文件列表:

dependencies\CCRColour.m (3772, 2011-10-26)
dependencies\ClusterHistogram.m (597, 2011-10-26)
dependencies\codeLBP3x3.m (1217, 2011-10-26)
dependencies\Colormaps\INDEXED_SPACE_EUC_MAP_HSV_27.mat (205862, 2008-04-12)
dependencies\Colormaps\INDEXED_SPACE_EUC_MAP_HSV_64.mat (223296, 2008-04-12)
dependencies\Colormaps\INDEXED_SPACE_EUC_MAP_HSV_8.mat (200519, 2008-04-12)
dependencies\Colormaps\INDEXED_SPACE_EUC_MAP_Lab_27.mat (422141, 2008-05-08)
dependencies\Colormaps\INDEXED_SPACE_EUC_MAP_Lab_64.mat (539592, 2008-05-08)
dependencies\Colormaps\INDEXED_SPACE_EUC_MAP_Lab_8.mat (306541, 2008-05-08)
dependencies\Colormaps\INDEXED_SPACE_EUC_MAP_RGB_27.mat (136766, 2008-04-11)
dependencies\Colormaps\INDEXED_SPACE_EUC_MAP_RGB_64.mat (143505, 2008-04-12)
dependencies\Colormaps\INDEXED_SPACE_EUC_MAP_RGB_8.mat (124245, 2008-04-11)
dependencies\Colormaps\MAP_HSV_27.mat (311, 2008-04-11)
dependencies\Colormaps\MAP_HSV_64.mat (443, 2008-04-11)
dependencies\Colormaps\MAP_HSV_8.mat (223, 2008-04-11)
dependencies\Colormaps\MAP_Lab_27.mat (473, 2008-05-08)
dependencies\Colormaps\MAP_Lab_64.mat (752, 2008-05-08)
dependencies\Colormaps\MAP_Lab_8.mat (280, 2008-05-08)
dependencies\Colormaps\MAP_RGB_27.mat (218, 2008-04-11)
dependencies\Colormaps\MAP_RGB_64.mat (221, 2008-04-11)
dependencies\Colormaps\MAP_RGB_8.mat (199, 2008-04-11)
dependencies\Colormaps\old\INDEXED_SPACE_EUC_MAP_HSV_27.mat (205450, 2008-04-05)
dependencies\Colormaps\old\INDEXED_SPACE_EUC_MAP_HSV_64.mat (213840, 2008-04-05)
dependencies\Colormaps\old\INDEXED_SPACE_EUC_MAP_HSV_8.mat (200370, 2008-04-05)
dependencies\Colormaps\old\INDEXED_SPACE_EUC_MAP_RGB_27.mat (138706, 2008-04-04)
dependencies\Colormaps\old\INDEXED_SPACE_EUC_MAP_RGB_64.mat (150608, 2008-04-05)
dependencies\Colormaps\old\INDEXED_SPACE_EUC_MAP_RGB_8.mat (124245, 2008-04-04)
dependencies\Colormaps\old\MAP_HSV_27.mat (280, 2008-01-10)
dependencies\Colormaps\old\MAP_HSV_64.mat (406, 2008-01-10)
dependencies\Colormaps\old\MAP_HSV_8.mat (220, 2008-01-10)
dependencies\Colormaps\old\MAP_Lab_27.mat (475, 2008-01-10)
dependencies\Colormaps\old\MAP_Lab_64.mat (768, 2008-01-10)
dependencies\Colormaps\old\MAP_Lab_8.mat (277, 2008-01-10)
dependencies\Colormaps\old\MAP_Luv_27.mat (468, 2008-02-05)
dependencies\Colormaps\old\MAP_Luv_64.mat (743, 2008-02-05)
dependencies\Colormaps\old\MAP_Luv_8.mat (283, 2008-02-05)
dependencies\Colormaps\old\MAP_RGB_27.mat (210, 2008-01-11)
dependencies\Colormaps\old\MAP_RGB_64.mat (224, 2008-01-11)
dependencies\Colormaps\old\MAP_RGB_8.mat (192, 2008-01-11)
dependencies\Colormaps\old\MAP_sRGB_27_.mat (274, 2008-01-11)
... ...

Gabor Filtering Toolbox v 0.1 for Matlab ---------------------------------------- This is the initial released version, some bugs will be likely found. It has been tested mainly with Matlab R14. Short introduction to usage of Gabor toolbox -------------------------------------------- First, an image is loaded and converted to double. The image has likely three color channels even if it is a gray-scale image, so only one of them must be selected. While not strictly necessary, the image can be also scaled to [0,1] instead of normal [0,255]. image=imread('someimage.jpg'); % load image image=image(:,:,1); % select only one color channel image=double(image)./256; % convert to double and scale to [0,1] Then, a filterbank can be created. bank=sg_createfilterbank(size(image), 0.2 , 5, 4,'verbose',1); creates a filterbank with the size of the image, the frequency of the highest frequency filter is 0.2, 5 filters at different frequencies and 4 orientations are created. The created filter bank will be displayed. Only half of the filterbank is created, because responses for the second half of the filter bank are complex conjugates of the responses from the first half. The filterbank can be used to filter images. r=sg_filterwithbank(image,bank); The responses will be returned in a special structure. The structure can be converted to a 3-d matrix by using m=sg_resp2samplematrix(r); Now, m will be a 512x512x20 (or whatever the image resolution was x20) matrix, since there are 5*4=20 Gabor filters. If you do not have a good idea what to do with the responses, you can for example view a spooky image by summing all the responses: imagesc(abs(sum(m,3))); colormap(gray); For object detection and localization functionality other functions are also available. Sample matrix can be normalized for illumination invariance: m_norm=sg_normalizesamplematrix(m) For scale invariance, extra frequencies can be first included in the filter bank: bank=sg_createfilterbank(size(image), 0.2 , 5, 4,'extra_freq',1); r=sg_filterwithbank(image,bank); m=sg_resp2samplematrix(r); Then, different scales of features can be selected from sample matrix: m2=sg_scalesamples(m,0,5,4) will select the 5 highest frequencies and m2=sg_scalesamples(m,1,5,4) the lower available 5 frequencies. Note that if normalization is required, it must be done after this sg_scalesamples step. For rotation invariance, the sample matrix can be rotated: m2=sg_rotatesamples(m,1,4) rotates the responses by 1 orientation.

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