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Markov random field (MRF) modelling provides a basis for the characterization for contextual constraints on visual interpretation which allows for development of optimal vision algorithms systematically based on sound principles. This text presents a study on using MRFs to solve computer vision problems, covering areas such as: introduction to fundamental theories; formulations of various vision models in the MRF framework; MRF parameter estimation; and optimization algorithms. Various MRF vision models are presented in a unified form, including image restoration and reconstruction, edge and region segmentation, texture, stereo and motion, object matching and recognition, and pose estimation. This book should be a useful reference for researchers working in computer vision, image processing, pattern recognition and applications of MRFs.