% Chessboard
n = 8;
v = 1:n; % 1 =w; 0=black
c = zeros(n,n); for k=1:n; c(k,:) = mod(v+k-1,2); end; c
% mean filter
h = [1 1 1 1 1 1 1; 1 1 1 1 1 1 1; 1 1 1 1 1 1 1; 1 1 1 1 1 1 1; 1 1 1 1 1 1 1; 1 1 1 1 1 1 1; 1 1 1 1 1 1 1;]/49;
cf = imfilter(c,h) % built-in matlab function
% Now we design our function for filtering.
% zero-pad c to e: (n+2)*(n+2). Note: n1=n+3-1=n+2.
e = zeros(n+2,n+2);
e(2:n+1,2:n+1)=c; % original image
e % print e on screen
% Now perform correlation filtering (not convolution):
% Move the mask across image as follows: put the center of the mask at each
% pixel of the extended (zero-padded) image. Multiply the corresponding
% elements, and add them, then put the result on the center pixel.
g = zeros(size(e));
for i=2:n+1
for j=2:n+1
m = e(i-1:i+5, j-1:j+5); % 7 by 7square, center at (i,j)
g(i,j) = sum(sum(h .* m));
end
end
cg = g(2:n+1,2:n+1)
imshow(cg);
