Extremely fast 2D image convolution with a max filter.
For each location computes J(y,x) = max(max(I(y-r:y+r,x-r:x+r))). The
filtering is constant time per-window, independent of r. First, the
filtering is separable, which brings the complexity down to O(r) per
window from O(r*r). To bring the implemention down to constant time
(independent of r) we use the van Herk/Gil-Werman algorithm. Ignoring
boundaries, just 3 max operations are need per-window regardless of r.
http://www.leptonica.com/grayscale-morphology.html#FAST-IMPLEMENTATION
The output is exactly equivalent to the following Matlab operations:
I=padarray(I,[r r],'replicate','both'); [h,w,d]=size(I); J=I;
for z=1:d, for x=r+1:w-r, for y=r+1:h-r
J(y,x,z) = max(max(I(y-r:y+r,x-r:x+r,z))); end; end; end
J=J(r+1:h-r,r+1:w-r,:);
The computation, however, is an order of magnitude faster than the above.
USAGE
J = convMax( I, r, [nomex] )
INPUTS
I - [hxwxk] input k channel single image
r - integer filter radius or radii along y and x
nomex - [0] if true perform computation in matlab (for testing/timing)
OUTPUTS
J - [hxwxk] max image
EXAMPLE
I = single(imResample(imread('cameraman.tif'),[480 640]))/255;
r = 5; % set parameter as desired
tic, J1=convMax(I,r); toc % mex version (fast)
tic, J2=convMax(I,r,1); toc % matlab version (slow)
figure(1); im(J1); figure(2); im(abs(J2-J1));
See also conv2, convTri, convBox
Piotr's Computer Vision Matlab Toolbox Version 3.00
Copyright 2014 Piotr Dollar & Ron Appel. [pdollar-at-gmail.com]
Licensed under the Simplified BSD License [see external/bsd.txt]