A = [1 2 3 4 5;6 7 8 9 10;11 12 13 14 15];
B = [1 -1 1; 2 -2 2; 3 -3 3];
basic_convolution(A,B)

function [filtered] = basic_convolution(image,kernel) 
    dimensions = size(image); 
    dimensions2 = size(kernel); 
 
% define kernel center indices 
    kernelCenter_x = 2%cast(dimensions2(1)/2,"uint32"); 
    kernelCenter_y = 2%cast(dimensions2(2)/2,"uint32"); 
 
    image2 = zeros(dimensions(1),dimensions(2)); 
    for i = 1:dimensions(1)
        for j = 1:dimensions(2)
            for k = 1:dimensions2(1)
                for l = 1:dimensions2(2) 
% New changes are added below 
                    ii = i+(k-kernelCenter_x); 
                    jj = j+(l-kernelCenter_y); 
                    if (ii >= 1 && ii <= dimensions(1) && jj >= 1 && jj <= dimensions(2)) 
                        image2(i,j) = image2(i,j) + image(ii,jj)* kernel(k,l); 
                    end 
                end 
            end 
        end   
    filtered = image2; 
    end 
end