Markov Random Fields: Theory and Application

This book introduces the theory and applications of Markov Random Fields in image processing and computer vision. Modeling images through the local interaction of Markov models has resulted in useful algorithms for problems in texture analysis, image synthesis, image restoration, image segmentation, surface reconstruction and integration of low-level visual modules. All of the contributors are leading researchers from the United States and Europe.

Presents statistical modeling of two- and three-dimensional images
Includes Markov Random Fields, Gibbs Distribution, and Simulated Annealing
Explains integration or fusion of images
Covers image segmentation, texture analysis, and image restoration using MRF models of context
Gives a systematic development of algorithms for image processing, analysis, and computer vision
Presents parallel algorithms for image processing, analysis, and computer vision