Items related to Markov Random Field Modeling in Image Analysis
Synopsis
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.
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