hough ellipse fit inaccurate?

[Johannes Schoenberger]

No, that’s not good. You need 5 points to estimate an ellipse model, and you should stick to the minimum parameters with RANSAC. Otherwise, you have to sample exponentially more to converge to a confident correct solution.

Try something like: min_samples=5, max_trials>200 (depending on the outlier ratio of your edge points this may have to increase significantly), residual_threshold>2 (depending on the spread of your edge points, excluding the outlier points)

Hope this helps, otherwise the only thing that helps would be to share your images and a code snippet.

Best, Johannes

> On Mar 7, 2015, at 8:32 AM, Arno Dietz <arnodietz86 at googlemail.com> wrote:
> 
> 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) 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
> 
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