Compute oriented gradient histograms. For each binSize x binSize region in an image I, computes a histogram of gradients, with each gradient quantized by its angle and weighed by its magnitude. If I has dimensions [hxw], the size of the computed feature vector H is floor([h/binSize w/binSize nOrients]). This function implements the gradient histogram features described in: P. Dollár, Z. Tu, P. Perona and S. Belongie "Integral Channel Features", BMVC 2009. These features in turn generalize the HOG features introduced in: N. Dalal and B. Triggs, "Histograms of Oriented Gradients for Human Detection," CVPR 2005. Setting the parameters appropriately gives almost identical features to the original HOG features, also see hog.m for more details. The input to the function are the gradient magnitude M and orientation O at each image location. See gradientMag.m for computing M and O from I. The first step in computing the gradient histogram is simply quantizing the magnitude M into nOrients [hxw] orientation channels according to the gradient orientation. The magnitude at each location is placed into the two nearest orientation bins using linear interpolation. Next, spatial binning is performed by summing the pixels in each binSize x binSize region of each [hxw] orientation channel. If "softBin" is true each pixel can contribute to multiple spatial bins (using bilinear interpolation), otherwise each pixel contributes to a single spatial bin. The result of these steps is a floor([h/binSize w/binSize nOrients]) feature map representing the gradient histograms in each image region. The above can effectively be used directly. Alternatively, if "useHog" is true, an additional 4-way normalization is performed on each histogram followed by clipping, resulting in nOrient*4 bins at each location. The result closely resembles the HOG features from Dalal's CVPR05 paper, for more details see hog.m. Parameter settings of particular interest: binSize=1: simply quantize the gradient magnitude into nOrients channels softBin=1, useHog=1, clip=.2: original HOG features softBin=0, useHog=0: channels used in Dollar's BMVC09 paper This code requires SSE2 to compile and run (most modern Intel and AMD processors support SSE2). Please see: http://en.wikipedia.org/wiki/SSE2. USAGE H = gradientHist( M,O,[binSize],[nOrients],[softBin],[useHog],[clip] ) INPUTS M - [hxw] gradient magnitude at each location (see gradientMag.m) O - [hxw] gradient orientation in [0,pi) binSize - [8] spatial bin size nOrients - [9] number of orientation bins softBin - [true] if true use "soft" bilinear spatial binning useHog - [false] if true perform 4-way hog normalization/clipping clipHog - [.2] value at which to clip hog histogram bins OUTPUTS H - [w/binSize x h/binSize x nOrients] gradient histograms EXAMPLE I=rgbConvert(imread('peppers.png'),'gray'); [M,O]=gradientMag(I); H1=gradientHist(M,O,2,6,0); figure(1); montage2(H1); H2=gradientHist(M,O,2,6,1); figure(2); montage2(H2); See also gradientMag, gradient2, hog Piotr's Image&Video Toolbox Version 3.00 Copyright 2012 Piotr Dollar & Ron Appel. [pdollar-at-caltech.edu] Please email me if you find bugs, or have suggestions or questions! Licensed under the Simplified BSD License [see external/bsd.txt]