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    <p>I've implemented a similar algorithm before, and I did it in a sort of incremental least-squares fashion. It worked fairly well. The pseudocode is somewhat like:</p> <pre><code>L = empty set of line segments for each white pixel p line = new line containing only p C = empty set of points P = set of all neighboring pixels of p while P is not empty n = first point in P add n to C remove n from P line' = line with n added to it perform a least squares fit of line' if MSE(line) &lt; max_mse and d(line, n) &lt; max_distance line = line' add all neighbors of n that are not in C to P if size(line) &gt; min_num_points add line to L </code></pre> <p>where MSE(line) is the mean-square-error of the line (sum over all points in the line of the squared distance to the best fitting line) and d(line,n) is the distance from point n to the line. Good values for max_distance seem to be a pixel or so and max_mse seems to be much less, and will depend on the average size of the line segments in your image. 0.1 or 0.2 pixels have worked in fairly large images for me.</p> <p>I had been using this on actual images pre-processed with the Canny operator, so the only results I have are of that. Here's the result of the above algorithm on an image: <img src="https://i.stack.imgur.com/GRLJv.png" alt="Raw image"> <img src="https://i.stack.imgur.com/yEJN6.png" alt="Detected segments"></p> <p>It's possible to make the algorithm fast, too. The C++ implementation I have (closed source enforced by my job, sorry, else I would give it to you) processed the above image in about 20 milliseconds. That includes application of the Canny operator for edge detection, so it should be even faster in your case.</p>
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