Do you mean the max_trials parameter? At the moment I use these: "model, inliers = measure.ransac(coords, measure.EllipseModel, min_samples=10, residual_threshold=1, max_trials=100)" I varied these parameters (min_samples=5 to 40, residual_threshold=0.005 to 10, max_trials=10 to 400) but with no success. The images with outliers remain the same with equal parameters but with different parameters the outliers appear on different images. Sometimes there also appear a warning message but in this case it is actually random, when it occurs: C:\Anaconda\lib\site-packages\scipy\optimize\minpack.py:419: RuntimeWarning: Number of calls to function has reached maxfev = 2600. warnings.warn(errors[info][0], RuntimeWarning) Another interesting fact, when I use a starburst algorithm (like this <http://www.frontiersin.org/files/Articles/2035/fnins-04-00193-HTML/image_m/f...>) to detect my points for ellipse fitting instead of the canny edge detector, it seems to work fine mostly without outliers. I think the only difference is, that my starburst algorithm generates much less points (about 300) then canny. Regards, Arno