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 parameters appropriately gives almost identical features to the original HOG or updated FHOG features, see hog.m and fhog.m for 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 if softBin >= 0 or simply to the nearest orientation bin if softBin < 0. Next, spatial binning is performed by summing the pixels in each binSize x binSize region of each [hxw] orientation channel. If "softBin" is odd 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. Parameter settings of particular interest: binSize=1: simply quantize the gradient magnitude into nOrients channels softBin=1, useHog=1, clip=.2: original HOG features (see hog.m) softBin=-1; useHog=2, clip=.2: FHOG features (see fhog.m) 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,clipHog,full] ) INPUTS M - [hxw] gradient magnitude at each location (see gradientMag.m) O - [hxw] gradient orientation in range defined by param flag binSize - [8] spatial bin size nOrients - [9] number of orientation bins softBin - [1] set soft binning (odd: spatial=soft, >=0: orient=soft) useHog - [0] 1: compute HOG (see hog.m), 2: compute FHOG (see fhog.m) clipHog - [.2] value at which to clip hog histogram bins full - [false] if true expects angles in [0,2*pi) else in [0,pi) 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, fhog Piotr's Computer Vision Matlab Toolbox Version 3.23 Copyright 2014 Piotr Dollar & Ron Appel. [pdollar-at-gmail.com] Licensed under the Simplified BSD License [see external/bsd.txt]

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