Format:
Online-Ressource (XIX, 323 p)
,
digital
Edition:
Springer eBook Collection. Computer Science
ISBN:
9784431670445
Series Statement:
Computer Science Workbench
Content:
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 second edition includes the most important progress in Markov modeling in image analysis in recent years such as Markov modeling of images with "macro" patterns (e.g. the FRAME model), Markov chain Monte Carlo (MCMC) methods, reversible jump MCMC. 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
Additional Edition:
ISBN 9784431703099
Additional Edition:
Erscheint auch als Druck-Ausgabe ISBN 9784431670452
Additional Edition:
Erscheint auch als Druck-Ausgabe ISBN 9784431703099
Language:
English
DOI:
10.1007/978-4-431-67044-5
URL:
Volltext
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