Markov Random Field Modeling In Image Analysis, 3rd Edition (200

Markov random field (MRF) theory provides a basis for modeling contextual constraints in visual processing and interpretation. It enables systematic development of optimal vision algorithms when used with optimization principles.

This detailed and thoroughly enhanced third edition presents a comprehensive study / reference to theories, methodologies and recent developments in solving computer vision problems based on MRFs, statistics and optimisation. It treats various problems in low- and high-level computational vision in a systematic and unified way within the MAP-MRF framework. Among the main issues covered are: how to use MRFs to encode contextual constraints that are indispensable to image understanding; how to derive the objective function for the optimal solution to a problem; and how to design computational algorithms for finding an optimal solution.

Easy-to-follow and coherent, the revised edition is accessible, includes the most recent advances, and has new and expanded sections on such topics as:
• Discriminative Random Fields (DRF)
• Strong Random Fields (SRF)
• Spatial-Temporal Models
• Total Variation Models
• Learning MRF for Classification (motivation + DRF)
• Relation to Graphic Models
• Graph Cuts
• Belief Propagation