Applies the meanShift algorithm to a joint spatial/range image. See "Mean Shift Analysis and Applications" by Comaniciu & Meer for info. Assumes X is an MxNxP array, where an X(i,j,:) represents the range data at locations (i,j). This function runs meanShift on each of the MxN data points. It takes advantage of the lattice structure of an image for efficiency (it only needs to calculate full distance between two points if they are near each other spatially). In the original formulation of the algorithm, after normalization of the data, the search window around each point x has radius 1 (ie corresponding to 1 std of the data). That is the search window only encloses 2*s+1 pixels, and of those, all which fall within 1 unit from x are used to calcluate the new mean. If softFlag==0 the original formulation is used. If softFlag==1, instead of using a fixed radius, each point p is used in the calulation of the mean with points close to x given significantly more weight. Specifically, each point p is given weight exp(-dist(x,p)). So instead of having a fixed cutoff at r, the cutoff is 'soft' (same idea as in softmax), and occurs at approximately r. The implementation remains efficient by actually using a hard cutoff at points further then 2r spatially from x. The resulting matrix M is of size MxNx(P+2). M(i,j,1) represents the convergent row location of X(i,j,:) - (which had initial row location i) and M(i,j,2) represents the final column location. M(i,j,p+2) represents the convergent value for X(i,j,p). The optionaly outputs Vr and Vc are 2D arrays where Vr(i,j)=M(i,j,1)-i and Vc(i,j)=M(i,j,2)-j. That is they represent the spatial offset between the original location of a point and its convergent location. Display using quiver(Vc,Vr,0). USAGE [M,Vr,Vc] = meanShiftIm( X,sigSpt,sigRng,[softFlag],[maxIter],[minDel] ) INPUTS X - MxNxP data array, P may be 1 sigSpt - integer specifying spatial standard deviation sigRng - value specifying the standard deviation of the range data softFlag - [0]- see above maxIter - [100] maximum number of iterations per data point minDel - [.001] minimum amount of spatial change defining convergence OUTPUTS M - array of convergent locations [see above] Vr - spatial motion in row direction Vc - spatial motion in col direction EXAMPLE I=double(imread('cameraman.tif'))/255; [M,Vr,Vc] = meanShiftIm( I,5,.2 ); figure(1); im(I); figure(2); im( M(:,:,3) ); % color image: I=double(imread('hestain.png'))/255; [M,Vr,Vc] = meanShiftIm( I,5,.2 ); figure(1); im(I); figure(2); im( M(:,:,3:end) ); See also MEANSHIFT, MEANSHIFTIMEXPLORE Piotr's Computer Vision Matlab Toolbox Version 2.0 Copyright 2014 Piotr Dollar. [pdollar-at-gmail.com] Licensed under the Simplified BSD License [see external/bsd.txt]

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