kp_harrislaplace.m

计算图像的描述子算法

function points = kp_harrislaplace(img)
    % Extract keypoints using Harris-Laplace algorithm
    %
    % Author :: Vincent Garcia
    % Date   :: 05/12/2007
    %
    % INPUT
    % =====
    % img    : the graylevel image
    %
    % OUTPUT
    % ======
    % points : the interest points extracted
    %
    % REFERENCES
    % ==========
    % K. Mikolajczyk and C. Schmid. Scale & affine invariant interest point detectors.
    % International Journal of Computer Vision, 2004
    %
    % EXAMPLE
    % =======
    % points = kp_harrislaplace(img)
    
    % IMAGE PARAMETERS
    img         = double(img(:,:,1));
    img_height  = size(img,1);
    img_width   = size(img,2);

    % SCALE PARAMETERS
    sigma_begin = 1.5;
    sigma_step  = 1.2;
    sigma_nb    = 13;
    sigma_array = (sigma_step.^(0:sigma_nb-1))*sigma_begin;


    % PART 1 : HARRIS
    harris_pts = zeros(0,3);
    for i=1:sigma_nb

        % scale (standard deviation)
        s_I = sigma_array(i);   % int間ration scale
        s_D = 0.7*s_I;          % derivative scale %0.7

        % derivative mask
        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(img, dx, 'same');
        Iy = conv2(img, dy, 'same');

        % auto-correlation matrix
        g   = fspecial('gaussian',max(1,fix(6*s_I+1)), s_I);
        Ix2 = conv2(Ix.^2, g,  'same');
        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 neighborgood
        [l,c,max_local] = findLocalMaximum(cim,3*s_I);%3*s_I

        % set threshold 1% of the maximum value
        t = 0.2*max(max_local(:));

        % find local maxima greater than threshold
        [l,c] = find(max_local>=t);

        % build interest points
        n = size(l,1);
        harris_pts(end+1:end+n,:) = [l,c,repmat(i,[n,1])];
    end


    % PART 2 : LAPLACE
    % compute scale-normalized laplacian operator
    laplace_snlo = zeros(img_height,img_width,sigma_nb);
    for i=1:sigma_nb
        s_L = sigma_array(i);   % scale
        laplace_snlo(:,:,i) = s_L*s_L*imfilter(img,fspecial('log', floor(6*s_L+1), s_L),'replicate');
    end
    % verify for each of the initial points whether the LoG attains a maximum at the scale of the point
    n   = size(harris_pts,1);
    cpt = 0;
    points = zeros(n,3);
    for i=1:n
        l = harris_pts(i,1);
        c = harris_pts(i,2);
        s = harris_pts(i,3);
        val = laplace_snlo(l,c,s);
        if s>1 && s<sigma_nb
            if val>laplace_snlo(l,c,s-1) && val>laplace_snlo(l,c,s+1)
                cpt = cpt+1;
                points(cpt,:) = harris_pts(i,:);
            end
        elseif s==1
            if val>laplace_snlo(l,c,2)
                cpt = cpt+1;
                points(cpt,:) = harris_pts(i,:);
            end
        elseif s==sigma_nb
            if val>laplace_snlo(l,c,s-1)
                cpt = cpt+1;
                points(cpt,:) = harris_pts(i,:);
            end
        end
    end
    points(cpt+1:end,:) = [];
    
    % SET SCALE TO 3*SIGMA FOR DISPLAY
    points(:,3) = 3*sigma_array(points(:,3));
end