A contrario detection of good continuation of points

Abstract

We will consider the problem of detecting configurations of points regularly spaced and lying on a smooth curve. This corresponds to the notion of good continuation introduced in the Gestalt theory. We present a robust algorithm for clustering points along such curves, whilst at the same time discarding noisy samples. Based on the a contrario methodology, the detector builds upon a simple, symmetric primitive for a triplet of points, and finds statistically meaningful chains of such triplets. An efficient implementation is proposed using the Floyd-Warshall algorithm. Experiments on synthetic and real data show that the method is able to identify the perceptually relevant configuration of points in good continuation.