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    <p>Use <code>houghCircle</code> directly on your image, don't extract edges first. Then test for each detected circle, how much percentage is really present in the image:</p> <pre><code>int main() { cv::Mat color = cv::imread("../houghCircles.png"); cv::namedWindow("input"); cv::imshow("input", color); cv::Mat canny; cv::Mat gray; /// Convert it to gray cv::cvtColor( color, gray, CV_BGR2GRAY ); // compute canny (don't blur with that image quality!!) cv::Canny(gray, canny, 200,20); cv::namedWindow("canny2"); cv::imshow("canny2", canny&gt;0); std::vector&lt;cv::Vec3f&gt; circles; /// Apply the Hough Transform to find the circles cv::HoughCircles( gray, circles, CV_HOUGH_GRADIENT, 1, 60, 200, 20, 0, 0 ); /// Draw the circles detected for( size_t i = 0; i &lt; circles.size(); i++ ) { Point center(cvRound(circles[i][0]), cvRound(circles[i][1])); int radius = cvRound(circles[i][2]); cv::circle( color, center, 3, Scalar(0,255,255), -1); cv::circle( color, center, radius, Scalar(0,0,255), 1 ); } //compute distance transform: cv::Mat dt; cv::distanceTransform(255-(canny&gt;0), dt, CV_DIST_L2 ,3); cv::namedWindow("distance transform"); cv::imshow("distance transform", dt/255.0f); // test for semi-circles: float minInlierDist = 2.0f; for( size_t i = 0; i &lt; circles.size(); i++ ) { // test inlier percentage: // sample the circle and check for distance to the next edge unsigned int counter = 0; unsigned int inlier = 0; cv::Point2f center((circles[i][0]), (circles[i][1])); float radius = (circles[i][2]); // maximal distance of inlier might depend on the size of the circle float maxInlierDist = radius/25.0f; if(maxInlierDist&lt;minInlierDist) maxInlierDist = minInlierDist; //TODO: maybe paramter incrementation might depend on circle size! for(float t =0; t&lt;2*3.14159265359f; t+= 0.1f) { counter++; float cX = radius*cos(t) + circles[i][0]; float cY = radius*sin(t) + circles[i][1]; if(dt.at&lt;float&gt;(cY,cX) &lt; maxInlierDist) { inlier++; cv::circle(color, cv::Point2i(cX,cY),3, cv::Scalar(0,255,0)); } else cv::circle(color, cv::Point2i(cX,cY),3, cv::Scalar(255,0,0)); } std::cout &lt;&lt; 100.0f*(float)inlier/(float)counter &lt;&lt; " % of a circle with radius " &lt;&lt; radius &lt;&lt; " detected" &lt;&lt; std::endl; } cv::namedWindow("output"); cv::imshow("output", color); cv::imwrite("houghLinesComputed.png", color); cv::waitKey(-1); return 0; } </code></pre> <p>For this input:</p> <p><img src="https://i.stack.imgur.com/bnxiX.png" alt="enter image description here"></p> <p>It gives this output:</p> <p><img src="https://i.stack.imgur.com/7jVXC.png" alt="enter image description here"></p> <p>The red circles are Hough results.</p> <p>The green sampled dots on the circle are inliers.</p> <p>The blue dots are outliers.</p> <p>Console output:</p> <pre><code>100 % of a circle with radius 27.5045 detected 100 % of a circle with radius 25.3476 detected 58.7302 % of a circle with radius 194.639 detected 50.7937 % of a circle with radius 23.1625 detected 79.3651 % of a circle with radius 7.64853 detected </code></pre> <p>If you want to test RANSAC instead of Hough, have a look at <a href="http://www.dcs.warwick.ac.uk/bmvc2007/proceedings/CD-ROM/papers/paper-260.pdf" rel="nofollow noreferrer">this</a>.</p>
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    1. COExcellent! It worked :) but why doesn't it work on edges? My objective was to reduce the wrong circle identification by having a lower limit on the contour size... that way I get a clean processed image.
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    2. COI'm not sure. I guess that's because of openCv's houghCircle using canny internally. If you work on edges, canny doesnt work. If you work on thick edges, canny gives 2 circles close to each other which interfere. But that's just a guess.
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    3. CO@harsh: added another answer using RANSAC (much work to be done yet) that works on the edge image directly
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