Perceptual organization using Bayesian networks

Abstract

It is shown that the formalism of Bayesian networks provides an elegant solution, in a probabilistic framework, to the problem of integrating top-down and bottom-up visual processes as well serving as a knowledge base. The formalism is modified to handle spatial data and thus extends the applicability of Bayesian networks to visual processing. The modified form is called the perceptual inference network (PIN). The theoretical background of a PIN is presented, and its viability is demonstrated in the context of perceptual organization. The PIN imparts an active inferential and integrating nature to perceptual organization.<>