68 lines
1.9 KiB
Matlab
68 lines
1.9 KiB
Matlab
function points = kp_harris(im)
|
|
% Extract keypoints using Harris algorithm (with an improvement
|
|
% version)
|
|
%
|
|
% Author :: Vincent Garcia
|
|
% Date :: 05/12/2007
|
|
%
|
|
% INPUT
|
|
% =====
|
|
% im : the graylevel image
|
|
%
|
|
% OUTPUT
|
|
% ======
|
|
% points : the interest points extracted
|
|
%
|
|
% REFERENCES
|
|
% ==========
|
|
% C.G. Harris and M.J. Stephens. "A combined corner and edge detector",
|
|
% Proceedings Fourth Alvey Vision Conference, Manchester.
|
|
% pp 147-151, 1988.
|
|
%
|
|
% Alison Noble, "Descriptions of Image Surfaces", PhD thesis, Department
|
|
% of Engineering Science, Oxford University 1989, p45.
|
|
%
|
|
% C. Schmid, R. Mohrand and C. Bauckhage, "Evaluation of Interest Point Detectors",
|
|
% Int. Journal of Computer Vision, 37(2), 151-172, 2000.
|
|
%
|
|
% EXAMPLE
|
|
% =======
|
|
% points = kp_harris(im)
|
|
|
|
% only luminance value
|
|
im = double(im(:,:,1));
|
|
sigma = 1.5;
|
|
|
|
% derivative masks
|
|
s_D = 0.7*sigma;
|
|
x = -round(3*s_D):round(3*s_D);
|
|
dx = x .* exp(-x.*x/(2*s_D*s_D)) ./ (s_D*s_D*s_D*sqrt(2*pi));
|
|
dy = dx';
|
|
|
|
% image derivatives
|
|
Ix = conv2(im, dx, 'same');
|
|
Iy = conv2(im, dy, 'same');
|
|
|
|
% sum of the Auto-correlation matrix
|
|
s_I = sigma;
|
|
g = fspecial('gaussian',max(1,fix(6*s_I+1)), s_I);
|
|
Ix2 = conv2(Ix.^2, g, 'same'); % Smoothed squared image derivatives
|
|
Iy2 = conv2(Iy.^2, g, 'same');
|
|
Ixy = conv2(Ix.*Iy, g, 'same');
|
|
|
|
% interest point response
|
|
cim = (Ix2.*Iy2 - Ixy.^2)./(Ix2 + Iy2 + eps); % Alison Noble measure.
|
|
% k = 0.06; cim = (Ix2.*Iy2 - Ixy.^2) - k*(Ix2 + Iy2).^2; % Original Harris measure.
|
|
|
|
% find local maxima on 3x3 neighborgood
|
|
[r,c,max_local] = findLocalMaximum(cim,3*s_I);
|
|
|
|
% set threshold 1% of the maximum value
|
|
t = 0.01*max(max_local(:));
|
|
|
|
% find local maxima greater than threshold
|
|
[r,c] = find(max_local>=t);
|
|
|
|
% build interest points
|
|
points = [r,c];
|
|
end |