# How to implement a gradient based Hough transform

I am trying to use the Hough transform for edge detection, and would like to use gradient images as the basis.

What I have done so far, given the image I of size [M,N] and its partial derivatives gx, gy, is to calculate the gradient angle in each pixel as thetas = atan(gy(x,y) ./ gx. Similarly I calculate the gradient magnitude as magnitudes = sqrt(gx.^2+gy.^2).

To build the Hough transform, I use the following MATLAB code:

max_rho = ceil(sqrt(M^2 + N^2));
hough = zeros(2*max_rho, 101);
for x=1:M
for y=1:N
theta = thetas(x,y);
rho = x*cos(theta) + y*sin(theta);

rho_idx = round(rho)+max_rho;
theta_idx = floor((theta + pi/2) / pi * 100) + 1;
hough(rho_idx, theta_idx) = hough(rho_idx, theta_idx) + magnitudes(x,y);
end
end


The resulting Hough transform looks plausible (see http://i.stack.imgur.com/hC9mP.png), but when I try to use its maxima as edge parameters in the original image, the results look more or less random. Did I do something wrong in constructing the Hough transform?

UPDATE: I had a stupid mistake in my code: rho was calculated as x*cos(theta)+y*cos(theta) instead of x*cos(theta)+y*sin(theta). That is, I was using two cosines instead of a cosine and a sine. I have edited the code above and the new resulting image is below. This did not give much better edges though.

@endolith: To plot an edge, given a maximal value in the hough-matrix at rho_idx, theta_idx, I translate the indices to rho,theta values:

theta = (theta_idx -1) / 100 * pi - pi / 2;
rho = rho_idx - max_rho;


Finally I plot the edge as y= (rho - x*cos(theta)) / sin(theta). • "when I try to use its maxima as edge parameters in the original image" How are you doing that? – endolith Oct 27 '11 at 19:41
• @Jonas Due Vesterheden Just wondering is this a time VS doppler frequency image?... – Spacey Nov 6 '11 at 22:25
• @Mohammad: Not that I know of. The original image is of some circuit board. What do you mean by "VS"? – Jonas Due Vesterheden Nov 9 '11 at 14:02
• @JonasDueVesterheden 'VS' just means 'versus'. (Time versus doppler frequency') :-) – Spacey Nov 10 '11 at 16:29
• You should smooth your hough map before applying Non max suppression to it. – user791 Jan 12 '12 at 11:10

If you want to detect straight lines, you would be better off starting with an edge map, and then using the hough function if the Image Processing toolbox, if you have access to it. The problem with doing a Hough transform on the gradient is that edge pixels forming a straight line might have opposite gradient orientations. For example, consider a checkerboard pattern. A edge between two rows of squares flips orientation depending on whether you have a black square above and a white square below, or the other way around.
• Thanks for the suggestion! As I understand it, it should be sufficient to use the angles from -90 to +90 degrees, since the "directions" of edges do not matter. I tried using atan2, but it didn't seem to fix the problems. – Jonas Due Vesterheden Oct 28 '11 at 10:16