Chen, S. Y.; Tong, Hanyang; Wang, Zhongjie; Liu, Sheng; Li, Ming; Zhang, Beiwei Improved generalized belief propagation for vision processing. (English) Zbl 1202.94026 Math. Probl. Eng. 2011, Article ID 416963, 12 p. (2011). Summary: Generalized belief propagation (GBP) is a region-based belief propagation algorithm which can get good convergence in Markov random fields. However, the computation time is too heavy to use in practical engineering applications. This paper proposes a method to accelerate the efficiency of GBP. A caching technique and chessboard passing strategy are used to speed up algorithm. Then, the direction set method which is used to reduce the complexity of computing clique messages from quadric to cubic. With such a strategy the processing speed can be greatly increased. Besides, it is the first attempt to apply GBP for solving the stereomatching problem. Experiments show that the proposed algorithm can speed up by 15+ times for typical stereo matching problem and infer a more plausible result. Cited in 33 Documents MSC: 94A08 Image processing (compression, reconstruction, etc.) in information and communication theory 94A12 Signal theory (characterization, reconstruction, filtering, etc.) PDF BibTeX XML Cite \textit{S. Y. Chen} et al., Math. Probl. Eng. 2011, Article ID 416963, 12 p. (2011; Zbl 1202.94026) Full Text: DOI EuDML References: [1] M. J. Wainwright, T. S. Jaakkola, and A. S. Willsky, “MAP estimation via agreement on trees: message-passing and linear programming,” IEEE Transactions on Information Theory, vol. 51, no. 11, pp. 3697-3717, 2005. · Zbl 1318.94025 [2] J. Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, Morgan Kaufmann, 1988. · Zbl 0746.68089 [3] J. S. Yedidia, T. F. William, and Y. 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