Group-membership reinforcement for straight edges based on Bayesian networks

A probabilistic approach to edge reinforcement is proposed that is based on Bayesian networks of two-dimensional (2-D) fields of variables. The proposed net is composed of three nodes, each devoted to estimating a field of variables. The first node contains available observations. The second node is associated with a coupled random field representing the estimates of the actual values of observed data and of their discontinuities. At the third node, a field of variables is used to represent parameters describing the membership of a discontinuity into a group. The edge reinforcement problem is stated in terms of minimization of local functionals, each associated with a different node, and made up of terms that can be computed locally. It is shown that a distributed minimization is equivalent to the minimization of a global reinforcement criterion. Results concerning the reinforcement of straight lines in synthetic and real images are reported, and applications to synthetic aperture radar (SAR) images are described