Distributed propagation of a-priori constraints in a Bayesian network of Markov random fields
作者:
C.S.Regazzoni,
V.Murino,
G.Vernazza,
期刊:
IEE Proceedings I (Communications, Speech and Vision)
(IET Available online 1993)
卷期:
Volume 140,
issue 1
页码: 46-55
年代: 1993
DOI:10.1049/ip-i-2.1993.0008
出版商: IEE
数据来源: IET
摘要:
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.
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