It has long been known that our vision takes advantage of the closed contours which border the perimeters of every object. Yet the information processing by which such contours could be identified was unknown. Given the predominance of visual-cortex neurons which detect and process segments of edges, the question becomes, What distribution of contours does a set of edge elements characterize? To this question mathematics is ugh these edge segments are curves of least constraint, I will derive these distributions, and express them in an unexpectedly concise manner. The results greatly simplify the segmentation of contours in real images, significantly out-performing all other methods, and enable us to understand first-order transitions in illusions.
It is not hard to imagine a time when computers will utilize patterns of information processing adapted from those used by regionsof the brain. This offers us the challenge of understanding the nature of this processing independent of the means employed to effect it.