State-wise Potential Description for Modeling Relationships between Distant Nodes in Markov Random Fields(International Session 1)
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概要
- 論文の詳細を見る
In various computer vision applications, Markov random fields (MRFs) have been widely used for statistical modeling of patterns. However, its applications were limited to handle only local relationships, mainly due to the computational complexity. In this paper, we proposed a novel technique which enables to deal with relationships between distant nodes effectively within the MRF framework. For different states of a node, interacting nodes are determined based on that state. Accordingly, an extended potential form is formulated, and a Monte Carlo approximation of the iterated conditional mode (ICM) algorithm was suggested. An implementation confirms the efficacy of the algorithm in detecting complex structures in 2D images.
- 社団法人電子情報通信学会の論文
- 2007-10-18
著者
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Kim Jin
Department Of Anatomy And Cell Biology College Of Veterinary Medicine And Bk21 Program For Veterinar
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Kim Jin
Department Of Computer Science Korea Advanced Institute Of Science And Technology
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Kwon Younghee
Department of Computer Science, Korea Advanced Institute of Science and Technology
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Kwon Younghee
Department Of Computer Science Korea Advanced Institute Of Science And Technology
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