A Probabilistic Approach to Perceptual Grouping

Abstract

We present a general framework for determining probability distributions over the space of possible image feature groupings. The framework can be used to find several of the most probable partitions of image features into groupings, rather than just returning a single partition of the features as do most feature grouping techniques. In addition to the groupings themselves, the probability of each partition is computed, providing information on the relative probability of multiple partitions that few grouping techniques offer. In determining the probability distribution of groupings, no parameters are estimated, thus eliminating problems that occur with small data sets and outliers such as the compounding of errors that can occur when parameters are estimated and the estimated parameters are used in the next grouping step. We have instantiated our framework for the two special cases of grouping line segments into straight lines and for grouping bilateral symmetries with parallel axes, where bilateral symmetries are formed by pairs of edges. Results are presented for these cases on several real images.