Fixed mapping executing the Harris corner detector
X = IM_HARRIS(A,N,SIGMA)
X = A*IM_HARRIS(,N,SIGMA)
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  software to find the corner points according to Harris . 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  and .
 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: .  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
|This file has been automatically generated. If badly readable, use the help-command in Matlab.|