From simple assumptions for the distributions of segment lengths, orientations and positions, we compute, as a function of this measure, a probability density for the accidental occurrence of junctions. Perceptual rules allow the extraction of a representative population of non accidental junctions from an image, from which a probability density for the non accidental occurrence of deviations is computed. From these two distributions, we perform the bayesian dentification of likely non-accidental junctions. These are probabilistically combined into chains, through a procedure that takes the interdependence of junctions into account. This procedure is to our knowledge original, and represents a practical and accurate simplification of an otherwise exponentially complex analysis.
Successive iterations allow the bridging of larger gaps.
The method uses both geometric and photometric information, allows for segment curvature, and automatically extracts statistics for natural image contours. Examples are presented. Both issues were raised by the Gestaltists in the early part of this century. It will be argued that object descripition and recognition is a key goal for perceptual organization. We will examine a hypothesize and verify approach to feature grouping.