Visual (or Vision?) Measuring Machine: Recognition Refined By Iterated Fitting 

Visual (or Vision?) Measuring Machine: Recognition Refined By Iterated Fitting

Things become ugly when they are zoomed very large, curves can be zig-zag and straight lines are not straight anymore.
Hough algorithm won't solve all problems. They are good at identifying parametric shapes, even the shapes are broken or not complete.
But when the shape itself cannot be approximated by parametric equations, Hough algorithms would find many scattered local maxima in the parameter space.
So when the images become ugly, we gotta fit something instead of detect a nice shape from the image.
To cope with the noise and to prune adjuncted edges from what we want, an iterated fitting strategy should be used to remove those points with large residuals with decreasing thresholds.

The steps are:
(1) track the edges
(2) fit it to line/circle/ellipse
(3) prune the points that has larger residual than current threshold
(4) decrease the threshold and goto (2)


fig. A plug zoom 30 times with a line fitted.