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.
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