216231167540017400174550ustar00rootroot00000000000000 VLFeat - Documentation - C API
Documentation - C API
Quick shift image segmentation
Author:
Brian Fulkerson
Andrea Vedaldi

quickshift.h implements an image segmentation algorithm based on the quick shift clustering algorithm [12] .

Overview

Quick shift [12] is a fast mode seeking algorithm, similar to mean shift. The algorithm segments an RGB image (or any image with more than one channel) by identifying clusters of pixels in the joint spatial and color dimensions. Segments are local (superpixels) and can be used as a basis for further processing.

Given an image, the algorithm calculates a forest of pixels whose branches are labeled with a distance value (vl_quickshift_get_parents, vl_quickshift_get_dists). This specifies a hierarchical segmentation of the image, with segments corresponding to subtrees. Useful superpixels can be identified by cutting the branches whose distance label is above a given threshold (the threshold can be either fixed by hand, or determined by cross validation).

Parameter influencing the algorithm are:

  • Kernel size. The pixel density and its modes are estimated by using a Parzen window estimator with a Gaussian kernel of the specified size (vl_quickshift_set_kernel_size). The larger the size, the larger the neighborhoods of pixels considered.
  • Maximum distance. This (vl_quickshift_set_max_dist) is the maximum distance between two pixels that the algorithm considers when building the forest. In principle, it can be infinity (so that a tree is returned), but in practice it is much faster to consider only relatively small distances (the maximum distance can be set to a small multiple of the kernel size).

Usage

Technical details

For each pixel (x,y), quick shift regards $ (x,y,I(x,y)) $ as a sample from a d + 2 dimensional vector space. It then calculates the Parzen density estimate (with a Gaussian kernel of standard deviation $ \sigma $)

\[ E(x,y) = P(x,y,I(x,y)) = \sum_{x'y'} \frac{1}{(2\pi\sigma)^{d+2}} \exp \left( -\frac{1}{2\sigma^2} \left[ \begin{array}{c} x - x' \\ y - y' \\ I(x,y) - I(x',y') \\ \end{array} \right] \right) \]

Then quick shift construct a tree connecting each image pixel to its nearest neighbor which has greater density value. Formally, write $ (x',y') >_P (x,y) $ if, and only if,

\[ P(x',y',I(x',y')) > P(x,y,I(x,y))}. \]

Each pixel (x, y) is connected to the closest higher density pixel parent(x, y) that achieves the minimum distance in

\[ \mathrm{dist}(x,y) = \mathrm{min}_{(x',y') > P(x,y)} \left( (x - x')^2 + (y - y')^2 + \| I(x,y) - I(x',y') \|_2^2 \right). \]

 
vlfeat-0.9.14/doc/api/quickshift_8c.html000066400000000000000000000413611167540017400200500ustar00rootroot00000000000000 VLFeat - Documentation - C API
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Functions
quickshift.c File Reference

Quick shift - Definition. More...

#include "quickshift.h"
#include "mathop.h"
#include <string.h>
#include <math.h>
#include <stdio.h>

Functions

vl_qs_type vl_quickshift_distance (vl_qs_type const *I, int N1, int N2, int K, int i1, int i2, int j1, int j2)
 Computes the accumulated channel L2 distance between i,j + the distance between i,j.
vl_qs_type vl_quickshift_inner (vl_qs_type const *I, int N1, int N2, int K, int i1, int i2, int j1, int j2)
 Computes the accumulated channel inner product between i,j + the distance between i,j.
VlQS * vl_quickshift_new (vl_qs_type const *image, int height, int width, int channels)
 Create a quick shift object.
void vl_quickshift_process (VlQS *q)
 Create a quick shift objet.
void vl_quickshift_delete (VlQS *q)
 Delete quick shift object.

Detailed Description

Author:
Brian Fulkerson
Andrea Vedaldi

Function Documentation

void vl_quickshift_delete ( VlQS *  q)
Parameters:
qquick shift object.
vl_qs_type vl_quickshift_distance ( vl_qs_type const *  I,
int  N1,
int  N2,
int  K,
int  i1,
int  i2,
int  j1,
int  j2 
) [inline]
Parameters:
Iinput image buffer
N1size of the first dimension of the image
N2size of the second dimension of the image
Knumber of channels
i1first dimension index of the first pixel to compare
i2second dimension of the first pixel
j1index of the second pixel to compare
j2second dimension of the second pixel

Takes the L2 distance between the values in I at pixel i and j, accumulating along K channels and adding in the distance between i,j in the image.

Returns:
the distance as described above
vl_qs_type vl_quickshift_inner ( vl_qs_type const *  I,
int  N1,
int  N2,
int  K,
int  i1,
int  i2,
int  j1,
int  j2 
) [inline]
Parameters:
Iinput image buffer
N1size of the first dimension of the image
N2size of the second dimension of the image
Knumber of channels
i1first dimension index of the first pixel to compare
i2second dimension of the first pixel
j1index of the second pixel to compare
j2second dimension of the second pixel

Takes the channel-wise inner product between the values in I at pixel i and j, accumulating along K channels and adding in the inner product between i,j in the image.

Returns:
the inner product as described above
VlQS* vl_quickshift_new ( vl_qs_type const *  image,
int  height,
int  width,
int  channels 
)
Parameters:
image
height
width
channels
Returns:
New quick shift object.
void vl_quickshift_process ( VlQS *  q)
Parameters:
qquick shift object.
 
vlfeat-0.9.14/doc/api/quickshift_8h.html000066400000000000000000000620331167540017400200540ustar00rootroot00000000000000 VLFeat - Documentation - C API
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Data Structures | Defines | Typedefs
quickshift.h File Reference

Quick shift (Quick shift image segmentation) More...

#include "generic.h"
#include "mathop.h"

Data Structures

struct  VlQS
 quick shift results More...

Defines

#define VL_QS_INF   VL_INFINITY_D
 quick shift infinity constant

Typedefs

typedef double vl_qs_type
 quick shift datatype

Functions

Create and destroy
VlQS * vl_quickshift_new (vl_qs_type const *im, int height, int width, int channels)
 Create a quick shift object.
void vl_quickshift_delete (VlQS *q)
 Delete quick shift object.
Process data
void vl_quickshift_process (VlQS *q)
 Create a quick shift objet.
Retrieve data and parameters
vl_qs_type vl_quickshift_get_max_dist (VlQS const *q)
 Get tau.
vl_qs_type vl_quickshift_get_kernel_size (VlQS const *q)
 Get sigma.
vl_bool vl_quickshift_get_medoid (VlQS const *q)
 Get medoid.
int * vl_quickshift_get_parents (VlQS const *q)
 Get parents.
vl_qs_type * vl_quickshift_get_dists (VlQS const *q)
 Get dists.
vl_qs_type * vl_quickshift_get_density (VlQS const *q)
 Get density.
Set parameters
void vl_quickshift_set_max_dist (VlQS *q, vl_qs_type tau)
 Set max distance.
void vl_quickshift_set_kernel_size (VlQS *q, vl_qs_type sigma)
 Set sigma.
void vl_quickshift_set_medoid (VlQS *q, vl_bool medoid)
 Set medoid.

Detailed Description

Author:
Andrea Vedaldi
Brian Fulkerson

Function Documentation

void vl_quickshift_delete ( VlQS *  q)
Parameters:
qquick shift object.
vl_qs_type * vl_quickshift_get_density ( VlQS const *  q) [inline]
Parameters:
qquick shift object.
Returns:
the estimate of the density at each pixel.
vl_qs_type * vl_quickshift_get_dists ( VlQS const *  q) [inline]
Parameters:
qquick shift object.
Returns:
for each pixel, the distance in feature space to the pixel that is its parent in the quick shift tree.
vl_qs_type vl_quickshift_get_kernel_size ( VlQS const *  q) [inline]
Parameters:
qquick shift object.
Returns:
the standard deviation of the kernel used in the Parzen density estimate.
vl_qs_type vl_quickshift_get_max_dist ( VlQS const *  q) [inline]
Parameters:
qquick shift object.
Returns:
the maximum distance in the feature space between nodes in the quick shift tree.
vl_bool vl_quickshift_get_medoid ( VlQS const *  q) [inline]
Parameters:
qquick Shift object.
Returns:
true if medoid shift is used instead of quick shift.
int * vl_quickshift_get_parents ( VlQS const *  q) [inline]
Parameters:
qquick shift object.
Returns:
a height x width matrix containing linearized links to the parents in the quick shift tree. If there is no parent, the parent will be set to the index of the node.
VlQS* vl_quickshift_new ( vl_qs_type const *  image,
int  height,
int  width,
int  channels 
)
Parameters:
image
height
width
channels
Returns:
New quick shift object.
void vl_quickshift_process ( VlQS *  q)
Parameters:
qquick shift object.
void vl_quickshift_set_kernel_size ( VlQS *  q,
vl_qs_type  sigma 
) [inline]
Parameters:
qquick shift object.
sigmastandard deviation of the kernel used in the Parzen density estimate.
void vl_quickshift_set_max_dist ( VlQS *  q,
vl_qs_type  tau 
) [inline]
Parameters:
qquick shift object.
tauthe maximum distance in the feature space between nodes in the quick shift tree.
void vl_quickshift_set_medoid ( VlQS *  q,
vl_bool  medoid 
) [inline]
Parameters:
qquick shift object.
medoidtrue to use kernelized medoid shift, false (default) uses quick shift.
 
vlfeat-0.9.14/doc/api/random_8c.html000066400000000000000000000254451167540017400171630ustar00rootroot00000000000000 VLFeat - Documentation - C API
Documentation - C API
Functions
random.c File Reference

Random number generator - Definition. More...

#include "random.h"
#include <stdio.h>
#include <string.h>

Functions

void vl_rand_init (VlRand *self)
 Initialise random number generator.
void vl_rand_seed (VlRand *self, vl_uint32 s)
 Seed the state of the random number generator.
void vl_rand_seed_by_array (VlRand *self, vl_uint32 const key[], vl_size keySize)
 Seed the state of the random number generator by an array.
vl_uint32 vl_rand_uint32 (VlRand *self)
 Generate a random UINT32.

Detailed Description

Author:
Andrea Vedaldi

Function Documentation

void vl_rand_init ( VlRand *  self)
Parameters:
selfnumber generator.
void vl_rand_seed ( VlRand *  self,
vl_uint32  s 
)
Parameters:
selfrandom number generator.
sseed.
void vl_rand_seed_by_array ( VlRand *  self,
vl_uint32 const  key[],
vl_size  keySize 
)
Parameters:
selfrandom number generator.
keyarray of numbers.
keySizelength of the array.
vl_uint32 vl_rand_uint32 ( VlRand *  self)
Parameters:
selfrandom number generator.
Returns:
a random number in [0, 0xffffffff].
 
vlfeat-0.9.14/doc/api/random_8h.html000066400000000000000000000623401167540017400171630ustar00rootroot00000000000000 VLFeat - Documentation - C API
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Data Structures
random.h File Reference

Random number generator. More...

#include "host.h"

Data Structures

struct  VlRand
 Random numbber generator state. More...

Functions

Setting and reading the state
void vl_rand_init (VlRand *self)
 Initialise random number generator.
void vl_rand_seed (VlRand *self, vl_uint32 s)
 Seed the state of the random number generator.
void vl_rand_seed_by_array (VlRand *self, vl_uint32 const key[], vl_size keySize)
 Seed the state of the random number generator by an array.
Generate random numbers
vl_uint64 vl_rand_uint64 (VlRand *self)
 Generate a random UINT64.
vl_int64 vl_rand_int63 (VlRand *self)
 Generate a random INT63.
vl_uint32 vl_rand_uint32 (VlRand *self)
 Generate a random UINT32.
vl_int32 vl_rand_int31 (VlRand *self)
 Generate a random INT31.
double vl_rand_real1 (VlRand *self)
 Generate a random number in [0,1].
double vl_rand_real2 (VlRand *self)
 Generate a random number in [0,1)
double vl_rand_real3 (VlRand *self)
 Generate a random number in (0,1)
double vl_rand_res53 (VlRand *self)
 Generate a random number in [0,1) with 53-bit resolution.
vl_uindex vl_rand_uindex (VlRand *self, vl_uindex range)
 Generate a random index in a given range.

Detailed Description

This module implements the popular Mersenne Twister algorithm (MATLAB random generator from version 7.4).

Overview

A random number generator can be initalized by

 VlRand rand ;
 vl_rand_init (&rand) ;

VlRand is a simple structure holding the state of the random number generator. The generator can be seeded by vl_rand_seed and vl_rand_seed_by_array. For intsance:

 vl_rand_seed (&rand, clock()) ;

The generator can be used to obtain random quantities of various types:

There is no need to explicitly destroy a VlRand instance.

[1] http://en.wikipedia.org/wiki/Mersenne_twister

Author:
Andrea Vedaldi

Function Documentation

void vl_rand_init ( VlRand *  self)
Parameters:
selfnumber generator.
vl_int32 vl_rand_int31 ( VlRand *  self) [inline]
Parameters:
selfrandom number generator.
Returns:
a random number in [0, 0x7fffffff].
vl_int64 vl_rand_int63 ( VlRand *  self) [inline]
Parameters:
selfrandom number generator.
Returns:
a random number in [0, 0x7fffffffffffffff].
double vl_rand_real1 ( VlRand *  self) [inline]
Parameters:
selfrandom number generator.
Returns:
a random number.
double vl_rand_real2 ( VlRand *  self) [inline]
Parameters:
selfrandom number generator.
Returns:
a random number.
double vl_rand_real3 ( VlRand *  self) [inline]
Parameters:
selfrandom number generator.
Returns:
a random number.
double vl_rand_res53 ( VlRand *  self) [inline]
Parameters:
selfrandom number generator.
Returns:
a random number.
void vl_rand_seed ( VlRand *  self,
vl_uint32  s 
)
Parameters:
selfrandom number generator.
sseed.
void vl_rand_seed_by_array ( VlRand *  self,
vl_uint32 const  key[],
vl_size  keySize 
)
Parameters:
selfrandom number generator.
keyarray of numbers.
keySizelength of the array.
vl_uindex vl_rand_uindex ( VlRand *  self,
vl_uindex  range 
) [inline]
Parameters:
selfrandom number generator.
rangerange.
Returns:
an index sampled uniformly at random in the interval [0, range - 1]
Remarks:
Currently, this function uses a simple algorithm that may yield slightly biased samples if range is not a power of two.
vl_uint32 vl_rand_uint32 ( VlRand *  self)
Parameters:
selfrandom number generator.
Returns:
a random number in [0, 0xffffffff].
vl_uint64 vl_rand_uint64 ( VlRand *  self) [inline]
Parameters:
selfrandom number generator.
Returns:
a random number in [0, 0xffffffffffffffff].
 
vlfeat-0.9.14/doc/api/rodrigues_8h.html000066400000000000000000000210061167540017400177000ustar00rootroot00000000000000 VLFeat - Documentation - C API
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Functions
rodrigues.h File Reference
#include "generic.h"

Functions

void vl_rodrigues (double *R_pt, double *dR_pt, const double *om_pt)
 Rodrigues' formula.
void vl_irodrigues (double *om_pt, double *dom_pt, const double *R_pt)
 Inverse Rodrigues formula.

Detailed Description

Rodrigues formulas

  • Use vl_rodrigues() to compute the Rodrigues formula and its derivative.
  • Use vl_irodrigues() to compute the inverse Rodrigues formula and its derivative.

Function Documentation

void vl_irodrigues ( double *  om_pt,
double *  dom_pt,
const double *  R_pt 
)
Parameters:
om_pt3 vector - array of 3 dobule (out).
dom_pt3x9 matrix - array of 3x9 dobule (out).
R_pt3x3 matrix - array of 9 double (in).

This function computes the Rodrigues formula of the argument om_pt. The result is stored int the matrix R_pt. If dR_pt is non null, then the derivative of the Rodrigues formula is computed and stored into the matrix dR_pt.

void vl_rodrigues ( double *  R_pt,
double *  dR_pt,
const double *  om_pt 
)
Parameters:
R_pt3x3 matrix - array of 9 double (in) .
dR_pt9x3 matrix - array of 27 double (in).
om_pt3 vector - array of 3 dobule (out).
 
vlfeat-0.9.14/doc/api/shuffle-def_8h.html000066400000000000000000000277321167540017400201010ustar00rootroot00000000000000 VLFeat - Documentation - C API
Documentation - C API
Defines | Functions
shuffle-def.h File Reference

Shuffle preprocessor metaprogram. More...

#include "host.h"
#include "random.h"
#include <assert.h>

Defines

#define VL_SHUFFLE_array   VL_SHUFFLE_type*
#define VL_SHUFFLE_prefix   ShufflePrefix
#define VL_SHUFFLE_type   ShuffleType
#define VL_SHUFFLE_array   ShuffleType*

Functions

void VL_SHUFFLE_swap (VL_SHUFFLE_array array, vl_uindex indexA, vl_uindex indexB)
 Swap two array elements.
void VL_SHUFFLE_shuffle (VL_SHUFFLE_array array, vl_size size, VlRand *rand)
 Shuffle.

Detailed Description

Author:
Andrea Vedaldi
Todo:
large array compatibility.

Define Documentation

#define VL_SHUFFLE_array   VL_SHUFFLE_type*

Data type of the shuffle container

#define VL_SHUFFLE_array   ShuffleType*

Data type of the shuffle container

#define VL_SHUFFLE_prefix   ShufflePrefix

Prefix of the shuffle functions

#define VL_SHUFFLE_type   ShuffleType

Data type of the shuffle elements

Function Documentation

void VL_SHUFFLE_shuffle ( VL_SHUFFLE_array  array,
vl_size  size,
VlRand *  rand 
) [inline]
Parameters:
array(in/out) pointer to the array.
sizesize of the array.
randrandom number generator to use.

The function randomly permutes the array.

void VL_SHUFFLE_swap ( VL_SHUFFLE_array  array,
vl_uindex  indexA,
vl_uindex  indexB 
) [inline]
Parameters:
arrayshuffle array.
indexAindex of the first element to swap.
indexBindex of the second element to swap.

The function swaps the two elements a and @ b. The function uses a temporary element of type VL_SHUFFLE_type and the copy operator =.

 
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Documentation - C API
Scale Invariant Feature Transform (SIFT)
Author:
Andrea Vedaldi
Credits:
May people have contributed with suggestions and bug reports. Although the following list is certainly incomplete, we would like to thank: Wei Dong, Loic, Giuseppe, Liu, Erwin, P. Ivanov, and Q. S. Luo.

sift.h implements a SIFT filter object, a reusable object to extract SIFT features [6] from one or multiple images.

Overview

A SIFT feature is a selected image region (also called keypoint) with an associated descriptor. Keypoints are extracted by the SIFT detector and their descriptors are computed by the SIFT descriptor. It is also common to use independently the SIFT detector (i.e. computing the keypoints without descriptors) or the SIFT descriptor (i.e. computing descriptors of custom keypoints).

SIFT detector

See also:
Scale space technical details, Detector technical details

A SIFT keypoint is a circular image region with an orientation. It is described by a geometric frame of four parameters: the keypoint center coordinates x and y, its scale (the radius of the region), and its orientation (an angle expressed in radians). The SIFT detector uses as keypoints image structures which resemble “blobs”. By searching for blobs at multiple scales and positions, the SIFT detector is invariant (or, more accurately, covariant) to translation, rotations, and rescaling of the image.

The keypoint orientation is also determined from the local image appearance and is covariant to image rotations. Depending on the symmetry of the keypoint appearance, determining the orientation can be ambiguous. In this case, the SIFT detectors returns a list of up to four possible orientations, constructing up to four frames (differing only by their orientation) for each detected image blob.

sift-frame.png
SIFT keypoints are circular image regions with an orientation.

There are several parameters that influence the detection of SIFT keypoints. First, searching keypoints at multiple scales is obtained by constructing a so-called “Gaussian scale space”. The scale space is just a collection of images obtained by progressively smoothing the input image, which is analogous to gradually reducing the image resolution. Conventionally, the smoothing level is called scale of the image. The construction of the scale space is influenced by the following parameters, set when creating the SIFT filter object by vl_sift_new():

  • Number of octaves. Increasing the scale by an octave means doubling the size of the smoothing kernel, whose effect is roughly equivalent to halving the image resolution. By default, the scale space spans as many octaves as possible (i.e. roughly log2(min(width,height)), which has the effect of searching keypoints of all possible sizes.
  • First octave index. By convention, the octave of index 0 starts with the image full resolution. Specifying an index greater than 0 starts the scale space at a lower resolution (e.g. 1 halves the resolution). Similarly, specifying a negative index starts the scale space at an higher resolution image, and can be useful to extract very small features (since this is obtained by interpolating the input image, it does not make much sense to go past -1).
  • Number of levels per octave. Each octave is sampled at this given number of intermediate scales (by default 3). Increasing this number might in principle return more refined keypoints, but in practice can make their selection unstable due to noise (see [1]).

Keypoints are further refined by eliminating those that are likely to be unstable, either because they are selected nearby an image edge, rather than an image blob, or are found on image structures with low contrast. Filtering is controlled by the follow:

  • Peak threshold. This is the minimum amount of contrast to accept a keypoint. It is set by configuring the SIFT filter object by vl_sift_set_peak_thresh().
  • Edge threshold. This is the edge rejection threshold. It is set by configuring the SIFT filter object by vl_sift_set_edge_thresh().
Summary of the parameters influencing the SIFT detector.
Parameter See also Controlled by Comment
number of octaves SIFT detector vl_sift_new
first octave index SIFT detector vl_sift_new set to -1 to extract very small features
number of scale levels per octave SIFT detector vl_sift_new can affect the number of extracted keypoints
edge threshold SIFT detector vl_sift_set_edge_thresh decrease to eliminate more keypoints
peak threshold SIFT detector vl_sift_set_peak_thresh increase to eliminate more keypoints

SIFT Descriptor

See also:
Descriptor technical details

A SIFT descriptor is a 3-D spatial histogram of the image gradients in characterizing the appearance of a keypoint. The gradient at each pixel is regarded as a sample of a three-dimensional elementary feature vector, formed by the pixel location and the gradient orientation. Samples are weighed by the gradient norm and accumulated in a 3-D histogram h, which (up to normalization and clamping) forms the SIFT descriptor of the region. An additional Gaussian weighting function is applied to give less importance to gradients farther away from the keypoint center. Orientations are quantized into eight bins and the spatial coordinates into four each, as follows:

sift-descr-easy.png
The SIFT descriptor is a spatial histogram of the image gradient.

SIFT descriptors are computed by either calling vl_sift_calc_keypoint_descriptor or vl_sift_calc_raw_descriptor. They accept as input a keypoint frame, which specifies the descriptor center, its size, and its orientation on the image plane. The following parameters influence the descriptor calculation:

  • magnification factor. The descriptor size is determined by multiplying the keypoint scale by this factor. It is set by vl_sift_set_magnif.
  • Gaussian window size. The descriptor support is determined by a Gaussian window, which discounts gradient contributions farther away from the descriptor center. The standard deviation of this window is set by vl_sift_set_window_size and expressed in unit of bins.

VLFeat SIFT descriptor uses the following convention. The y axis points downwards and angles are measured clockwise (to be consistent with the standard image convention). The 3-D histogram (consisting of $ 8 \times 4 \times 4 = 128 $ bins) is stacked as a single 128-dimensional vector, where the fastest varying dimension is the orientation and the slowest the y spatial coordinate. This is illustrated by the following figure.

sift-conv-vlfeat.png
VLFeat conventions
Note:
Keypoints (frames) D. Lowe's SIFT implementation convention is slightly different: The y axis points upwards and the angles are measured counter-clockwise.
sift-conv.png
D. Lowes' SIFT implementation conventions
Summary of the parameters influencing the SIFT descriptor.
Parameter See also Controlled by Comment
magnification factor SIFT Descriptor vl_sift_set_magnif increase this value to enlarge the image region described
Gaussian window size SIFT Descriptor vl_sift_set_window_size smaller values let the center of the descriptor count more

Extensions

Eliminating low-contrast descriptors. Near-uniform patches do not yield stable keypoints or descriptors. vl_sift_set_norm_thresh() can be used to set a threshold on the average norm of the local gradient to zero-out descriptors that correspond to very low contrast regions. By default, the threshold is equal to zero, which means that no descriptor is zeroed. Normally this option is useful only with custom keypoints, as detected keypoints are implicitly selected at high contrast image regions.

Using the SIFT filter object

The code provided in this module can be used in different ways. You can instantiate and use a SIFT filter to extract both SIFT keypoints and descriptors from one or multiple images. Alternatively, you can use one of the low level functions to run only a part of the SIFT algorithm (for instance, to compute the SIFT descriptors of custom keypoints).

To use a SIFT filter object:

To compute SIFT descriptors of custom keypoints, use vl_sift_calc_raw_descriptor().

Technical details

Scale space

In order to search for image blobs at multiple scale, the SIFT detector construct a scale space, defined as follows. Let $I_0(\mathbf{x})$ denote an idealized infinite resolution image. Consider the Gaussian kernel

\[ g_{\sigma}(\mathbf{x}) = \frac{1}{2\pi\sigma^2} \exp \left( -\frac{1}{2} \frac{\mathbf{x}^\top\mathbf{x}}{\sigma^2} \right) \]

The Gaussian scale space is the collection of smoothed images

\[ I_\sigma = g_\sigma * I, \quad \sigma \geq 0. \]

The image at infinite resolution $ I_0 $ is useful conceptually, but is not available to us; instead, the input image $ I_{\sigma_n} $ is assumed to be pre-smoothed at a nominal level $ \sigma_n = 0.5 $ to account for the finite resolution of the pixels. Thus in practice the scale space is computed by

\[ I_\sigma = g_{\sqrt{\sigma^2 - \sigma_n^2}} * I_{\sigma_n}, \quad \sigma \geq \sigma_n. \]

Scales are sampled at logarithmic steps given by

\[ \sigma = \sigma_0 2^{o+s/S}, \quad s = 0,\dots,S-1, \quad o = o_{\min}, \dots, o_{\min}+O-1, \]

where $ \sigma_0 = 1.6 $ is the base scale, $ o_{\min} $ is the first octave index, O the number of octaves and S the number of scales per octave.

Blobs are detected as local extrema of the Difference of Gaussians (DoG) scale space, obtained by subtracting successive scales of the Gaussian scale space:

\[ \mathrm{DoG}_{\sigma(o,s)} = I_{\sigma(o,s+1)} - I_{\sigma(o,s)} \]

At each next octave, the resolution of the images is halved to save computations. The images composing the Gaussian and DoG scale space can then be arranged as in the following figure:

sift-ss.png
GSS and DoG scale space structures.

The black vertical segments represent images of the Gaussian Scale Space (GSS), arranged by increasing scale $\sigma$. Notice that the scale level index s varies in a slightly redundant set

\[ s = -1, \dots, S+2 \]

This simplifies glueing together different octaves and extracting DoG maxima (required by the SIFT detector).

Detector

The SIFT frames (keypoints) are extracted based on local extrema (peaks) of the DoG scale space. Numerically, local extrema are elements whose $ 3 \times 3 \times 3 $ neighbors (in space and scale) have all smaller (or larger) value. Once extracted, local extrema are quadratically interpolated (this is very important especially at the lower resolution scales in order to have accurate keypoint localization at the full resolution). Finally, they are filtered to eliminate low-contrast responses or responses close to edges and the orientation(s) are assigned, as explained next.

Eliminating low contrast responses

Peaks which are too short may have been generated by noise and are discarded. This is done by comparing the absolute value of the DoG scale space at the peak with the peak threshold $t_p$ and discarding the peak its value is below the threshold.

Eliminating edge responses

Peaks which are too flat are often generated by edges and do not yield stable features. These peaks are detected and removed as follows. Given a peak $x,y,\sigma$, the algorithm evaluates the x,y Hessian of of the DoG scale space at the scale $\sigma$. Then the following score (similar to the Harris function) is computed:

\[ \frac{(\mathrm{tr}\,D(x,y,\sigma))^2}{\det D(x,y,\sigma)}, \quad D = \left[ \begin{array}{cc} \frac{\partial^2 \mathrm{DoG}}{\partial x^2} & \frac{\partial^2 \mathrm{DoG}}{\partial x\partial y} \\ \frac{\partial^2 \mathrm{DoG}}{\partial x\partial y} & \frac{\partial^2 \mathrm{DoG}}{\partial y^2} \end{array} \right]. \]

This score has a minimum (equal to 4) when both eigenvalues of the Jacobian are equal (curved peak) and increases as one of the eigenvalues grows and the other stays small. Peaks are retained if the score is below the quantity $(t_e+1)(t_e+1)/t_e$, where $t_e$ is the edge threshold. Notice that this quantity has a minimum equal to 4 when $t_e=1$ and grows thereafter. Therefore the range of the edge threshold is $[1,\infty)$.

Orientation assignment

A peak in the DoG scale space fixes 2 parameters of the keypoint: the position and scale. It remains to choose an orientation. In order to do this, SIFT computes an histogram of the gradient orientations in a Gaussian window with a standard deviation which is 1.5 times bigger than the scale $\sigma$ of the keypoint.

sift-orient.png

This histogram is then smoothed and the maximum is selected. In addition to the biggest mode, up to other three modes whose amplitude is within the 80% of the biggest mode are retained and returned as additional orientations.

Descriptor

A SIFT descriptor of a local region (keypoint) is a 3-D spatial histogram of the image gradients. The gradient at each pixel is regarded as a sample of a three-dimensional elementary feature vector, formed by the pixel location and the gradient orientation. Samples are weighed by the gradient norm and accumulated in a 3-D histogram h, which (up to normalization and clamping) forms the SIFT descriptor of the region. An additional Gaussian weighting function is applied to give less importance to gradients farther away from the keypoint center.

Construction in the canonical frame

Denote the gradient vector field computed at the scale $ \sigma $ by

\[ J(x,y) = \nalba I_\sigma(x,y) = \left[\begin{array}{cc} \frac{\partial I_\sigma}{\partial x} & \frac{\partial I_\sigma}{\partial y} & \end{array}\right] \]

The descriptor is a 3-D spatial histogram capturing the distribution of $ J(x,y) $. It is convenient to describe its construction in the canonical frame. In this frame, the image and descriptor axes coincide and each spatial bin has side 1. The histogram has $ N_\theta \times N_x \times N_y $ bins (usually $ 8 \times 4 \times 4 $), as in the following figure:

sift-can.png
Canonical SIFT descriptor and spatial binning functions

Bins are indexed by a triplet of indexes t, i, j and their centers are given by

\begin{eqnarray*} \theta_t &=& \frac{2\pi}{N_\theta} t, \quad t = 0,\dots,N_{\theta}-1, \\ x_i &=& i - \frac{N_x -1}{2}, \quad i = 0,\dots,N_x-1, \\ y_j &=& j - \frac{N_x -1}{2}, \quad j = 0,\dots,N_y-1. \\ \end{eqnarray*}

The histogram is computed by using trilinear interpolation, i.e. by weighing contributions by the binning functions

\begin{eqnarray*} \displaystyle w(z) &=& \mathrm{max}(0, 1 - |z|), \\ \displaystyle w_\mathrm{ang}(z) &=& \sum_{k=-\infty}^{+\infty} w\left( \frac{N_\theta}{2\pi} z + N_\theta k \right). \end{eqnarray*}

The gradient vector field is transformed in a three-dimensional density map of weighed contributions

\[ f(\theta, x, y) = |J(x,y)| \delta(\theta - \angle J(x,y)) \]

The historam is localized in the keypoint support by a Gaussian window of standard deviation $ \sigma_{\mathrm{win}} $. The histogram is then given by

\begin{eqnarray*} h(t,i,j) &=& \int g_{\sigma_\mathrm{win}}(x,y) w_\mathrm{ang}(\theta - \theta_t) w(x-x_i) w(y-y_j) f(\theta,x,y) d\theta\,dx\,dy \\ &=& \int g_{\sigma_\mathrm{win}}(x,y) w_\mathrm{ang}(\angle J(x,y) - \theta_t) w(x-x_i) w(y-y_j) |J(x,y)|\,dx\,dy \end{eqnarray*}

In post processing, the histogram is $ l^2 $ normalized, then clamped at 0.2, and $ l^2 $ normalized again.

Calculation in the image frame

Invariance to similarity transformation is attained by attaching descriptors to SIFT keypoints (or other similarity-covariant frames). Then projecting the image in the canonical descriptor frames has the effect of undoing the image deformation.

In practice, however, it is convenient to compute the descriptor directly in the image frame. To do this, denote with a hat quantities relative to the canonical frame and without a hat quantities relative to the image frame (so for instance $ \hat x $ is the x-coordinate in the canonical frame and $ x $ the x-coordinate in the image frame). Assume that canonical and image frame are related by an affinity:

\[ \mathbf{x} = A \hat\mathbf{x} + T, \qquad \mathbf{x} = \left[\begin{array}{cc} x \\ y \end{arraty}\right], \quad \hat\mathbf{x} = \left[\begin{array}{cc} \hat x \\ \hat y \end{arraty}\right]. \]

sift-image-frame.png

Then all quantities can be computed in the image frame directly. For instance, the image at infinite resolution in the two frames are related by

\[ \hat I_0(\hat{\mathbf{x}}) = I_0(\mathbf{x}), \qquad \mathbf{x} = A \hat{\mathbf{x}} + T. \]

The canonized image at scale $ \hat \sigma $ is in relation with the scaled image

\[ \hat I_{\hat{\sigma}}(\hat{\mathbf{x}}) = I_{A\hat{\sigma}}(\mathbf{x}), \qquad \mathbf{x} = A \hat{\mathbf{x}} + T \]

where, by generalizing the previous definitions, we have

\[ I_{A\hat \sigma}(\mathbf{x}) = (g_{A\hat\sigma} * I_0)(\mathbf{x}), \quad g_{A\hat\sigma}(\mathbf{x}) = \frac{1}{2\pi|A|\hat \sigma^2} \exp \left( -\frac{1}{2} \frac{\mathbf{x}^\top A^{-\top}A^{-1}\mathbf{x}}{\hat \sigma^2} \right) \]

Deriving shows that the gradient fields are in relation

\[ \hat J(\hat{\mathbf{x}}) = J(\mathbf{x}) A, \quad J(\mathbf{x}) = (\nabla I_{A\hat\sigma})(\mathbf{x}), \qquad \mathbf{x} = A \hat{\mathbf{x}} + T. \]

Therefore we can compute the descriptor either in the image or canonical frame as:

\begin{eqnarray*} h(t,i,j) &=& \int g_{\hat \sigma_\mathrm{win}}(\hat{\mathbf{x}})\, w_\mathrm{ang}(\angle \hat J(\hat{\mathbf{x}}) - \theta_t)\, w_{ij}(\hat{\mathbf{x}})\, |\hat J(\hat{\mathbf{x}})|\, d\hat{\mathbf{x}} \\ &=& \int g_{A \hat \sigma_\mathrm{win}}(\mathbf{x} - T)\, w_\mathrm{ang}(\angle J(\mathbf{x})A - \theta_t)\, w_{ij}(A^{-1}(\mathbf{x} - T))\, |J(\mathbf{x})A|\, d\mathbf{x}. \end{eqnarray*}

where we defined the product of the two spatial binning functions

\[ w_{ij}(\hat{\mathbf{x}}) = w(\hat x - \hat x_i) w(\hat y - \hat y_j) \]

In the actual implementation, this integral is computed by visiting a rectangular area of the image that fully contains the keypoint grid (along with half a bin border to fully include the bin windowing function). Since the descriptor can be rotated, this area is a rectangle of sides $m/2\sqrt{2} (N_x+1,N_y+1)$ (see also the illustration).

Standard SIFT descriptor

For a SIFT-detected keypoint of center $ T $, scale $ \sigma $ and orientation $ \theta $, the affine transformation $ (A,T) $ reduces to the similarity transformation

\[ \mathbf{x} = m \sigma R(\theta) \hat{\mathbf{x}} + T \]

where $ R(\theta) $ is a counter-clockwise rotation of $ \theta $ radians, $ m \mathcal{\sigma} $ is the size of a descriptor bin in pixels, and m is the descriptor magnification factor which expresses how much larger a descriptor bin is compared to the scale of the keypoint $ \sigma $ (the default value is m = 3). Moreover, the standard SIFT descriptor computes the image gradient at the scale of the keypoints, which in the canonical frame is equivalent to a smoothing of $ \hat \sigma = 1/m $. Finally, the default Gaussian window size is set to have standard deviation $ \hat \sigma_\mathrm{win} = 2 $. This yields the formula

\begin{eqnarray*} h(t,i,j) &=& m \sigma \int g_{\sigma_\mathrm{win}}(\mathbf{x} - T)\, w_\mathrm{ang}(\angle J(\mathbf{x}) - \theta - \theta_t)\, w_{ij}\left(\frac{R(\theta)^\top \mathbf{x} - T}{m\sigma}\right)\, |J(\mathbf{x})|\, d\mathbf{x}, \\ \sigma_{\mathrm{win}} &=& m\sigma\hat \sigma_{\mathrm{win}}, \\ J(\mathbf{x}) &=& \nabla (g_{m \sigma \hat \sigma} * I)(\mathbf{x}) = \nabla (g_{\sigma} * I)(\mathbf{x}) = \nabla I_{\sigma} (\mathbf{x}). \end{eqnarray*}

 
vlfeat-0.9.14/doc/api/sift_8c.html000066400000000000000000001400771167540017400166470ustar00rootroot00000000000000 VLFeat - Documentation - C API
Documentation - C API
Defines | Functions | Variables
sift.c File Reference

SIFT - Definition. More...

#include "sift.h"
#include "imopv.h"
#include "mathop.h"
#include <assert.h>
#include <stdlib.h>
#include <string.h>
#include <math.h>
#include <stdio.h>

Defines

#define VL_SIFT_BILINEAR_ORIENTATIONS   1
 Use bilinear interpolation to compute orientations.
#define EXPN_SZ   256
#define EXPN_MAX   25.0

Functions

double fast_expn (double x)
 Fast $exp(-x)$ approximation.
void fast_expn_init ()
 Initialize tables for fast_expn.
static void copy_and_upsample_rows (vl_sift_pix *dst, vl_sift_pix const *src, int width, int height)
 Copy image, upsample rows and take transpose.
static void _vl_sift_smooth (VlSiftFilt *self, vl_sift_pix *outputImage, vl_sift_pix *tempImage, vl_sift_pix const *inputImage, vl_size width, vl_size height, double sigma)
 Smooth an image.
static void copy_and_downsample (vl_sift_pix *dst, vl_sift_pix const *src, int width, int height, int d)
 Copy and downsample an image.
VlSiftFilt * vl_sift_new (int width, int height, int noctaves, int nlevels, int o_min)
 Create a new SIFT filter.
void vl_sift_delete (VlSiftFilt *f)
 Delete SIFT filter.
int vl_sift_process_first_octave (VlSiftFilt *f, vl_sift_pix const *im)
 Start processing a new image.
int vl_sift_process_next_octave (VlSiftFilt *f)
 Process next octave.
void vl_sift_detect (VlSiftFilt *f)
 Detect keypoints.
static void update_gradient (VlSiftFilt *f)
 Update gradients to current GSS octave.
int vl_sift_calc_keypoint_orientations (VlSiftFilt *f, double angles[4], VlSiftKeypoint const *k)
 Calculate the keypoint orientation(s)
vl_sift_pix normalize_histogram (vl_sift_pix *begin, vl_sift_pix *end)
 Normalizes in norm L_2 a descriptor.
void vl_sift_calc_raw_descriptor (VlSiftFilt const *f, vl_sift_pix const *grad, vl_sift_pix *descr, int width, int height, double x, double y, double sigma, double angle0)
 Run the SIFT descriptor on raw data.
void vl_sift_calc_keypoint_descriptor (VlSiftFilt *f, vl_sift_pix *descr, VlSiftKeypoint const *k, double angle0)
 Compute the descriptor of a keypoint.
void vl_sift_keypoint_init (VlSiftFilt const *f, VlSiftKeypoint *k, double x, double y, double sigma)
 Initialize a keypoint from its position and scale.

Variables

double expn_tab [EXPN_SZ+1]

Detailed Description

Author:
Andrea Vedaldi

Define Documentation

#define EXPN_MAX   25.0

fast_expn table max

#define EXPN_SZ   256

fast_expn table size

#define VL_SIFT_BILINEAR_ORIENTATIONS   1

Function Documentation

static void _vl_sift_smooth ( VlSiftFilt *  self,
vl_sift_pix *  outputImage,
vl_sift_pix *  tempImage,
vl_sift_pix const *  inputImage,
vl_size  width,
vl_size  height,
double  sigma 
) [static]
Parameters:
selfSIFT filter.
outputImageoutput imgae buffer.
tempImagetemporary image buffer.
inputImageinput image buffer.
widthinput image width.
heightinput image height.
sigmasmoothing.
static void copy_and_downsample ( vl_sift_pix *  dst,
vl_sift_pix const *  src,
int  width,
int  height,
int  d 
) [static]
Parameters:
dstoutput imgae buffer.
srcinput image buffer.
widthinput image width.
heightinput image height.
doctaves (non negative).

The function downsamples the image d times, reducing it to 1/2^d of its original size. The parameters width and height are the size of the input image. The destination image dst is assumed to be floor(width/2^d) pixels wide and floor(height/2^d) pixels high.

static void copy_and_upsample_rows ( vl_sift_pix *  dst,
vl_sift_pix const *  src,
int  width,
int  height 
) [static]
Parameters:
dstoutput image buffer.
srcinput image buffer.
widthinput image width.
heightinput image height.

The output image has dimensions height by 2 width (so the destination buffer must be at least as big as two times the input buffer).

Upsampling is performed by linear interpolation.

double fast_expn ( double  x) [inline]
Parameters:
xargument.

The argument must be in the range [0, EXPN_MAX] .

Returns:
approximation of $exp(-x)$.
void fast_expn_init ( ) [inline]
vl_sift_pix normalize_histogram ( vl_sift_pix *  begin,
vl_sift_pix *  end 
) [inline]
Parameters:
beginbegin of histogram.
endend of histogram.
static void update_gradient ( VlSiftFilt *  f) [static]
Parameters:
fSIFT filter.

The function makes sure that the gradient buffer is up-to-date with the current GSS data.

Remarks:
The minimum octave size is 2x2xS.
void vl_sift_calc_keypoint_descriptor ( VlSiftFilt *  f,
vl_sift_pix *  descr,
VlSiftKeypoint const *  k,
double  angle0 
)
Parameters:
fSIFT filter.
descrSIFT descriptor (output)
kkeypoint.
angle0keypoint direction.

The function computes the SIFT descriptor of the keypoint k of orientation angle0. The function fills the buffer descr which must be large enough to hold the descriptor.

The function assumes that the keypoint is on the current octave. If not, it does not do anything.

int vl_sift_calc_keypoint_orientations ( VlSiftFilt *  f,
double  angles[4],
VlSiftKeypoint const *  k 
)
Parameters:
fSIFT filter.
anglesorientations (output).
kkeypoint.

The function computes the orientation(s) of the keypoint k. The function returns the number of orientations found (up to four). The orientations themselves are written to the vector angles.

Remarks:
The function requires the keypoint octave k->o to be equal to the filter current octave vl_sift_get_octave. If this is not the case, the function returns zero orientations.
The function requires the keypoint scale level k->s to be in the range s_min+1 and s_max-2 (where usually s_min=0 and s_max=S+2). If this is not the case, the function returns zero orientations.
Returns:
number of orientations found.
void vl_sift_calc_raw_descriptor ( VlSiftFilt const *  f,
vl_sift_pix const *  grad,
vl_sift_pix *  descr,
int  width,
int  height,
double  x,
double  y,
double  sigma,
double  angle0 
)
Parameters:
fSIFT filter.
gradimage gradients.
descrSIFT descriptor (output).
widthimage width.
heightimage height.
xkeypoint x coordinate.
ykeypoint y coordinate.
sigmakeypoint scale.
angle0keypoint orientation.

The function runs the SIFT descriptor on raw data. Here image is a 2 x width x height array (by convention, the memory layout is a s such the first index is the fastest varying one). The first width x height layer of the array contains the gradient magnitude and the second the gradient angle (in radians, between 0 and $ 2\pi $). x, y and sigma give the keypoint center and scale respectively.

In order to be equivalent to a standard SIFT descriptor the image gradient must be computed at a smoothing level equal to the scale of the keypoint. In practice, the actual SIFT algorithm makes the following additional approximation, which influence the result:

  • Scale is discretized in S levels.
  • The image is downsampled once for each octave (if you do this, the parameters x, y and sigma must be scaled too).
void vl_sift_delete ( VlSiftFilt *  f)
Parameters:
fSIFT filter to delete.

The function frees the resources allocated by vl_sift_new().

void vl_sift_detect ( VlSiftFilt *  f)

The function detect keypoints in the current octave filling the internal keypoint buffer. Keypoints can be retrieved by vl_sift_get_keypoints().

Parameters:
fSIFT filter.

Index GSS

Index matrix A

void vl_sift_keypoint_init ( VlSiftFilt const *  f,
VlSiftKeypoint *  k,
double  x,
double  y,
double  sigma 
)
Parameters:
fSIFT filter.
kSIFT keypoint (output).
xx coordinate of the keypoint center.
yy coordinate of the keypoint center.
sigmakeypoint scale.

The function initializes a keypoint structure k from the location x and y and the scale sigma of the keypoint. The keypoint structure maps the keypoint to an octave and scale level of the discretized Gaussian scale space, which is required for instance to compute the keypoint SIFT descriptor.

Algorithm

The formula linking the keypoint scale sigma to the octave and scale indexes is

\[ \sigma(o,s) = \sigma_0 2^{o+s/S} \]

In addition to the scale index s (which can be fractional due to scale interpolation) a keypoint has an integer scale index is too (which is the index of the scale level where it was detected in the DoG scale space). We have the constraints (Detector see also the "SIFT detector"):

  • o is integer in the range $ [o_\mathrm{min}, o_{\mathrm{min}}+O-1] $.
  • is is integer in the range $ [s_\mathrm{min}+1, s_\mathrm{max}-2] $. This depends on how the scale is determined during detection, and must be so here because gradients are computed only for this range of scale levels and are required for the calculation of the SIFT descriptor.
  • $ |s - is| < 0.5 $ for detected keypoints in most cases due to the interpolation technique used during detection. However this is not necessary.

Thus octave o represents scales $ \{ \sigma(o, s) : s \in [s_\mathrm{min}+1-.5, s_\mathrm{max}-2+.5] \} $. Note that some scales may be represented more than once. For each scale, we select the largest possible octave that contains it, i.e.

\[ o(\sigma) = \max \{ o \in \mathbb{Z} : \sigma_0 2^{\frac{s_\mathrm{min}+1-.5}{S}} \leq \sigma \} = \mathrm{floor}\,\left[ \log_2(\sigma / \sigma_0) - \frac{s_\mathrm{min}+1-.5}{S}\right] \]

and then

\[ s(\sigma) = S \left[\log_2(\sigma / \sigma_0) - o(\sigma)\right], \quad is(\sigma) = \mathrm{round}\,(s(\sigma)) \]

In practice, both $ o(\sigma) $ and $ is(\sigma) $ are clamped to their feasible range as determined by the SIFT filter parameters.

VlSiftFilt* vl_sift_new ( int  width,
int  height,
int  noctaves,
int  nlevels,
int  o_min 
)
Parameters:
widthimage width.
heightimage height.
noctavesnumber of octaves.
nlevelsnumber of levels per octave.
o_minfirst octave index.

The function allocates and returns a new SIFT filter for the specified image and scale space geometry.

Setting O to a negative value sets the number of octaves to the maximum possible value depending on the size of the image.

Returns:
the new SIFT filter.
See also:
vl_sift_delete().
int vl_sift_process_first_octave ( VlSiftFilt *  f,
vl_sift_pix const *  im 
)
Parameters:
fSIFT filter.
imimage data.

The function starts processing a new image by computing its Gaussian scale space at the lower octave. It also empties the internal keypoint buffer.

Returns:
error code. The function returns VL_ERR_EOF if there are no more octaves to process.
See also:
vl_sift_process_next_octave().
int vl_sift_process_next_octave ( VlSiftFilt *  f)
Parameters:
fSIFT filter.

The function computes the next octave of the Gaussian scale space. Notice that this clears the record of any feature detected in the previous octave.

Returns:
error code. The function returns the error VL_ERR_EOF when there are no more octaves to process.
See also:
vl_sift_process_first_octave().

Variable Documentation

double expn_tab[EXPN_SZ+1]

fast_expn table

 
vlfeat-0.9.14/doc/api/sift_8h.html000066400000000000000000001634711167540017400166570ustar00rootroot00000000000000 VLFeat - Documentation - C API
Documentation - C API
Data Structures | Typedefs
sift.h File Reference

SIFT (Scale Invariant Feature Transform (SIFT)) More...

#include <stdio.h>
#include "generic.h"

Data Structures

struct  VlSiftKeypoint
 SIFT filter keypoint. More...
struct  VlSiftFilt
 SIFT filter. More...

Typedefs

typedef float vl_sift_pix
 SIFT filter pixel type.

Functions

Create and destroy
VlSiftFilt * vl_sift_new (int width, int height, int noctaves, int nlevels, int o_min)
 Create a new SIFT filter.
void vl_sift_delete (VlSiftFilt *f)
 Delete SIFT filter.
Process data
int vl_sift_process_first_octave (VlSiftFilt *f, vl_sift_pix const *im)
 Start processing a new image.
int vl_sift_process_next_octave (VlSiftFilt *f)
 Process next octave.
void vl_sift_detect (VlSiftFilt *f)
 Detect keypoints.
int vl_sift_calc_keypoint_orientations (VlSiftFilt *f, double angles[4], VlSiftKeypoint const *k)
 Calculate the keypoint orientation(s)
void vl_sift_calc_keypoint_descriptor (VlSiftFilt *f, vl_sift_pix *descr, VlSiftKeypoint const *k, double angle)
 Compute the descriptor of a keypoint.
void vl_sift_calc_raw_descriptor (VlSiftFilt const *f, vl_sift_pix const *image, vl_sift_pix *descr, int widht, int height, double x, double y, double s, double angle0)
 Run the SIFT descriptor on raw data.
void vl_sift_keypoint_init (VlSiftFilt const *f, VlSiftKeypoint *k, double x, double y, double sigma)
 Initialize a keypoint from its position and scale.
Retrieve data and parameters
int vl_sift_get_octave_index (VlSiftFilt const *f)
 Get current octave index.
int vl_sift_get_noctaves (VlSiftFilt const *f)
 Get number of octaves.
int vl_sift_get_octave_first (VlSiftFilt const *f)
 Get first octave.
int vl_sift_get_octave_width (VlSiftFilt const *f)
 Get current octave width.
int vl_sift_get_octave_height (VlSiftFilt const *f)
 Get current octave height.
int vl_sift_get_nlevels (VlSiftFilt const *f)
 Get number of levels per octave.
int vl_sift_get_nkeypoints (VlSiftFilt const *f)
 Get number of keypoints.
double vl_sift_get_peak_thresh (VlSiftFilt const *f)
 Get peaks treashold.
double vl_sift_get_edge_thresh (VlSiftFilt const *f)
 Get edges threshold.
double vl_sift_get_norm_thresh (VlSiftFilt const *f)
 Get norm threshold.
double vl_sift_get_magnif (VlSiftFilt const *f)
 Get the magnification factor.
double vl_sift_get_window_size (VlSiftFilt const *f)
 Get the Gaussian window size.
vl_sift_pix * vl_sift_get_octave (VlSiftFilt const *f, int s)
 Get current octave data.
VlSiftKeypoint const * vl_sift_get_keypoints (VlSiftFilt const *f)
 Get keypoints.
Set parameters
void vl_sift_set_peak_thresh (VlSiftFilt *f, double t)
 Set peaks threshold.
void vl_sift_set_edge_thresh (VlSiftFilt *f, double t)
 Set edges threshold.
void vl_sift_set_norm_thresh (VlSiftFilt *f, double t)
 Set norm threshold.
void vl_sift_set_magnif (VlSiftFilt *f, double m)
 Set the magnification factor.
void vl_sift_set_window_size (VlSiftFilt *f, double x)
 Set the Gaussian window size.

Detailed Description

Author:
Andrea Vedaldi

Function Documentation

void vl_sift_calc_keypoint_descriptor ( VlSiftFilt *  f,
vl_sift_pix *  descr,
VlSiftKeypoint const *  k,
double  angle0 
)
Parameters:
fSIFT filter.
descrSIFT descriptor (output)
kkeypoint.
angle0keypoint direction.

The function computes the SIFT descriptor of the keypoint k of orientation angle0. The function fills the buffer descr which must be large enough to hold the descriptor.

The function assumes that the keypoint is on the current octave. If not, it does not do anything.

int vl_sift_calc_keypoint_orientations ( VlSiftFilt *  f,
double  angles[4],
VlSiftKeypoint const *  k 
)
Parameters:
fSIFT filter.
anglesorientations (output).
kkeypoint.

The function computes the orientation(s) of the keypoint k. The function returns the number of orientations found (up to four). The orientations themselves are written to the vector angles.

Remarks:
The function requires the keypoint octave k->o to be equal to the filter current octave vl_sift_get_octave. If this is not the case, the function returns zero orientations.
The function requires the keypoint scale level k->s to be in the range s_min+1 and s_max-2 (where usually s_min=0 and s_max=S+2). If this is not the case, the function returns zero orientations.
Returns:
number of orientations found.
void vl_sift_calc_raw_descriptor ( VlSiftFilt const *  f,
vl_sift_pix const *  grad,
vl_sift_pix *  descr,
int  width,
int  height,
double  x,
double  y,
double  sigma,
double  angle0 
)
Parameters:
fSIFT filter.
gradimage gradients.
descrSIFT descriptor (output).
widthimage width.
heightimage height.
xkeypoint x coordinate.
ykeypoint y coordinate.
sigmakeypoint scale.
angle0keypoint orientation.

The function runs the SIFT descriptor on raw data. Here image is a 2 x width x height array (by convention, the memory layout is a s such the first index is the fastest varying one). The first width x height layer of the array contains the gradient magnitude and the second the gradient angle (in radians, between 0 and $ 2\pi $). x, y and sigma give the keypoint center and scale respectively.

In order to be equivalent to a standard SIFT descriptor the image gradient must be computed at a smoothing level equal to the scale of the keypoint. In practice, the actual SIFT algorithm makes the following additional approximation, which influence the result:

  • Scale is discretized in S levels.
  • The image is downsampled once for each octave (if you do this, the parameters x, y and sigma must be scaled too).
void vl_sift_delete ( VlSiftFilt *  f)
Parameters:
fSIFT filter to delete.

The function frees the resources allocated by vl_sift_new().

void vl_sift_detect ( VlSiftFilt *  f)

The function detect keypoints in the current octave filling the internal keypoint buffer. Keypoints can be retrieved by vl_sift_get_keypoints().

Parameters:
fSIFT filter.

Index GSS

Index matrix A

double vl_sift_get_edge_thresh ( VlSiftFilt const *  f) [inline]
Parameters:
fSIFT filter.
Returns:
threshold.
VlSiftKeypoint const * vl_sift_get_keypoints ( VlSiftFilt const *  f) [inline]
Parameters:
fSIFT filter.
Returns:
pointer to the keypoints list.
double vl_sift_get_magnif ( VlSiftFilt const *  f) [inline]
Parameters:
fSIFT filter.
Returns:
magnification factor.
int vl_sift_get_nkeypoints ( VlSiftFilt const *  f) [inline]
Parameters:
fSIFT filter.
Returns:
number of keypoints.
int vl_sift_get_nlevels ( VlSiftFilt const *  f) [inline]
Parameters:
fSIFT filter.
Returns:
number of leves per octave.
int vl_sift_get_noctaves ( VlSiftFilt const *  f) [inline]
Parameters:
fSIFT filter.
Returns:
number of octaves.
double vl_sift_get_norm_thresh ( VlSiftFilt const *  f) [inline]
Parameters:
fSIFT filter.
Returns:
threshold.
vl_sift_pix * vl_sift_get_octave ( VlSiftFilt const *  f,
int  s 
) [inline]
Parameters:
fSIFT filter.
slevel index.

The level index s ranges in the interval s_min = -1 and s_max = S + 2, where S is the number of levels per octave.

Returns:
pointer to the octave data for level s.
int vl_sift_get_octave_first ( VlSiftFilt const *  f) [inline]

-------------------------------------------------------------------

Parameters:
fSIFT filter.
Returns:
index of the first octave.
int vl_sift_get_octave_height ( VlSiftFilt const *  f) [inline]
Parameters:
fSIFT filter.
Returns:
current octave height.
int vl_sift_get_octave_index ( VlSiftFilt const *  f) [inline]
Parameters:
fSIFT filter.
Returns:
index of the current octave.
int vl_sift_get_octave_width ( VlSiftFilt const *  f) [inline]
Parameters:
fSIFT filter.
Returns:
current octave width.
double vl_sift_get_peak_thresh ( VlSiftFilt const *  f) [inline]
Parameters:
fSIFT filter.
Returns:
threshold ;
double vl_sift_get_window_size ( VlSiftFilt const *  f) [inline]
Parameters:
fSIFT filter.
Returns:
standard deviation of the Gaussian window (in spatial bin units).
void vl_sift_keypoint_init ( VlSiftFilt const *  f,
VlSiftKeypoint *  k,
double  x,
double  y,
double  sigma 
)
Parameters:
fSIFT filter.
kSIFT keypoint (output).
xx coordinate of the keypoint center.
yy coordinate of the keypoint center.
sigmakeypoint scale.

The function initializes a keypoint structure k from the location x and y and the scale sigma of the keypoint. The keypoint structure maps the keypoint to an octave and scale level of the discretized Gaussian scale space, which is required for instance to compute the keypoint SIFT descriptor.

Algorithm

The formula linking the keypoint scale sigma to the octave and scale indexes is

\[ \sigma(o,s) = \sigma_0 2^{o+s/S} \]

In addition to the scale index s (which can be fractional due to scale interpolation) a keypoint has an integer scale index is too (which is the index of the scale level where it was detected in the DoG scale space). We have the constraints (Detector see also the "SIFT detector"):

  • o is integer in the range $ [o_\mathrm{min}, o_{\mathrm{min}}+O-1] $.
  • is is integer in the range $ [s_\mathrm{min}+1, s_\mathrm{max}-2] $. This depends on how the scale is determined during detection, and must be so here because gradients are computed only for this range of scale levels and are required for the calculation of the SIFT descriptor.
  • $ |s - is| < 0.5 $ for detected keypoints in most cases due to the interpolation technique used during detection. However this is not necessary.

Thus octave o represents scales $ \{ \sigma(o, s) : s \in [s_\mathrm{min}+1-.5, s_\mathrm{max}-2+.5] \} $. Note that some scales may be represented more than once. For each scale, we select the largest possible octave that contains it, i.e.

\[ o(\sigma) = \max \{ o \in \mathbb{Z} : \sigma_0 2^{\frac{s_\mathrm{min}+1-.5}{S}} \leq \sigma \} = \mathrm{floor}\,\left[ \log_2(\sigma / \sigma_0) - \frac{s_\mathrm{min}+1-.5}{S}\right] \]

and then

\[ s(\sigma) = S \left[\log_2(\sigma / \sigma_0) - o(\sigma)\right], \quad is(\sigma) = \mathrm{round}\,(s(\sigma)) \]

In practice, both $ o(\sigma) $ and $ is(\sigma) $ are clamped to their feasible range as determined by the SIFT filter parameters.

VlSiftFilt* vl_sift_new ( int  width,
int  height,
int  noctaves,
int  nlevels,
int  o_min 
)
Parameters:
widthimage width.
heightimage height.
noctavesnumber of octaves.
nlevelsnumber of levels per octave.
o_minfirst octave index.

The function allocates and returns a new SIFT filter for the specified image and scale space geometry.

Setting O to a negative value sets the number of octaves to the maximum possible value depending on the size of the image.

Returns:
the new SIFT filter.
See also:
vl_sift_delete().
int vl_sift_process_first_octave ( VlSiftFilt *  f,
vl_sift_pix const *  im 
)
Parameters:
fSIFT filter.
imimage data.

The function starts processing a new image by computing its Gaussian scale space at the lower octave. It also empties the internal keypoint buffer.

Returns:
error code. The function returns VL_ERR_EOF if there are no more octaves to process.
See also:
vl_sift_process_next_octave().
int vl_sift_process_next_octave ( VlSiftFilt *  f)
Parameters:
fSIFT filter.

The function computes the next octave of the Gaussian scale space. Notice that this clears the record of any feature detected in the previous octave.

Returns:
error code. The function returns the error VL_ERR_EOF when there are no more octaves to process.
See also:
vl_sift_process_first_octave().
void vl_sift_set_edge_thresh ( VlSiftFilt *  f,
double  t 
) [inline]
Parameters:
fSIFT filter.
tthreshold.
void vl_sift_set_magnif ( VlSiftFilt *  f,
double  m 
) [inline]
Parameters:
fSIFT filter.
mmagnification factor.
void vl_sift_set_norm_thresh ( VlSiftFilt *  f,
double  t 
) [inline]
Parameters:
fSIFT filter.
tthreshold.
void vl_sift_set_peak_thresh ( VlSiftFilt *  f,
double  t 
) [inline]
Parameters:
fSIFT filter.
tthreshold.
void vl_sift_set_window_size ( VlSiftFilt *  f,
double  x 
) [inline]
Parameters:
fSIFT filter.
xGaussian window size (in units of spatial bin).

This is the parameter $ \hat \sigma_{\text{win}} $ of the standard SIFT descriptor Standard SIFT descriptor.

 
vlfeat-0.9.14/doc/api/slic.html000066400000000000000000000221521167540017400162330ustar00rootroot00000000000000 VLFeat - Documentation - C API
Documentation - C API
Simple Linear Iterative Clustering (SLIC)
Author:
Andrea Vedaldi

slic.h implements the SLIC superpixels, an image segmentation algorithm described by [1] .

Overview

SLIC [1] is a simple and efficient method to decompose an image in visually homogeneous regions. It is based on a spatially localized version of k-means clustering. Similar to mean shift or quick shift (quickshift.h), each pixel is associated to a feature vector

\[ \Psi(x,y) = \left[ \begin{array}{c} \lambda x \\ \lambda y \\ I(x,y) \end{array} \right] \]

and then k-means clustering is run on those. As discussed below, the coefficient $ \lambda $ balances the spatial and appearance components of the feature vectors, imposing a degree of spatial regularization to the extracted regions.

SLIC takes two parameters: the nominal size of the regions (superpixels) regionSize and the strength of the spatial regularization regularizer. The image is first divided into a grid with step regionSize. The center of each grid tile is then used to initialize a corresponding k-means (up to a small shift to avoid image edges). Finally, the k-means centers and clusters are refined by using the Lloyd algorithm, yielding segmenting the image. As a further restriction and simplification, during the k-means iterations each pixel can be assigned to only the 22 centers corresponding to grid tiles adjacent to the pixel.

The parameter regularizer sets the trade-off between clustering appearance and spatial regularization. This is obtained by setting

\[ \lambda = \frac{\mathtt{regularizer}}{\mathtt{regionSize}} \]

in the definition of the feature $ \psi(x,y) $.

After the k-means step, SLIC optionally removes any segment whose area is smaller than a threshld minRegionSize by merging them into larger ones.

Usage from the C library

To compute the SLIC superpixels of an image use the function vl_slic_segment.

Technical details

SLIC starts by dividing the image domain into a regular grid with $ M \times N $ tiles, where

\[ M = \lceil \frac{\mathtt{imageWidth}}{\mathtt{regionSize}} \rceil, \quad N = \lceil \frac{\mathtt{imageHeight}}{\mathtt{regionSize}} \rceil. \]

A region (superpixel or k-means cluster) is initialized from each grid center

\[ x_i = \operatorname{round} i \frac{\mathtt{imageWidth}}{\mathtt{regionSize}} \quad y_j = \operatorname{round} j \frac{\mathtt{imageWidth}}{\mathtt{regionSize}}. \]

In order to avoid placing these centers on top of image discontinuities, the centers are then moved in a 33 neighbourohood to minimize the edge strength

\[ \operatorname{edge}(x,y) = \| I(x+1,y) - I(x-1,y) \|_2^2 + \| I(x,y+1) - I(x,y-1) \|_2^2. \]

Then the regions are obtained by running k-means clustering, started from the centers

\[ C = \{ \Psi(x_i,y_j), i=0,1,\dots,M-1\ j=0,1,\dots,N-1 \} \]

thus obtained. K-means uses the standard LLoyd algorithm alternating assigning pixels to the clostest centers a re-estiamting the centers as the average of the corresponding feature vectors of the pixel assigned to them. The only difference compared to standard k-means is that each pixel can be assigned only to the center originated from the neighbour tiles. This guarantees that there are exactly four pixel-to-center comparisons at each round of minimization, which threfore cost $ O(n) $, where $ n $ is the number of superpixels.

After k-means has converged, SLIC eliminates any connected region whose area is less than minRegionSize pixels. This is done by greedily merging regions to neighbour ones: the pixels $ p $ are scanned in lexicographical order and the corresponding connected components are visited. If a region has already been visited, it is skipped; if not, its area is computed and if this is less than minRegionSize its label is changed to the one of a neighbour region at $ p $ that has already been vistied (there is always one except for the very first pixel).

 
vlfeat-0.9.14/doc/api/slic_8c.html000066400000000000000000000225231167540017400166270ustar00rootroot00000000000000 VLFeat - Documentation - C API
Documentation - C API
Functions
slic.c File Reference

SLIC superpixels - Definition. More...

#include "slic.h"
#include "mathop.h"
#include <math.h>
#include <string.h>

Functions

void vl_slic_segment (vl_uint32 *segmentation, float const *image, vl_size width, vl_size height, vl_size numChannels, vl_size regionSize, float regularization, vl_size minRegionSize)
 SLIC superpixel segmentation.

Detailed Description

Author:
Andrea Vedaldi

Function Documentation

void vl_slic_segment ( vl_uint32 *  segmentation,
float const *  image,
vl_size  width,
vl_size  height,
vl_size  numChannels,
vl_size  regionSize,
float  regularization,
vl_size  minRegionSize 
)
Parameters:
segmentationsegmentation.
imageimage to segment.
widthimage width.
heightimage height.
numChannelsnumber of image channels (depth).
regionSizenominal size of the regions.
regularizationtrade-off between appearance and spatial terms.
minRegionSizeminimum size of a segment.

The function computes the SLIC superpixels of the specified image image. image is a pointer to an width by height by by numChannles array of float. segmentation is a pointer to a width by height array of vl_uint32. segmentation contain the labels of each image pixels, from 0 to the number of regions minus one.

See also:
Overview, Technical details
 
vlfeat-0.9.14/doc/api/slic_8h.html000066400000000000000000000223541167540017400166360ustar00rootroot00000000000000 VLFeat - Documentation - C API
Documentation - C API
Functions
slic.h File Reference

SLIC superpixels (Simple Linear Iterative Clustering (SLIC)) More...

#include "generic.h"

Functions

void vl_slic_segment (vl_uint32 *segmentation, float const *image, vl_size width, vl_size height, vl_size numChannels, vl_size regionSize, float regularization, vl_size minRegionSize)
 SLIC superpixel segmentation.

Detailed Description

Author:
Andrea Vedaldi

Function Documentation

void vl_slic_segment ( vl_uint32 *  segmentation,
float const *  image,
vl_size  width,
vl_size  height,
vl_size  numChannels,
vl_size  regionSize,
float  regularization,
vl_size  minRegionSize 
)
Parameters:
segmentationsegmentation.
imageimage to segment.
widthimage width.
heightimage height.
numChannelsnumber of image channels (depth).
regionSizenominal size of the regions.
regularizationtrade-off between appearance and spatial terms.
minRegionSizeminimum size of a segment.

The function computes the SLIC superpixels of the specified image image. image is a pointer to an width by height by by numChannles array of float. segmentation is a pointer to a width by height array of vl_uint32. segmentation contain the labels of each image pixels, from 0 to the number of regions minus one.

See also:
Overview, Technical details
 
vlfeat-0.9.14/doc/api/stringop_8c.html000066400000000000000000000632151167540017400175450ustar00rootroot00000000000000 VLFeat - Documentation - C API
Documentation - C API
Functions
stringop.c File Reference

String operations - Definition. More...

#include "stringop.h"
#include <string.h>
#include <ctype.h>

Functions

char * vl_string_parse_protocol (char const *string, int *protocol)
 Extract the protocol prefix from a string.
char const * vl_string_protocol_name (int protocol)
 Get protocol name.
vl_size vl_string_basename (char *destination, vl_size destinationSize, char const *source, vl_size maxNumStrippedExtensions)
 Extract base of file name.
vl_size vl_string_replace_wildcard (char *destination, vl_size destinationSize, char const *source, char wildcardChar, char escapeChar, char const *replacement)
 Replace wildcard characters by a string.
vl_size vl_string_copy (char *destination, vl_size destinationSize, char const *source)
 Copy string.
vl_size vl_string_copy_sub (char *destination, vl_size destinationSize, char const *beginning, char const *end)
 Copy substring.
char * vl_string_find_char_rev (char const *beginning, char const *end, char c)
 Search character in reversed order.
vl_size vl_string_length (char const *string)
 Calculate string length.
int vl_string_casei_cmp (const char *string1, const char *string2)
 Compare strings case-insensitive.

Detailed Description

Author:
Andrea Vedaldi

Function Documentation

vl_size vl_string_basename ( char *  destination,
vl_size  destinationSize,
char const *  source,
vl_size  maxNumStrippedExtensions 
)
Parameters:
destinationdestination buffer.
destinationSizesize of destination buffer.
sourceinput string.
maxNumStrippedExtensionsmaximum number of extensions to strip.
Returns:
length of the destination string.

The function removes the leading path and up to maxNumStrippedExtensions trailing extensions from the string source and writes the result to the buffer destination.

The leading path is the longest suffix that ends with either the \ or / characters. An extension is a string starting with the . character not containing it. For instance, the string file.png contains the extension .png and the string file.tar.gz contains two extensions (.tar and .gz).

See also:
Detecting overflow.
int vl_string_casei_cmp ( const char *  string1,
const char *  string2 
)
Parameters:
string1fisrt string.
string2second string.
Returns:
an integer =,<,> 0 if string1 =,<,> string2
vl_size vl_string_copy ( char *  destination,
vl_size  destinationSize,
char const *  source 
)
Parameters:
destinationoutput buffer.
destinationSizesize of the output buffer.
sourcestring to copy.
Returns:
length of the source string.

The function copies the string source to the buffer destination of size destinationSize.

See also:
Detecting overflow.
vl_size vl_string_copy_sub ( char *  destination,
vl_size  destinationSize,
char const *  beginning,
char const *  end 
)
Parameters:
destinationoutput buffer.
destinationSizesize of output buffer.
beginningstart of the substring.
endend of the substring.
Returns:
length of the destination string.

The function copies the substring from at beginning to end (not included) to the buffer destination of size destinationSize. If, however, the null character is found before end, the substring terminates there.

See also:
Detecting overflow.
char* vl_string_find_char_rev ( char const *  beginning,
char const *  end,
char  c 
)
Parameters:
beginningpointer to the substring beginning.
endpointer to the substring end.
ccharacter to search for.
Returns:
pointer to last occurrence of c, or 0 if none.

The function searches for the last occurrence of the character c in the substring from beg to end (the latter not being included).

vl_size vl_string_length ( char const *  string)
Parameters:
stringstring.
Returns:
string length.
char* vl_string_parse_protocol ( char const *  string,
int *  protocol 
)
Parameters:
stringstring.
protocolprotocol code (output).
Returns:
pointer to the first character after the protocol prefix.

The function extracts the prefix of the string string terminated by the first occurrence of the :// substring (if any). It then matches the suffix terminated by :// to the supported File protocols protocols. If protocol is not NULL, the corresponding protocol code is written to protocol

The function writes to protocol the value VL_PROT_NONE if no suffix is detected and VL_PROT_UNKNOWN if there is a suffix but it cannot be matched to any of the supported protocols.

char const* vl_string_protocol_name ( int  protocol)
Parameters:
protocolprotocol code.
Returns:
pointer protocol name string.

The function returns a pointer to a string containing the name of the protocol protocol (see the vl-file-protocols protocols list). If the protocol is unknown the function returns the empty string.

vl_size vl_string_replace_wildcard ( char *  destination,
vl_size  destinationSize,
char const *  source,
char  wildcardChar,
char  escapeChar,
char const *  replacement 
)
Parameters:
destinationoutput buffer.
destinationSizesize of the output buffer.
sourceinput string.
wildcardCharwildcard character.
escapeCharescape character.
replacementreplacement string.

The function replaces the occurrence of the specified wildcard character wildcardChar by the string replacement. The result is written to the buffer destination of size destinationSize.

Wildcard characters may be escaped by preceding them by the esc character. More in general, anything following an occurrence of esc character is copied verbatim. To disable the escape characters simply set esc to 0.

Returns:
length of the result.
See also:
Detecting overflow.
 
vlfeat-0.9.14/doc/api/stringop_8h.html000066400000000000000000000735731167540017400175620ustar00rootroot00000000000000 VLFeat - Documentation - C API
Documentation - C API
Enumerations | Functions
stringop.h File Reference

String operations. More...

#include "generic.h"

Enumerations

enum  { VL_PROT_UNKNOWN = -1, VL_PROT_NONE = 0, VL_PROT_ASCII, VL_PROT_BINARY }

Functions

vl_size vl_string_copy (char *destination, vl_size destinationSize, char const *source)
 Copy string.
vl_size vl_string_copy_sub (char *destination, vl_size destinationSize, char const *beginning, char const *end)
 Copy substring.
char * vl_string_parse_protocol (char const *string, int *protocol)
 Extract the protocol prefix from a string.
char const * vl_string_protocol_name (int prot)
 Get protocol name.
vl_size vl_string_basename (char *destination, vl_size destinationSize, char const *source, vl_size maxNumStrippedExtension)
 Extract base of file name.
vl_size vl_string_replace_wildcard (char *destination, vl_size destinationSize, char const *src, char wildcardChar, char escapeChar, char const *replacement)
 Replace wildcard characters by a string.
char * vl_string_find_char_rev (char const *beginning, char const *end, char c)
 Search character in reversed order.
vl_size vl_string_length (char const *string)
 Calculate string length.
int vl_string_casei_cmp (const char *string1, const char *string2)
 Compare strings case-insensitive.

Detailed Description

Author:
Andrea Vedaldi

stringop.h implements basic string operations. All functions that write to strings use range checking, which makes them safer than some standard POSIX equivalent (see Detecting overflow).

File protocols

stringop.h defines a few file "protocols" and helps parsing them from URL-like formatted strings. The supported protocols are:

File protocols
ProtocolCodeURL prefix
ASCIIVL_PROT_ASCIIascii://
BINARYVL_PROT_BINARYbinary://

Detecting overflow

stringop.h functions that write a string to a character buffer take both the buffer and its size n as input. If n is not large enough, the output may be truncated but it is always a null terminated string (provided that n >= 1). Such functions also return the length of the string that would have been written r (which does not include the terminating null character) had the buffer been large enough. Hence an overflow can be detected by testing if r >= n, r can be used to re-allocate a buffer large enough to contain the result, and the operation can be repeated.

Author:
Andrea Vedaldi

Enumeration Type Documentation

anonymous enum
Enumerator:
VL_PROT_UNKNOWN 

unknown protocol

VL_PROT_NONE 

no protocol

VL_PROT_ASCII 

ASCII protocol

VL_PROT_BINARY 

Binary protocol

Function Documentation

vl_size vl_string_basename ( char *  destination,
vl_size  destinationSize,
char const *  source,
vl_size  maxNumStrippedExtensions 
)
Parameters:
destinationdestination buffer.
destinationSizesize of destination buffer.
sourceinput string.
maxNumStrippedExtensionsmaximum number of extensions to strip.
Returns:
length of the destination string.

The function removes the leading path and up to maxNumStrippedExtensions trailing extensions from the string source and writes the result to the buffer destination.

The leading path is the longest suffix that ends with either the \ or / characters. An extension is a string starting with the . character not containing it. For instance, the string file.png contains the extension .png and the string file.tar.gz contains two extensions (.tar and .gz).

See also:
Detecting overflow.
int vl_string_casei_cmp ( const char *  string1,
const char *  string2 
)
Parameters:
string1fisrt string.
string2second string.
Returns:
an integer =,<,> 0 if string1 =,<,> string2
vl_size vl_string_copy ( char *  destination,
vl_size  destinationSize,
char const *  source 
)
Parameters:
destinationoutput buffer.
destinationSizesize of the output buffer.
sourcestring to copy.
Returns:
length of the source string.

The function copies the string source to the buffer destination of size destinationSize.

See also:
Detecting overflow.
vl_size vl_string_copy_sub ( char *  destination,
vl_size  destinationSize,
char const *  beginning,
char const *  end 
)
Parameters:
destinationoutput buffer.
destinationSizesize of output buffer.
beginningstart of the substring.
endend of the substring.
Returns:
length of the destination string.

The function copies the substring from at beginning to end (not included) to the buffer destination of size destinationSize. If, however, the null character is found before end, the substring terminates there.

See also:
Detecting overflow.
char* vl_string_find_char_rev ( char const *  beginning,
char const *  end,
char  c 
)
Parameters:
beginningpointer to the substring beginning.
endpointer to the substring end.
ccharacter to search for.
Returns:
pointer to last occurrence of c, or 0 if none.

The function searches for the last occurrence of the character c in the substring from beg to end (the latter not being included).

vl_size vl_string_length ( char const *  string)
Parameters:
stringstring.
Returns:
string length.
char* vl_string_parse_protocol ( char const *  string,
int *  protocol 
)
Parameters:
stringstring.
protocolprotocol code (output).
Returns:
pointer to the first character after the protocol prefix.

The function extracts the prefix of the string string terminated by the first occurrence of the :// substring (if any). It then matches the suffix terminated by :// to the supported File protocols protocols. If protocol is not NULL, the corresponding protocol code is written to protocol

The function writes to protocol the value VL_PROT_NONE if no suffix is detected and VL_PROT_UNKNOWN if there is a suffix but it cannot be matched to any of the supported protocols.

char const* vl_string_protocol_name ( int  protocol)
Parameters:
protocolprotocol code.
Returns:
pointer protocol name string.

The function returns a pointer to a string containing the name of the protocol protocol (see the vl-file-protocols protocols list). If the protocol is unknown the function returns the empty string.

vl_size vl_string_replace_wildcard ( char *  destination,
vl_size  destinationSize,
char const *  source,
char  wildcardChar,
char  escapeChar,
char const *  replacement 
)
Parameters:
destinationoutput buffer.
destinationSizesize of the output buffer.
sourceinput string.
wildcardCharwildcard character.
escapeCharescape character.
replacementreplacement string.

The function replaces the occurrence of the specified wildcard character wildcardChar by the string replacement. The result is written to the buffer destination of size destinationSize.

Wildcard characters may be escaped by preceding them by the esc character. More in general, anything following an occurrence of esc character is copied verbatim. To disable the escape characters simply set esc to 0.

Returns:
length of the result.
See also:
Detecting overflow.
 
vlfeat-0.9.14/doc/api/structVlAIB.html000066400000000000000000000357711167540017400174560ustar00rootroot00000000000000 VLFeat - Documentation - C API
Documentation - C API
Data Fields
VlAIB Struct Reference

AIB algorithm data. More...

#include <aib.h>

Data Fields

vl_uint * nodes
vl_uint nentries
double * beta
vl_uint * bidx
vl_uint * which
vl_uint nwhich
double * Pcx
double * Px
double * Pc
vl_uint nvalues
vl_uint nlabels
vl_uint * parents
double * costs

Detailed Description

The implementation is quite straightforward, but the way feature values are handled in order to support efficient joins, deletions and re-arrangement needs to be explained. This is achieved by adding a layer of indirection:

  • Call each feature value (either original or obtained by a join operation) a node. Nodes are identified by numbers.
  • Call each element of the various arrays (such as VlAIB::Px) an entry.
  • Entries are dynamically associated to nodes as specified by VlAIB::nodes. For example, Px[i] refers to the node nodes[i].

Field Documentation

double* VlAIB::beta

Minimum distance to an entry

vl_uint* VlAIB::bidx

Closest entry

double* VlAIB::costs

Cost of each merge

vl_uint VlAIB::nentries

Total number of entries (= # active nodes)

vl_uint VlAIB::nlabels

Number of labels

vl_uint* VlAIB::nodes

Entires to nodes

vl_uint VlAIB::nvalues

Number of feature values

vl_uint VlAIB::nwhich

Number of entries to update

vl_uint* VlAIB::parents

Array of parents

double* VlAIB::Pc

Marginal.

double* VlAIB::Pcx

Joint probability table

double* VlAIB::Px

Marginal.

vl_uint* VlAIB::which

List of entries to update

The documentation for this struct was generated from the following file:
 
vlfeat-0.9.14/doc/api/structVlArray.html000066400000000000000000000102401167540017400201210ustar00rootroot00000000000000 VLFeat - Documentation - C API
Documentation - C API
VlArray Struct Reference

Numeric array.

#include <array.h>

The documentation for this struct was generated from the following file:
 
vlfeat-0.9.14/doc/api/structVlDsiftDescriptorGeometry.html000066400000000000000000000201731167540017400236750ustar00rootroot00000000000000 VLFeat - Documentation - C API
Documentation - C API
Data Fields
VlDsiftDescriptorGeometry Struct Reference

Dense SIFT descriptor geometry.

#include <dsift.h>

Data Fields

int numBinT
int numBinX
int numBinY
int binSizeX
int binSizeY

Field Documentation

int VlDsiftDescriptorGeometry::binSizeX

size of bins along X

int VlDsiftDescriptorGeometry::binSizeY

size of bins along Y

int VlDsiftDescriptorGeometry::numBinT

number of orientation bins

int VlDsiftDescriptorGeometry::numBinX

number of bins along X

int VlDsiftDescriptorGeometry::numBinY

number of bins along Y

The documentation for this struct was generated from the following file:
 
vlfeat-0.9.14/doc/api/structVlDsiftFilter.html000066400000000000000000000471251167540017400212760ustar00rootroot00000000000000 VLFeat - Documentation - C API
Documentation - C API
Data Fields
VlDsiftFilter Struct Reference

Dense SIFT filter.

#include <dsift.h>

Data Fields

int imWidth
 image width
int imHeight
 image height
int stepX
int stepY
int boundMinX
int boundMinY
int boundMaxX
int boundMaxY
VlDsiftDescriptorGeometry geom
int useFlatWindow
double windowSize
int numFrames
int descrSize
VlDsiftKeypoint * frames
float * descrs
int numBinAlloc
int numFrameAlloc
int numGradAlloc
float ** grads
float * convTmp1
float * convTmp2

Field Documentation

int VlDsiftFilter::boundMaxX

frame bounding box max X

int VlDsiftFilter::boundMaxY

frame bounding box max Y

int VlDsiftFilter::boundMinX

frame bounding box min X

int VlDsiftFilter::boundMinY

frame bounding box min Y

float* VlDsiftFilter::convTmp1

temporary buffer

float* VlDsiftFilter::convTmp2

temporary buffer

float* VlDsiftFilter::descrs

descriptor buffer

int VlDsiftFilter::descrSize

size of a descriptor

VlDsiftKeypoint* VlDsiftFilter::frames

frame buffer

VlDsiftDescriptorGeometry VlDsiftFilter::geom

descriptor parameters

float** VlDsiftFilter::grads

gradient buffer

int VlDsiftFilter::imHeight
int VlDsiftFilter::imWidth
int VlDsiftFilter::numBinAlloc

buffer allocated: descriptor size

int VlDsiftFilter::numFrameAlloc

buffer allocated: number of frames

int VlDsiftFilter::numFrames

number of sampled frames

int VlDsiftFilter::numGradAlloc

buffer allocated: number of orientations

int VlDsiftFilter::stepX

frame sampling step X

int VlDsiftFilter::stepY

frame sampling step Y

int VlDsiftFilter::useFlatWindow

flag: whether to approximate the Gaussian window with a flat one

double VlDsiftFilter::windowSize

size of the Gaussian window

The documentation for this struct was generated from the following file:
 
vlfeat-0.9.14/doc/api/structVlDsiftKeypoint.html000066400000000000000000000161641167540017400216520ustar00rootroot00000000000000 VLFeat - Documentation - C API
Documentation - C API
Data Fields
VlDsiftKeypoint Struct Reference

Dense SIFT keypoint.

#include <dsift.h>

Data Fields

double x
double y
double s
double norm

Field Documentation

double VlDsiftKeypoint::norm

SIFT descriptor norm

double VlDsiftKeypoint::s

scale

double VlDsiftKeypoint::x

x coordinate

double VlDsiftKeypoint::y

y coordinate

The documentation for this struct was generated from the following file:
 
vlfeat-0.9.14/doc/api/structVlHIKMNode.html000066400000000000000000000142131167540017400204050ustar00rootroot00000000000000 VLFeat - Documentation - C API
Documentation - C API
Data Fields
VlHIKMNode Struct Reference

HIKM tree node. More...

#include <hikmeans.h>

Data Fields

VlIKMFilt * filter
struct _VlHIKMNode ** children

Detailed Description

The number of children K is not bigger than the K parameter of the HIKM tree.

Field Documentation

struct _VlHIKMNode** VlHIKMNode::children

Node children (if any)

VlIKMFilt* VlHIKMNode::filter

IKM filter for this node

The documentation for this struct was generated from the following file:
 
vlfeat-0.9.14/doc/api/structVlHIKMTree.html000066400000000000000000000222411167540017400204170ustar00rootroot00000000000000 VLFeat - Documentation - C API
Documentation - C API
Data Fields
VlHIKMTree Struct Reference

HIKM tree.

#include <hikmeans.h>

Data Fields

int M
int K
int max_niters
int method
int verb
int depth
VlHIKMNode * root

Field Documentation

int VlHIKMTree::depth

Depth of the tree

int VlHIKMTree::K

IKM: K

int VlHIKMTree::M

IKM: data dimensionality

int VlHIKMTree::max_niters

IKM: maximum # of iterations

int VlHIKMTree::method

IKM: method

VlHIKMNode* VlHIKMTree::root

Tree root node

int VlHIKMTree::verb

Verbosity level

The documentation for this struct was generated from the following file:
 
vlfeat-0.9.14/doc/api/structVlHomogeneousKernelMap.html000066400000000000000000000103231167540017400231340ustar00rootroot00000000000000 VLFeat - Documentation - C API
Documentation - C API
VlHomogeneousKernelMap Struct Reference

Homogeneous kernel map.

#include <homkermap.h>

The documentation for this struct was generated from the following file:
 
vlfeat-0.9.14/doc/api/structVlIKMFilt.html000066400000000000000000000226131167540017400203110ustar00rootroot00000000000000 VLFeat - Documentation - C API
Documentation - C API
Data Fields
VlIKMFilt Struct Reference

IKM quantizer.

#include <ikmeans.h>

Data Fields

int M
int K
int method
int max_niters
int verb
vl_ikm_acc * centers
vl_ikm_acc * inter_dist

Field Documentation

vl_ikm_acc* VlIKMFilt::centers

centers

vl_ikm_acc* VlIKMFilt::inter_dist

centers inter-distances

int VlIKMFilt::K

number of centers

int VlIKMFilt::M

data dimensionality

int VlIKMFilt::max_niters

Lloyd: maximum number of iterations

int VlIKMFilt::method

Learning method

int VlIKMFilt::verb

verbosity level

The documentation for this struct was generated from the following file:
 
vlfeat-0.9.14/doc/api/structVlKDForest.html000066400000000000000000000102531167540017400205300ustar00rootroot00000000000000 VLFeat - Documentation - C API
Documentation - C API
VlKDForest Struct Reference

KDForest object.

#include <kdtree.h>

The documentation for this struct was generated from the following file:
 
vlfeat-0.9.14/doc/api/structVlKDForestNeighbor.html000066400000000000000000000140071167540017400222070ustar00rootroot00000000000000 VLFeat - Documentation - C API
Documentation - C API
Data Fields
VlKDForestNeighbor Struct Reference

Neighbor of a query point.

#include <kdtree.h>

Data Fields

double distance
vl_uindex index

Field Documentation

double VlKDForestNeighbor::distance

distance to the query point

vl_uindex VlKDForestNeighbor::index

index of the neighbor in the KDTree data

The documentation for this struct was generated from the following file:
 
vlfeat-0.9.14/doc/api/structVlKMeans.html000066400000000000000000000315331167540017400202310ustar00rootroot00000000000000 VLFeat - Documentation - C API
Documentation - C API
Data Fields
VlKMeans Struct Reference

K-means quantizer.

#include <kmeans.h>

Data Fields

vl_type dataType
vl_size dimension
vl_size numCenters
VlKMeansAlgorithm algorithm
VlVectorComparisonType distance
vl_size maxNumIterations
vl_size numRepetitions
int verbosity
void * centers
void * centerDistances
double energy

Field Documentation

VlKMeansAlgorithm VlKMeans::algorithm

< Initalization algorithm Clustring algorithm

void* VlKMeans::centerDistances

centers inter-distances

void* VlKMeans::centers

centers

vl_type VlKMeans::dataType

data type

vl_size VlKMeans::dimension

data dimensionality

VlVectorComparisonType VlKMeans::distance

Distance

double VlKMeans::energy

current solution energy

vl_size VlKMeans::maxNumIterations

Maximum number of refinement iterations

vl_size VlKMeans::numCenters

number of centers

vl_size VlKMeans::numRepetitions

Number of clustering repetitions

int VlKMeans::verbosity

verbosity level

The documentation for this struct was generated from the following file:
 
vlfeat-0.9.14/doc/api/structVlLbp.html000066400000000000000000000102471167540017400175670ustar00rootroot00000000000000 VLFeat - Documentation - C API
Documentation - C API
VlLbp Struct Reference

Local Binary Pattern extractor.

#include <lbp.h>

The documentation for this struct was generated from the following file:
 
vlfeat-0.9.14/doc/api/structVlPgmImage.html000066400000000000000000000174401167540017400205420ustar00rootroot00000000000000 VLFeat - Documentation - C API
Documentation - C API
Data Fields
VlPgmImage Struct Reference

PGM image meta data. More...

#include <pgm.h>

Data Fields

int width
int height
int max_value
vl_bool is_raw

Detailed Description

A PGM image is a 2-D array of pixels of width width and height height. Each pixel is an integer one or two bytes wide, depending whether max_value is smaller than 256.

Field Documentation

int VlPgmImage::height

image height.

vl_bool VlPgmImage::is_raw

is RAW format?

int VlPgmImage::max_value

pixel maximum value (<= 2^16-1).

int VlPgmImage::width

image width.

The documentation for this struct was generated from the following file:
 
vlfeat-0.9.14/doc/api/structVlQS.html000066400000000000000000000166011167540017400173750ustar00rootroot00000000000000 VLFeat - Documentation - C API
Documentation - C API
Data Fields
VlQS Struct Reference

quick shift results More...

#include <quickshift.h>

Data Fields

vl_qs_type * image
int height
int width
int channels

Detailed Description

This implements quick shift mode seeking.

Field Documentation

int VlQS::channels

number of channels in the image

int VlQS::height

height of the image

vl_qs_type* VlQS::image

height x width x channels feature image

int VlQS::width

width of the image

The documentation for this struct was generated from the following file:
 
vlfeat-0.9.14/doc/api/structVlRand.html000066400000000000000000000102621167540017400177330ustar00rootroot00000000000000 VLFeat - Documentation - C API
Documentation - C API
VlRand Struct Reference

Random numbber generator state.

#include <random.h>

The documentation for this struct was generated from the following file:
 
vlfeat-0.9.14/doc/api/structVlSiftFilt.html000066400000000000000000000641231167540017400206000ustar00rootroot00000000000000 VLFeat - Documentation - C API
Documentation - C API
Data Fields
VlSiftFilt Struct Reference

SIFT filter. More...

#include <sift.h>

Data Fields

double sigman
double sigma0
double sigmak
double dsigma0
int width
int height
int O
int S
int o_min
int s_min
int s_max
int o_cur
vl_sift_pix * temp
vl_sift_pix * octave
vl_sift_pix * dog
int octave_width
int octave_height
vl_sift_pix * gaussFilter
double gaussFilterSigma
vl_size gaussFilterWidth
VlSiftKeypoint * keys
int nkeys
int keys_res
double peak_thresh
double edge_thresh
double norm_thresh
double magnif
double windowSize
vl_sift_pix * grad
int grad_o

Detailed Description

This filter implements the SIFT detector and descriptor.

Field Documentation

vl_sift_pix* VlSiftFilt::dog

current DoG data.

double VlSiftFilt::dsigma0

delta-smoothing.

double VlSiftFilt::edge_thresh

edge threshold.

vl_sift_pix* VlSiftFilt::gaussFilter

current Gaussian filter

double VlSiftFilt::gaussFilterSigma

current Gaussian filter std

vl_size VlSiftFilt::gaussFilterWidth

current Gaussian filter width

vl_sift_pix* VlSiftFilt::grad

GSS gradient data.

int VlSiftFilt::grad_o

GSS gradient data octave.

int VlSiftFilt::height

image height.

VlSiftKeypoint* VlSiftFilt::keys

detected keypoints.

int VlSiftFilt::keys_res

size of the keys buffer.

double VlSiftFilt::magnif

magnification factor.

int VlSiftFilt::nkeys

number of detected keypoints.

double VlSiftFilt::norm_thresh

norm threshold.

int VlSiftFilt::O

number of octaves.

int VlSiftFilt::o_cur

current octave.

int VlSiftFilt::o_min

minimum octave index.

vl_sift_pix* VlSiftFilt::octave

current GSS data.

int VlSiftFilt::octave_height

current octave height.

int VlSiftFilt::octave_width

current octave width.

double VlSiftFilt::peak_thresh

peak threshold.

int VlSiftFilt::S

number of levels per octave.

int VlSiftFilt::s_max

maximum level index.

int VlSiftFilt::s_min

minimum level index.

double VlSiftFilt::sigma0

smoothing of pyramid base.

double VlSiftFilt::sigmak

k-smoothing

double VlSiftFilt::sigman

nominal image smoothing.

vl_sift_pix* VlSiftFilt::temp

temporary pixel buffer.

int VlSiftFilt::width

image width.

double VlSiftFilt::windowSize

size of Gaussian window (in spatial bins)

The documentation for this struct was generated from the following file:
 
vlfeat-0.9.14/doc/api/structVlSiftKeypoint.html000066400000000000000000000242061167540017400215020ustar00rootroot00000000000000 VLFeat - Documentation - C API
Documentation - C API
Data Fields
VlSiftKeypoint Struct Reference

SIFT filter keypoint. More...

#include <sift.h>

Data Fields

int o
int ix
int iy
int is
float x
float y
float s
float sigma

Detailed Description

This structure represent a keypoint as extracted by the SIFT filter VlSiftFilt.

Field Documentation

int VlSiftKeypoint::is

Integer s coordinate.

int VlSiftKeypoint::ix

Integer unnormalized x coordinate.

int VlSiftKeypoint::iy

Integer unnormalized y coordinate.

int VlSiftKeypoint::o

o coordinate (octave).

float VlSiftKeypoint::s

s coordinate.

float VlSiftKeypoint::sigma

scale.

float VlSiftKeypoint::x

x coordinate.

float VlSiftKeypoint::y

y coordinate.

The documentation for this struct was generated from the following file:
 
vlfeat-0.9.14/doc/api/structVlState.html000066400000000000000000000105001167540017400201220ustar00rootroot00000000000000 VLFeat - Documentation - C API
Documentation - C API
VlState Struct Reference

VLFeat global state. More...

#include <generic.h>

Detailed Description

The documentation for this struct was generated from the following file:
 
vlfeat-0.9.14/doc/api/structVlThreadSpecificState.html000066400000000000000000000105521167540017400227270ustar00rootroot00000000000000 VLFeat - Documentation - C API
Documentation - C API
VlThreadSpecificState Struct Reference

VLFeat thread state. More...

#include <generic.h>

Detailed Description

The documentation for this struct was generated from the following file:
 
vlfeat-0.9.14/doc/api/struct__VlFileMeta.html000066400000000000000000000201251167540017400210320ustar00rootroot00000000000000 VLFeat - Documentation - C API
Documentation - C API
Data Fields
VlFileMeta Struct Reference

Support for command line drivers - Definition. More...

#include <generic-driver.h>

Data Fields

vl_bool active
char pattern [1024]
int protocol
char name [1024]
FILE * file

Detailed Description

File meta information type.

Author:
Andrea Vedaldi

This file contains support code which is shared by the command line drivers.File meta information

See also:
_VlFileMeta

Field Documentation

vl_bool VlFileMeta::active

Is the file active?

FILE* VlFileMeta::file

Current file stream

char VlFileMeta::name[1024]

Current file name

char VlFileMeta::pattern[1024]

File name pattern

int VlFileMeta::protocol

File protocol

The documentation for this struct was generated from the following file:
  • generic-driver.h
 
vlfeat-0.9.14/doc/api/struct__VlMserExtrReg.html000066400000000000000000000245621167540017400215640ustar00rootroot00000000000000 VLFeat - Documentation - C API
Documentation - C API
Data Fields
VlMserExtrReg Struct Reference

MSER: extremal region (declaration) More...

#include <mser.h>

Data Fields

int parent
int index
vl_mser_pix value
vl_uint shortcut
vl_uint area
float variation
vl_uint max_stable

Detailed Description

MSER: extremal region.

Extremal regions (ER) are extracted from the region forest. Each region is represented by an instance of this structure. The structures are stored into an array, in arbitrary order.

ER are arranged into a tree. parent points to the parent ER, or to itself if the ER is the root.

An instance of the structure represents the extremal region of the level set of intensity VlMserExtrReg::value and containing the pixel VlMserExtReg::index.

VlMserExtrReg::area is the area of the extremal region and VlMserExtrReg::area_top is the area of the extremal region containing this region in the level set of intensity VlMserExtrReg::area + delta.

VlMserExtrReg::variation is the relative area variation (area_top-area)/area.

VlMserExtrReg::max_stable is a flag signaling whether this extremal region is also maximally stable.

Field Documentation

vl_uint VlMserExtrReg::area

area of the region

int VlMserExtrReg::index

index of pivot pixel

vl_uint VlMserExtrReg::max_stable

max stable number (=0 if not maxstable)

int VlMserExtrReg::parent

index of the parent region

vl_uint VlMserExtrReg::shortcut

shortcut used when building a tree

vl_mser_pix VlMserExtrReg::value

value of pivot pixel

float VlMserExtrReg::variation

rel. area variation

The documentation for this struct was generated from the following file:
 
vlfeat-0.9.14/doc/api/struct__VlMserFilt.html000066400000000000000000000540631167540017400211010ustar00rootroot00000000000000 VLFeat - Documentation - C API
Documentation - C API
Data Fields
VlMserFilt Struct Reference

MSER filter. More...

#include <mser.h>

Data Fields

vl_uint * perm
vl_uint * joins
int njoins
Image data and meta data @internal
int ndims
int * dims
int nel
int * subs
int * dsubs
int * strides
Regions
VlMserReg * r
VlMserExtrReg * er
vl_uint * mer
int ner
int nmer
int rer
int rmer
Ellipsoids fitting
float * acc
float * ell
int rell
int nell
int dof
Configuration
vl_bool verbose
int delta
double max_area
double min_area
double max_variation
double min_diversity

Detailed Description

MSER Filter.

See also:
Maximally Stable Extremal Regions

The MSER filter computes the Maximally Stable Extremal Regions of an image.

This structure is opaque.

See also:
Maximally Stable Extremal Regions

Field Documentation

float* VlMserFilt::acc

moment accumulator.

int VlMserFilt::delta

delta filter parameter

int* VlMserFilt::dims

dimensions

int VlMserFilt::dof

number of dof of ellipsoids.

int* VlMserFilt::dsubs

another subscript

float* VlMserFilt::ell

ellipsoids list.

VlMserExtrReg* VlMserFilt::er

extremal tree

vl_uint* VlMserFilt::joins

sequence of join ops

double VlMserFilt::max_area

badness test parameter

double VlMserFilt::max_variation

badness test parameter

vl_uint* VlMserFilt::mer

maximally stable extremal regions

double VlMserFilt::min_area

badness test parameter

double VlMserFilt::min_diversity

minimum diversity

int VlMserFilt::ndims

number of dimensions

int VlMserFilt::nel

number of image elements (pixels)

int VlMserFilt::nell

number of ellipsoids extracted

int VlMserFilt::ner

number of extremal regions

int VlMserFilt::njoins

number of join ops

int VlMserFilt::nmer

number of maximally stable extr. reg.

vl_uint* VlMserFilt::perm

pixel ordering

VlMserReg* VlMserFilt::r

basic regions

int VlMserFilt::rell

size of ell buffer

int VlMserFilt::rer

size of er buffer

int VlMserFilt::rmer

size of mer buffer

int* VlMserFilt::strides

strides to move in image data

int* VlMserFilt::subs

N-dimensional subscript

vl_bool VlMserFilt::verbose

be verbose

The documentation for this struct was generated from the following file:
 
vlfeat-0.9.14/doc/api/struct__VlMserReg.html000066400000000000000000000207001167540017400207070ustar00rootroot00000000000000 VLFeat - Documentation - C API
Documentation - C API
Data Fields
VlMserReg Struct Reference

MSER: basic region (declaration) More...

#include <mser.h>

Data Fields

vl_uint parent
vl_uint shortcut
vl_uint height
vl_uint area

Detailed Description

MSER: basic region.

Extremal regions and maximally stable extremal regions are instances of image regions.

There is an image region for each pixel of the image. Each region is represented by an instance of this structure. Regions are stored into an array in pixel order.

Regions are arranged into a forest. VlMserReg::parent points to the parent node, or to the node itself if the node is a root. VlMserReg::parent is the index of the node in the node array (which therefore is also the index of the corresponding pixel). VlMserReg::height is the distance of the fartest leaf. If the node itself is a leaf, then VlMserReg::height is zero.

VlMserReg::area is the area of the image region corresponding to this node.

VlMserReg::region is the extremal region identifier. Not all regions are extremal regions however; if the region is NOT extremal, this field is set to ....

Field Documentation

vl_uint VlMserReg::area

area of the region.

vl_uint VlMserReg::height

region height in the forest.

vl_uint VlMserReg::parent

points to the parent region.

vl_uint VlMserReg::shortcut

points to a region closer to a root.

The documentation for this struct was generated from the following file:
 
vlfeat-0.9.14/doc/api/struct__VlMserStats.html000066400000000000000000000207221167540017400212740ustar00rootroot00000000000000 VLFeat - Documentation - C API
Documentation - C API
Data Fields
VlMserStats Struct Reference

MSER filter statistics definition. More...

#include <mser.h>

Data Fields

int num_extremal
int num_unstable
int num_abs_unstable
int num_too_big
int num_too_small
int num_duplicates

Detailed Description

MSER filter statistics.

Field Documentation

int VlMserStats::num_abs_unstable

number of regions that failed the absolute stability test

int VlMserStats::num_duplicates

number of regions that failed the duplicate test

int VlMserStats::num_extremal

number of extremal regions

int VlMserStats::num_too_big

number of regions that failed the maximum size test

int VlMserStats::num_too_small

number of regions that failed the minimum size test

int VlMserStats::num_unstable

number of unstable extremal regions

The documentation for this struct was generated from the following file:
 
vlfeat-0.9.14/doc/api/struct__vlmxOption.html000066400000000000000000000145321167540017400212260ustar00rootroot00000000000000 VLFeat - Documentation - C API
Documentation - C API
Data Fields
vlmxOption Struct Reference

MEX option. More...

#include <mexutils.h>

Data Fields

const char * name
int has_arg
int val

Detailed Description

MEX option type.

Field Documentation

int vlmxOption::has_arg

has argument?

const char* vlmxOption::name

option name

int vlmxOption::val

value to return

The documentation for this struct was generated from the following file:
 
vlfeat-0.9.14/doc/api/structoption.html000066400000000000000000000164111167540017400200570ustar00rootroot00000000000000 VLFeat - Documentation - C API
Documentation - C API
Data Fields
option Struct Reference

getopt_long option

#include <getopt_long.h>

Data Fields

const char * name
int has_arg
int * flag
int val

Field Documentation

int* option::flag

pointer to a variable to set (if NULL, the value is returned instead)

int option::has_arg

flag indicating whether the option has no, required or optional argument

const char* option::name

option long name

int option::val

value to set or to return

The documentation for this struct was generated from the following file:
 
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Documentation - C API
vlfeat-0.9.14/doc/applications/000077500000000000000000000000001167540017400163265ustar00rootroot00000000000000vlfeat-0.9.14/doc/applications/apps.html000066400000000000000000000100421167540017400201540ustar00rootroot00000000000000 VLFeat - Applications
Applications

This page lists a number of sample VLFeat applications. Their code can be found in the VLROOT/apps/ subdirectory.

Caltech-101 classification

Caltech-101 Collage

This sample application uses VLFeat to train an test an image classifier on the Caltech-101 data. The classifier achieves 65% average accuracy by using a single feature and 15 training images per class. It uses:

  • PHOW features (dense multi-scale SIFT descriptors)
  • Elkan k-means for fast visual word dictionary construction
  • Spatial histograms as image descriptors
  • A homogeneous kernel map to transform a Chi2 support vector machine (SVM) into a linear one
  • An internal SVM (based on PEGASOS) for classification

The program is fully contained in a single MATLAB M-file, and can also be simply adapted to use your own data (change conf.calDir).

SIFT mosaic

SIFT mosaic

This sample application uses VLFeat to extract SIFT features form a pair of images and match them. It then filters the matches based on RANSAC and produces a mosaic. Read the code.

 
vlfeat-0.9.14/doc/applications/caltech-101-code.html000066400000000000000000000334151167540017400220340ustar00rootroot00000000000000 VLFeat - Applications - phow_caltech101.m
Applications - phow_caltech101.m 
function phow_caltech101
% PHOW_CALTECH101 Image classification in the Caltech-101 dataset
%   This program demonstrates how to use VLFeat to construct an image
%   classifier on the Caltech-101 data. The classifier uses PHOW
%   features (dense SIFT), spatial histograms of visual words, and a
%   Chi2 SVM. To speedup computation it uses VLFeat fast dense SIFT,
%   kd-trees, and homogeneous kernel map. The program also
%   demonstrates VLFeat PEGASOS SVM solver, although for this small
%   dataset other solvers such as LIBLINEAR can be more efficient.
%
%   By default 15 training images are used, which should result in
%   about 64% performance (a good performance considering that only a
%   single feature type is being used).
%
%   Call PHOW_CALTECH101 to train and test a classifier on a small
%   subset of the Caltech-101 data. Note that the program
%   automatically downloads a copy of the Caltech-101 data from the
%   Internet if it cannot find a local copy.
%
%   Edit the PHOW_CALTECH101 file to change the program configuration.
%
%   To run on the entire dataset change CONF.TINYPROBLEM to FALSE.
%
%   The Caltech-101 data is saved into CONF.CALDIR, which defaults to
%   'data/caltech-101'. Change this path to the desired location, for
%   instance to point to an existing copy of the Caltech-101 data.
%
%   The program can also be used to train a model on custom data by
%   pointing CONF.CALDIR to it. Just create a subdirectory for each
%   class and put the training images there. Make sure to adjust
%   CONF.NUMTRAIN accordingly.
%
%   Intermediate files are stored in the directory CONF.DATADIR. All
%   such files begin with the prefix CONF.PREFIX, which can be changed
%   to test different parameter settings without overriding previous
%   results.
%
%   The program saves the trained model in
%   <CONF.DATADIR>/<CONF.PREFIX>-model.mat. This model can be used to
%   test novel images independently of the Caltech data.
%
%     load('data/baseline-model.mat') ; # change to the model path
%     label = model.classify(model, im) ;
%

% AUTORIGHTS

conf.calDir = 'data/caltech-101' ;
conf.dataDir = 'data/' ;
conf.autoDownloadData = true ;
conf.numTrain = 15 ;
conf.numTest = 15 ;
conf.numClasses = 102 ;
conf.numWords = 600 ;
conf.numSpatialX = [2 4] ;
conf.numSpatialY = [2 4] ;
conf.quantizer = 'kdtree' ;
conf.svm.C = 10 ;
conf.svm.solver = 'pegasos' ;
conf.svm.biasMultiplier = 1 ;
conf.phowOpts = {'Step', 3} ;
conf.clobber = false ;
conf.tinyProblem = true ;
conf.prefix = 'baseline' ;
conf.randSeed = 1 ;

if conf.tinyProblem
  conf.prefix = 'tiny' ;
  conf.numClasses = 5 ;
  conf.numSpatialX = 2 ;
  conf.numSpatialY = 2 ;
  conf.numWords = 300 ;
  conf.phowOpts = {'Verbose', 2, 'Sizes', 7, 'Step', 5} ;
end

conf.vocabPath = fullfile(conf.dataDir, [conf.prefix '-vocab.mat']) ;
conf.histPath = fullfile(conf.dataDir, [conf.prefix '-hists.mat']) ;
conf.modelPath = fullfile(conf.dataDir, [conf.prefix '-model.mat']) ;
conf.resultPath = fullfile(conf.dataDir, [conf.prefix '-result']) ;

randn('state',conf.randSeed) ;
rand('state',conf.randSeed) ;
vl_twister('state',conf.randSeed) ;

% --------------------------------------------------------------------
%                                            Download Caltech-101 data
% --------------------------------------------------------------------

if ~exist(conf.calDir, 'dir') || ...
   (~exist(fullfile(conf.calDir, 'airplanes'),'dir') && ...
    ~exist(fullfile(conf.calDir, '101_ObjectCategories', 'airplanes')))
  if ~conf.autoDownloadData
    error(...
      ['Caltech-101 data not found. ' ...
       'Set conf.autoDownloadData=true to download the required data.']) ;
  end
  vl_xmkdir(conf.calDir) ;
  calUrl = ['http://www.vision.caltech.edu/Image_Datasets/' ...
    'Caltech101/101_ObjectCategories.tar.gz'] ;
  fprintf('Downloading Caltech-101 data to ''%s''. This will take a while.', conf.calDir) ;
  untar(calUrl, conf.calDir) ;
end

if ~exist(fullfile(conf.calDir, 'airplanes'),'dir')
  conf.calDir = fullfile(conf.calDir, '101_ObjectCategories') ;
end

% --------------------------------------------------------------------
%                                                           Setup data
% --------------------------------------------------------------------
classes = dir(conf.calDir) ;
classes = classes([classes.isdir]) ;
classes = {classes(3:conf.numClasses+2).name} ;

images = {} ;
imageClass = {} ;
for ci = 1:length(classes)
  ims = dir(fullfile(conf.calDir, classes{ci}, '*.jpg'))' ;
  ims = vl_colsubset(ims, conf.numTrain + conf.numTest) ;
  ims = cellfun(@(x)fullfile(classes{ci},x),{ims.name},'UniformOutput',false) ;
  images = {images{:}, ims{:}} ;
  imageClass{end+1} = ci * ones(1,length(ims)) ;
end
selTrain = find(mod(0:length(images)-1, conf.numTrain+conf.numTest) < conf.numTrain) ;
selTest = setdiff(1:length(images), selTrain) ;
imageClass = cat(2, imageClass{:}) ;

model.classes = classes ;
model.phowOpts = conf.phowOpts ;
model.numSpatialX = conf.numSpatialX ;
model.numSpatialY = conf.numSpatialY ;
model.quantizer = conf.quantizer ;
model.vocab = [] ;
model.w = [] ;
model.b = [] ;
model.classify = @classify ;

% --------------------------------------------------------------------
%                                                     Train vocabulary
% --------------------------------------------------------------------

if ~exist(conf.vocabPath) || conf.clobber

  % Get some PHOW descriptors to train the dictionary
  selTrainFeats = vl_colsubset(selTrain, 30) ;
  descrs = {} ;
  %for ii = 1:length(selTrainFeats)
  parfor ii = 1:length(selTrainFeats)
    im = imread(fullfile(conf.calDir, images{selTrainFeats(ii)})) ;
    im = standarizeImage(im) ;
    [drop, descrs{ii}] = vl_phow(im, model.phowOpts{:}) ;
  end

  descrs = vl_colsubset(cat(2, descrs{:}), 10e4) ;
  descrs = single(descrs) ;

  % Quantize the descriptors to get the visual words
  vocab = vl_kmeans(descrs, conf.numWords, 'verbose', 'algorithm', 'elkan') ;
  save(conf.vocabPath, 'vocab') ;
else
  load(conf.vocabPath) ;
end

model.vocab = vocab ;

if strcmp(model.quantizer, 'kdtree')
  model.kdtree = vl_kdtreebuild(vocab) ;
end

% --------------------------------------------------------------------
%                                           Compute spatial histograms
% --------------------------------------------------------------------

if ~exist(conf.histPath) || conf.clobber
  hists = {} ;
  parfor ii = 1:length(images)
  % for ii = 1:length(images)
    fprintf('Processing %s (%.2f %%)\n', images{ii}, 100 * ii / length(images)) ;
    im = imread(fullfile(conf.calDir, images{ii})) ;
    hists{ii} = getImageDescriptor(model, im);
  end

  hists = cat(2, hists{:}) ;
  save(conf.histPath, 'hists') ;
else
  load(conf.histPath) ;
end

% --------------------------------------------------------------------
%                                                  Compute feature map
% --------------------------------------------------------------------

psix = vl_homkermap(hists, 1, 'kchi2', 'gamma', .5) ;

% --------------------------------------------------------------------
%                                                            Train SVM
% --------------------------------------------------------------------

if ~exist(conf.modelPath) || conf.clobber
  switch conf.svm.solver
    case 'pegasos'
      lambda = 1 / (conf.svm.C *  length(selTrain)) ;
      w = [] ;
      % for ci = 1:length(classes)
      parfor ci = 1:length(classes)
        perm = randperm(length(selTrain)) ;
        fprintf('Training model for class %s\n', classes{ci}) ;
        y = 2 * (imageClass(selTrain) == ci) - 1 ;
        w(:,ci) = vl_pegasos(psix(:,selTrain(perm)), ...
                             int8(y(perm)), lambda, ...
                             'NumIterations', 50/lambda, ...
                             'BiasMultiplier', conf.svm.biasMultiplier) ;
      end
    case 'liblinear'
      svm = train(imageClass(selTrain)', ...
                  sparse(double(psix(:,selTrain))),  ...
                  sprintf(' -s 3 -B %f -c %f', ...
                          conf.svm.biasMultiplier, conf.svm.C), ...
                  'col') ;
      w = svm.w' ;
  end

  model.b = conf.svm.biasMultiplier * w(end, :) ;
  model.w = w(1:end-1, :) ;

  save(conf.modelPath, 'model') ;
else
  load(conf.modelPath) ;
end

% --------------------------------------------------------------------
%                                                Test SVM and evaluate
% --------------------------------------------------------------------

% Estimate the class of the test images
scores = model.w' * psix + model.b' * ones(1,size(psix,2)) ;
[drop, imageEstClass] = max(scores, [], 1) ;

% Compute the confusion matrix
idx = sub2ind([length(classes), length(classes)], ...
              imageClass(selTest), imageEstClass(selTest)) ;
confus = zeros(length(classes)) ;
confus = vl_binsum(confus, ones(size(idx)), idx) ;

% Plots
figure(1) ; clf;
subplot(1,2,1) ;
imagesc(scores(:,[selTrain selTest])) ; title('Scores') ;
set(gca, 'ytick', 1:length(classes), 'yticklabel', classes) ;
subplot(1,2,2) ;
imagesc(confus) ;
title(sprintf('Confusion matrix (%.2f %% accuracy)', ...
              100 * mean(diag(confus)/conf.numTest) )) ;
print('-depsc2', [conf.resultPath '.ps']) ;
save([conf.resultPath '.mat'], 'confus', 'conf') ;

% -------------------------------------------------------------------------
function im = standarizeImage(im)
% -------------------------------------------------------------------------

im = im2single(im) ;
if size(im,1) > 480, im = imresize(im, [480 NaN]) ; end

% -------------------------------------------------------------------------
function hist = getImageDescriptor(model, im)
% -------------------------------------------------------------------------

im = standarizeImage(im) ;
width = size(im,2) ;
height = size(im,1) ;
numWords = size(model.vocab, 2) ;

% get PHOW features
[frames, descrs] = vl_phow(im, model.phowOpts{:}) ;

% quantize appearance
switch model.quantizer
  case 'vq'
    [drop, binsa] = min(vl_alldist(model.vocab, single(descrs)), [], 1) ;
  case 'kdtree'
    binsa = double(vl_kdtreequery(model.kdtree, model.vocab, ...
                                  single(descrs), ...
                                  'MaxComparisons', 15)) ;
end

for i = 1:length(model.numSpatialX)
  binsx = vl_binsearch(linspace(1,width,model.numSpatialX(i)+1), frames(1,:)) ;
  binsy = vl_binsearch(linspace(1,height,model.numSpatialY(i)+1), frames(2,:)) ;

  % combined quantization
  bins = sub2ind([model.numSpatialY(i), model.numSpatialX(i), numWords], ...
                 binsy,binsx,binsa) ;
  hist = zeros(model.numSpatialY(i) * model.numSpatialX(i) * numWords, 1) ;
  hist = vl_binsum(hist, ones(size(bins)), bins) ;
  hists{i} = single(hist / sum(hist)) ;
end
hist = cat(1,hists{:}) ;
hist = hist / sum(hist) ;

% -------------------------------------------------------------------------
function [className, score] = classify(model, im)
% -------------------------------------------------------------------------

hist = getImageDescriptor(model, im) ;
psix = vl_homkermap(hist, 1, .7, 'kchi2') ;
scores = model.w' * psix + model.b' ;
[score, best] = max(scores) ;
className = model.classes{best} ;
 
vlfeat-0.9.14/doc/applications/sift-mosaic-code.html000066400000000000000000000163741167540017400223550ustar00rootroot00000000000000 VLFeat - Applications - sift_mosaic.m
Applications - sift_mosaic.m 
function mosaic = sift_mosaic(im1, im2)
% SIFT_MOSAIC Demonstrates matching two images using SIFT and RANSAC
%
%   SIFT_MOSAIC demonstrates matching two images based on SIFT
%   features and RANSAC and computing their mosaic.
%
%   SIFT_MOSAIC by itself runs the algorithm on two standard test
%   images. Use SIFT_MOSAIC(IM1,IM2) to compute the mosaic of two
%   custom images IM1 and IM2.

% AUTORIGHTS

if nargin == 0
  im1 = imread(fullfile(vl_root, 'data', 'river1.jpg')) ;
  im2 = imread(fullfile(vl_root, 'data', 'river2.jpg')) ;
end

% make single
im1 = im2single(im1) ;
im2 = im2single(im2) ;

% make grayscale
if size(im1,3) > 1, im1g = rgb2gray(im1) ; else im1g = im1 ; end
if size(im2,3) > 1, im2g = rgb2gray(im2) ; else im2g = im2 ; end

% --------------------------------------------------------------------
%                                                         SIFT matches
% --------------------------------------------------------------------

[f1,d1] = vl_sift(im1g) ;
[f2,d2] = vl_sift(im2g) ;

[matches, scores] = vl_ubcmatch(d1,d2) ;

numMatches = size(matches,2) ;

X1 = f1(1:2,matches(1,:)) ; X1(3,:) = 1 ;
X2 = f2(1:2,matches(2,:)) ; X2(3,:) = 1 ;

% --------------------------------------------------------------------
%                                         RANSAC with homography model
% --------------------------------------------------------------------

clear H score ok ;
for t = 1:100
  % estimate homograpyh
  subset = vl_colsubset(1:numMatches, 4) ;
  A = [] ;
  for i = subset
    A = cat(1, A, kron(X1(:,i)', vl_hat(X2(:,i)))) ;
  end
  [U,S,V] = svd(A) ;
  H{t} = reshape(V(:,9),3,3) ;

  % score homography
  X2_ = H{t} * X1 ;
  du = X2_(1,:)./X2_(3,:) - X2(1,:)./X2(3,:) ;
  dv = X2_(2,:)./X2_(3,:) - X2(2,:)./X2(3,:) ;
  ok{t} = (du.*du + dv.*dv) < 6*6 ;
  score(t) = sum(ok{t}) ;
end

[score, best] = max(score) ;
H = H{best} ;
ok = ok{best} ;

% --------------------------------------------------------------------
%                                                  Optional refinement
% --------------------------------------------------------------------

function err = residual(H)
 u = H(1) * X1(1,ok) + H(4) * X1(2,ok) + H(7) ;
 v = H(2) * X1(1,ok) + H(5) * X1(2,ok) + H(8) ;
 d = H(3) * X1(1,ok) + H(6) * X1(2,ok) + 1 ;
 du = X2(1,ok) - u ./ d ;
 dv = X2(2,ok) - v ./ d ;
 err = sum(du.*du + dv.*dv) ;
end

if exist('fminsearch') == 2
  H = H / H(3,3) ;
  opts = optimset('Display', 'none', 'TolFun', 1e-8, 'TolX', 1e-8) ;
  H(1:8) = fminsearch(@residual, H(1:8)', opts) ;
else
  warning('Refinement disabled as fminsearch was not found.') ;
end

% --------------------------------------------------------------------
%                                                         Show matches
% --------------------------------------------------------------------

dh1 = max(size(im2,1)-size(im1,1),0) ;
dh2 = max(size(im1,1)-size(im2,1),0) ;

figure(1) ; clf ;
subplot(2,1,1) ;
imagesc([padarray(im1,dh1,'post') padarray(im2,dh2,'post')]) ;
o = size(im1,2) ;
line([f1(1,matches(1,:));f2(1,matches(2,:))+o], ...
     [f1(2,matches(1,:));f2(2,matches(2,:))]) ;
title(sprintf('%d tentative matches', numMatches)) ;
axis image off ;

subplot(2,1,2) ;
imagesc([padarray(im1,dh1,'post') padarray(im2,dh2,'post')]) ;
o = size(im1,2) ;
line([f1(1,matches(1,ok));f2(1,matches(2,ok))+o], ...
     [f1(2,matches(1,ok));f2(2,matches(2,ok))]) ;
title(sprintf('%d (%.2f%%) inliner matches out of %d', ...
              sum(ok), ...
              100*sum(ok)/numMatches, ...
              numMatches)) ;
axis image off ;

drawnow ;

% --------------------------------------------------------------------
%                                                               Mosaic
% --------------------------------------------------------------------

box2 = [1  size(im2,2) size(im2,2)  1 ;
        1  1           size(im2,1)  size(im2,1) ;
        1  1           1            1 ] ;
box2_ = inv(H) * box2 ;
box2_(1,:) = box2_(1,:) ./ box2_(3,:) ;
box2_(2,:) = box2_(2,:) ./ box2_(3,:) ;
ur = min([1 box2_(1,:)]):max([size(im1,2) box2_(1,:)]) ;
vr = min([1 box2_(2,:)]):max([size(im1,1) box2_(2,:)]) ;

[u,v] = meshgrid(ur,vr) ;
im1_ = vl_imwbackward(im2double(im1),u,v) ;

z_ = H(3,1) * u + H(3,2) * v + H(3,3) ;
u_ = (H(1,1) * u + H(1,2) * v + H(1,3)) ./ z_ ;
v_ = (H(2,1) * u + H(2,2) * v + H(2,3)) ./ z_ ;
im2_ = vl_imwbackward(im2double(im2),u_,v_) ;

mass = ~isnan(im1_) + ~isnan(im2_) ;
im1_(isnan(im1_)) = 0 ;
im2_(isnan(im2_)) = 0 ;
mosaic = (im1_ + im2_) ./ mass ;

figure(2) ; clf ;
imagesc(mosaic) ; axis image off ;
title('Mosaic') ;

if nargout == 0, clear mosaic ; end

end
 
vlfeat-0.9.14/doc/compiling.html000066400000000000000000000202621167540017400165110ustar00rootroot00000000000000 VLFeat - Download - Compiling
Download - Compiling

These instructions explain how to compile VLFeat on your own. This can be useful to customize or contribute to VLFeat, but it is otherwise not necessary (use the binary package instead).

Compiling on Unix

Compiling for UNIX-like platforms (e.g. Mac OS X, Linux) uses the standard GNU toolchain. It is usually sufficient to change to VLFeat root directory, denoted VLFEATROOT in the following, and type make:

$ cd VLFEATROOT
$ make

The make script attempts to automatically detect the host architecture and configure itself accordingly. If the architecture is not detected correctly, it can be specified manually. For instance

$ make ARCH=glnx86

compiles for GNU/Linux 32-bit. make help can be used to obtain a list of other useful options.

MATLAB support. In order for MATLAB support to be compiled, the MATLAB mex script must be in the current path. If it is not, its location must be passed to make as follows. First, determine MATLAB's root directory by running a MATLAB session and issuing the matlabroot command. Let MATLABROOT denote the returned path (e.g. /Applications/MATLAB_R2009b.app/). The mex script is usually located in MALTABROOT/bin/mex. Then run the compilation with the command

$ make MEX=MATLABROOT/bin/mex

VLFeat must be compiled for the architecture used by MATLAB (run MATLAB's computer command to obtain this information). On Mac OS X it is often necessary to turn on 64-bit support explicitly by setting ARCH=maci64 as both the 32 and 64 bit versions are plausible targets on 64-bit machines.

Mac OS X 10.6 (Snow Leopard), 64-bit, and MATLAB 2009b. MATLAB 2009b for Snow Leopard has added 64 bit mex support and a corresponding extension mexmaci64. VLFeat now supports compiling for this platform, but the architecture must be specified manually when compiling, either like:

$ make ARCH=maci64

or

$ make ARCH=maci

Unfortunately, MATLAB 2009b's mex script has a bug that must be manually fixed in order for this procedure to work correctly. It is recommend to make backup of the mex script before attempting this. Th fix the bug, edit the MATLABROOT/bin/mex script by cutting the line:

get_arch    # Call $MATLAB/bin/util/arch.sh

and pasting it after the processing of options:

...
shift
done

#PASTE get_arch call here
get_arch    # Call $MATLAB/bin/util/arch.sh

if [ $v7_compat -eq 1 ] ; then
fc_flags="$fc_flags -DMX_COMPAT_32"
...

Compiling on Windows

For Windows, the library bundles an NMake makefile (Makefile.mak). In order to use it, you must edit Makefile.mak to adjust the values of a number of configuration variables to match your setup. Once you have done that, start the Visual Studio Command Prompt and type

$ nmake /f Makefile.mak # for the Windows 32 target
$ nmake /f Makefile.mak ARCH=win64 # for the Windows 64 target

For Windows platform, it is also possible to compile just the MATLAB MEX files from within MATLAB (using the vl_compile command). This is meant to help less experienced users that may need to recompile the mex file due to binary incompatibilities with older MATLAB versions.

MATLAB 7.0 and earlier require recompiling the MEX files by the provided vl_compile command.

Windows troubleshooting

syntax error: '=' unexpected:

Use nmake /f Makefile.mak. Without /f, nmake will default to the wrong makefile.

'long' followed by 'long' is illegal:

This error is usually caused by attempting to compile outside of the Visual Studio Command Prompt.

 
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