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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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