Harris算子进行角点检测算法
function points = kp_harris(im) % Extract keypoints using Harris algorithm (with an improvement % version) % 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, "d" , % Int. Journal of Computer Vision, 37(2), 151-172, 2000. % % EXAMPLE % ======= % points = kp_harris(im) % only luminance value %size(im) 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); % 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(:)); t = 0.6*max(max_local(:)); %door.jpg %t = 0.48*max(max_local(:)); %sunflower.jpg % find local maxima greater than threshold [r,c] = find(max_local>=t); % build interest points points = [r,c]; end |
function [row,col,max_local] = findLocalMaximum(val,radius) % Determine the local maximum of a given value % % % INPUT % ===== % val : the NxM matrix containing values % radius : the radius of the neighborhood % % OUTPUT % ====== % row : the row position of the local maxima % col : the column position of the local maxima % max_local : the NxM matrix containing values of val on unique local maximum % % EXAMPLE % ======= % [l,c,m] = findLocalMaximum(img,radius); % FIND UNIQUE LOCAL MAXIMA USING FILTERING (FAST) mask = fspecial( 'disk' ,radius)>0; nb = sum(mask(:)); highest = ordfilt2(val, nb, mask); second_highest = ordfilt2(val, nb-1, mask); index = highest==val & highest~=second_highest; max_local = zeros(size(val)); max_local(index) = val(index); [row,col] = find(index==1); end |
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