Hi, ok it seams reasonable that its caused by canny because with other edge detection method (starburst) the ellipse fit works fine. Certainly <http://www.dict.cc/englisch-deutsch/certainly.html> 0.4 px isn't too bad. But my aim is a very high accuracy and the outliers are clearly systematic <http://www.dict.cc/englisch-deutsch/systematic.html>error <http://www.dict.cc/englisch-deutsch/error.html>s so they should be avoidable. Upsampling sounds like a good Idea. I tried it like this: ... img_upsampled = cv2.resize(img, (0, 0), fx=(8.0), fy=(8.0), interpolation = cv2.INTER_CUBIC) ret, thresh = cv2.threshold(img_upsampled, 20, 255, cv2.THRESH_BINARY_INV) img = canny(thresh, sigma=3).astype(np.uint8) img[img > 0] = 255 coords = np.column_stack(np.nonzero(img)) model, inliers = measure.ransac(coords, measure.EllipseModel, min_samples=5, residual_threshold=1, max_trials=200) cx = model.params[1] / 8.0 cy = model.params[0] / 8.0 But there are still a lot of outliers. The upsampled canny image doesn't look too good (see image). I also tried without thresholding and with different interpolation methods but without success. Regards, Arno
