Compute gradient magnitude and orientation at each image location. If input image has k>1 channels and channel=0, keeps gradient with maximum magnitude (over all channels) at each location. Otherwise if channel is between 1 and k computes gradient for the given channel. If full==1 orientation is computed in [0,2*pi) else it is in [0,pi). If normRad>0, normalization is performed by first computing S, a smoothed version of the gradient magnitude, then setting: M = M./(S + normConst). S is computed by S = convTri( M, normRad ). 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 [M,O] = gradientMag( I, [channel], [normRad], [normConst], [full] ) INPUTS I - [hxwxk] input k channel single image channel - [0] if>0 color channel to use for gradient computation normRad - [0] normalization radius (no normalization if 0) normConst - [.005] normalization constant full - [0] if true compute angles in [0,2*pi) else in [0,pi) OUTPUTS M - [hxw] gradient magnitude at each location O - [hxw] approximate gradient orientation modulo PI EXAMPLE I=rgbConvert(imread('peppers.png'),'gray'); [Gx,Gy]=gradient2(I); M=sqrt(Gx.^2+Gy.^2); O=atan2(Gy,Gx); full=0; [M1,O1]=gradientMag(I,0,0,0,full); D=abs(M-M1); mean2(D), if(full), o=pi*2; else o=pi; end D=abs(O-O1); D(~M)=0; D(D>o*.99)=o-D(D>o*.99); mean2(abs(D)) See also gradient, gradient2, gradientHist, convTri 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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