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]