Markov Random Field Modeling in Computer Vision

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Markov random field (MRF) theory provides a basis for modeling contextual constraints in visual processing and interpretation. It enables us to develop optimal vision algorithms systematically when used with optimization principles. This book presents a comprehensive study on the use of MRFs for solving computer vision problems. The book covers the following parts essential to the subject: introduction to fundamental theories, formulations of MRF vision models, MRF parameter estimation, and optimization algorithms. Various vision models are presented in a unified framework, including image restoration and reconstruction, edge and region segmentation, texture, stereo and motion, object matching and recognition, and pose estimation. This book is an excellent reference for researchers working in computer vision, image processing, statistical pattern recognition and applications of MRFs. It is also suitable as a text for advanced courses in these areas.

Stan Z. Li

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Cliques | Computer Vision | Gibbs Random Field | Markov Random Fields (MRF) | MRF Models | MRF Parameter Estimation |

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Added	13 Jan 2009
Updated	06 Feb 2009
Authors	Stan Z. Li

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Markov Random Field Modeling in Computer Vision

Cliques | Computer Vision | Gibbs Random Field | Markov Random Fields (MRF) | MRF Models | MRF Parameter Estimation |

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