Subscripted assignment dimension mismatch error in harris laplace

See more:

MatLab

computer-vision

Hi All,

I am working on a project and need to extract corners from the IRIS image. I am not very experienced in MATLAB so found a related code on internet but that is not working properly. Can anyone help me getting this code correct? Please point out the different errors in it. I will be grateful. The error is:

Subscripted assignment dimension mismatch. Error in harrislaplace (line 48)
      harris_pts(l,c) = [l,c,repmat(i,[n,1])];

function points = harrislaplace(img)
    
    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(img_height,img_width);
    for i=1:sigma_nb

        % scale (standard deviation)
        s_I = sigma_array(i);   % intégration scale
        s_D = 0.7*s_I;          % derivative scale %0.7

        [Ix, Iy] = imgradientxy(img);
         
        
        % 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
      
        k = 0.04; 
        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(l,c) = [l,c,repmat(i,[n,1])];
    end


    % PART 2 : LAPLACE
    
    % compute scale-normalized laplacian operator
    laplace_snlo = zeros(img_height,img_width,sigma_nb); % sigma_nb matrices of size img_height by img_width
    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

What I have tried:

I tried checking output of each executed lines. Size of l and c is very small as compared to the entire image which is not supposed to be because both l and c shows the x, y positions of the corner pixel in the image. One function is called by above code which finds local maxima is mentioned below:

function [row,col,max_local] = findLocalMaximum(val,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