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.

Inhalt

Foreword by Anil K. Jain.- Chapter 1. Introduction: 1.1 Visual Labeling. 1.2 Markov Random Fields and Gibbs Distributions. 1.3 Useful MRF Models. 1.4 Optimization-Based Vision. 1.5 Bayes Labeling of MRFs.- Chapter 2. Low Level MRF Models: 2.1 Observation Models. 2.2 Image Restoration and Reconstruction. 2.2 Edge Detection. 2.3 Texture Synthesis and Analysis. 2.4 Optical Flow.- Chapter 3. Discontinuities in MRFs: 3.1 Smoothness, Regularization and Discontinuities. 3.2 The Discontinuity Adaptive MRF Model. 3.3 Computation of DA Solutions. 3.4 Conclusion.- Chapter 4. Discontinuity-Adaptivity Model and Robust Estimation: 4.1 The DA Prior and Robust Statistics. 4.2 Experimental Comparison.- Chapter 5. High Level MRF Models: 5.1 Matching under Relational Constraints. 5.2 MRF-Based Matching. 5.3 Pose Computation.- Chapter 6. MRF Parameter Estimation: 6.1 Supervised Estimation with Labeled Data. 6.2 Unsupervised Estimation with Unlabeled Data. 6.3 Further Issues.- Chapter 7. Parameter Estimation in Optimal Object Recognition: 7.1 Motivation. 7.2 Theory of Parameter Estimation for Recognition. 7.3 Application in MRF Object Recognition. 7.4 Experiments. 7.5 Conclusion.- Chapter 8. Minimization -- Local Methods: 8.1 Classical Minimization with Continuous Labels. 8.2 Minimization with Discrete Labels. 8.3 Constrained Minimization. Chapter 9. Minimization -- Global Methods: 9.1 Simulated Annealing. 9.2 Mean Field Annealing. 9.3 Graduated Non-Convexity. 9.4 Genetic Algorithms. 9.5 Experimental Comparison. 9.6 Accelerating Computation. 9.7 Model Debugging.- References.- List of Notation.- Index.