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This book describes the design of a complete, flexible system for perceptual organization in computer vision using graph theoretic techniques, voting methods, and an extension of the Bayesian networks called perceptual inference networks (PINs). The PIN, which forms the heart of the algorithm, is based on Bayesian probabilistic networks and has potential for being used in other spatial information tasks. The book also has a comprehensive review of the prior work in the area. It not only classifies the prior work but also identifies some areas of future work.
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This book describes the design of a complete, flexible system for perceptual organization in computer vision using graph theoretic techniques, voting methods, and an extension of the Bayesian networks called perceptual inference networks (PINs). The PIN, which forms the heart of the algorithm, is based on Bayesian probabilistic networks and has potential for being used in other spatial information tasks. The book also has a comprehensive review of the prior work in the area. It not only classifies the prior work but also identifies some areas of future work.