Distributed propagation of a-priori constraints in a Bayesian network of Markov random fields
- 1 January 1993
- journal article
- Published by Institution of Engineering and Technology (IET) in IEE Proceedings I Communications, Speech and Vision
- Vol. 140 (1), 46-55
- https://doi.org/10.1049/ip-i-2.1993.0008
Abstract
In this paper, Bayesian networks of Markov random fields (BN-MRFs) are proposed as a technique for representing and applying apriori knowledge at different abstraction levels inside a distributed image processing framework. It is shown that this approach, thanks to the common probabilistic basis of the two techniques, is able to combine in a natural way causal inference properties at different abstraction levels as provided by Bayesian networks with optimisation criteria usually applied to find the best configuration for an MRF. Examples of two-level BN-MRFs are given, where each node uses a coupled Markov random field which has to solve a coupled restoration and segmentation problem. Experiments are concerned with expert-driven registered segmentation and tracking of regions from image sequences.Keywords
This publication has 3 references indexed in Scilit:
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- BAYESIAN INFERENCEPublished by Elsevier BV ,1988