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    <p>A few things you can try to improve your results:</p> <h2>Apply a Region of Interest</h2> <p>Your image looks to have some bordering window effects. I removed them with a region of interest resulting in an image that looks like this (I tweaked it until it looked right, but if you're using some kind of kernel operator it's window size probably better defines this ROI):</p> <p><img src="https://i.stack.imgur.com/YUJqS.jpg" alt="enter image description here"></p> <h2>Use standard Hough transform</h2> <p>It also seems you're using the probabilistic Hough transform. So, you're only getting line segments instead of an interpolated line. Consider using the standard transform to get the full theoretical line (rho, theta). Doing this I got an image like shown below:</p> <p><img src="https://i.stack.imgur.com/llni2.jpg" alt="enter image description here"></p> <p>Here is a code snippet I used to generate the lines (from Python interface):</p> <pre><code>(mu, sigma) = cv2.meanStdDev(stairs8u) edges = cv2.Canny(stairs8u, mu - sigma, mu + sigma) lines = cv2.HoughLines(edges, 1, pi / 180, 70) </code></pre> <h2>Filter lines based on angle</h2> <p>You can probably filter out poor lines by taking the most frequently occurring line angles, and throwing away outliers. This should narrow it down to the most visible steps.</p> <p>Hope that helps!</p>
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    1. COThanks alot for the reply. The problem with standard houghman, that it detects too long edges, i.e. edges are detected where's actually no edges. I believe detecting too few edges and then reconstructing the actual edges is better than too many false positives. Even if I take lines with dominant orientation and remove outliers I'll have too many lines, so I'll probably use the probabilistic houghman and reconstrct the missing lines. Best regards
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      1. USMoataz Elmasry
    2. COWell, the length of the line is an artifact of the drawing process (since lines proceed to +/- infinity). You can limit the length of lines since you have a Canny mask. After you eliminate the poor lines by angle (theta), then you'll probably need to cluster them by their radial parameter (rho) this should get you down to the desired number of lines. You'll need to do this clustering regardless of standard or probabilistic since you have multiple lines per step edges.
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    3. COHello. Unfortuantly I can't remove the poor lines, since I also need to detect the boundaries of the stairs(left/right). But it can be tolrated that some edges are undetected (better than false positives).What I'm doing right now is 1- detect edges with probabilistic houghman.2- build a 2d histogram of edges based on rho and theta. 3- merge almost colinear line segments. in each bin -4 remove short lines -5-due to discritization problem of histogram, lines almost on the boundary of a bin but could be merged with a line from the neighbor bin is then migrated to the other bin and lines are merg
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