Markov random field modeling in image analysis
著者
書誌事項
Markov random field modeling in image analysis
（Advances in pattern recognition）
Springer, c2010
3rd ed
- : pbk.
大学図書館所蔵 全1件
注記
Includes bibliographical references (p. 315-350) and index
内容説明・目次
内容説明
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. 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 third edition includes the most recent advances and has new and expanded sections on topics such as: Bayesian Network; Discriminative Random Fields; Strong Random Fields; Spatial-Temporal Models; Learning MRF for Classification. 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.
目次
- Mathematical MRF Models.- Low-Level MRF Models.- High-Level MRF Models.- Discontinuities in MRF#x0027
- s.- MRF Model with Robust Statistics.- MRF Parameter Estimation.- Parameter Estimation in Optimal Object Recognition.- Minimization - Local Methods.- Minimization - Global Methods.
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