PRTools Contents

PRTools User Guide



Fixed mapping executing the Harris corner detector

     X = A*IM_HARRIS([],N,SIGMA)

 A Datafile or dataset with images
 N Number of desired Harris points per image (default 100)
 SIGMA Smoothing size (default 3)

 X Dataset with a [N,3] array with for every image  x, y and strength per Harris point.


We use Kosevi's [1] software to find the corner points according to  Harris [2]. On top of the Kosevi Harris point detector we run

  • multi-feature images (e.g. color images) are averaged
  • only points that are maximum in a K x K window are selected.  If less points are found, K is iteratively reduced.  The initial value of K is about 4*SIGMA.  Although SIGMA can be interpreted as scaling parameter, it might be  better to appropriately subsample images instead of using a large SIGMA.

If you use this software for publications, please refer to [1] and [2].


[1] P. D. Kovesi, MATLAB and Octave Functions for Computer Vision and Image Processing, School of Computer Science & Software Engineering,  The University of Western Australia. Available from: . [2] C. Harris and M. Stephens, A combined corner and edge detector, Proc. 4th Alvey Vision Conf., 1988, pp. 147-151.


 a = kimia;                  % take simple shapesas example
 b = gendat(a,25)*im_gray;   % just 25 images at random
 c = data2im(b);             % convert dataset to images for display
 x = im_harris(b,15,1);      % compute maximum 15 Harris points at scale 1
 y = data2im(x);             % unpack dataset with results 
 for j=1:25                  % show results one by one
     figure(j); imagesc(c(:,:,1,j)); colormap gray; hold on

PRTools Contents

PRTools User Guide

This file has been automatically generated. If badly readable, use the help-command in Matlab.