Markov Random Field Modeling in Computer Vision - Tapa blanda

Sinopsis

1 Introduction.- 1.1 Visual Labeling.- 1.1.1 Sites and Labels.- 1.1.2 The Labeling Problem.- 1.1.3 Labeling Problems in Vision.- 1.1.4 Labeling with Contextual Constraints.- 1.2 Markov Random Fields and Gibbs Distributions.- 1.2.1 Neighborhood System and Cliques.- 1.2.2 Markov Random Fields.- 1.2.3 Gibbs Random Fields.- 1.2.4 Markov-Gibbs Equivalence.- 1.2.5 Normalized and Canonical Forms.- 1.3 Useful MRF Models.- 1.3.1 Auto-Models.- 1.3.2 Multi-Level Logistic Model.- 1.3.3 The Smoothness Prior.- 1.3.4 Hierarchical GRF Model.- 1.4 Optimization-Based Vision.- 1.4.1 Research Issues.- 1.4.2 Role of Energy Functions.- 1.4.3 Formulation of Objective Functions.- 1.4.4 Optimality Criteria.- 1.5 Bayes Labeling of MRFs.- 1.5.1 Bayes Estimation.- 1.5.2 MAP-MRF Labeling.- 1.5.3 Regularization.- 1.5.4 Summary of MAP-MRF Approach.- 2 Low Level MRF Models.- 2.1 Observation Models.- 2.2 Image Restoration and Reconstruction.- 2.2.1 MRF Priors for Image Surfaces.- 2.2.2 Piecewise Constant Restoration.- 2.2.3 Piecewise Continuous Restoration.- 2.2.4 Surface Reconstruction.- 2.3 Edge Detection.- 2.3.1 Edge Labeling using Line Process.- 2.3.2 Forbidden Edge Patterns.- 2.4 Texture Synthesis and Analysis.- 2.4.1 MRF Texture Modeling.- 2.4.2 Texture Segmentation.- 2.5 Optical Flow.- 2.5.1 Variational Approach.- 2.5.2 Flow Discontinuities.- 3 Discontinuities in MRFs.- 3.1 Smoothness, Regularization and Discontinuities.- 3.1.1 Regularization and Discontinuities.- 3.1.2 Other Regularization Models.- 3.2 The Discontinuity Adaptive MRF Model.- 3.2.1 Defining the DA Model.- 3.2.2 Relations with Previous Models.- 3.2.3 Discrete Data and 2D Cases.- 3.2.4 Solution Stability.- 3.3 Computation of DA Solutions.- 3.3.1 Solving the Euler Equation.- 3.3.2 Experimental Results.- 3.4 Conclusion.- 4 Discontinuity-Adaptivity Model and Robust Estimation.- 4.1 The DA Prior and Robust Statistics.- 4.1.1 Robust M Estimator.- 4.1.2 Problems with M Estimator.- 4.1.3 Redefinition of M Estimator.- 4.1.4 AM Estimator.- 4.1.5 Convex DA and M-Estimation Models.- 4.2 Experimental Comparison.- 4.2.1 Location Estimation.- 4.2.2 Rotation Angle Estimation.- 5 High Level MRF Models.- 5.1 Matching under Relational Constraints.- 5.1.1 Relational Structure Representation.- 5.1.2 Work in Relational Matching.- 5.2 MRF-Based Matching.- 5.2.1 Posterior Probability and Energy.- 5.2.2 Matching to Multiple Objects.- 5.2.3 Experiments.- 5.2.4 Extensions.- 5.3 Pose Computation.- 5.3.1 Pose Clustering and Estimation.- 5.3.2 Simultaneous Matching and Pose.- 5.3.3 Discussion.- 6 MRF Parameter Estimation.- 6.1 Supervised Estimation with Labeled Data.- 6.1.1 Maximum Likelihood.- 6.1.2 Pseudo-Likelihood.- 6.1.3 Coding Method.- 6.1.4 Mean Field Approximations.- 6.1.5 Least Squares Fit.- 6.2 Unsupervised Estimation with Unlabeled Data.- 6.2.1 Simultaneous Restoration and Estimation.- 6.2.2 Simultaneous Segmentation and Estimation.- 6.2.3 Expectation-Maximization.- 6.2.4 Cross Validation.- 6.3 Further Issues.- 6.3.1 Estimating the Number of MRFs.- 6.3.2 Reduction of Nonzero Parameters.- 7 Parameter Estimation in Optimal Object Recognition.- 7.1 Motivation.- 7.2 Theory of Parameter Estimation for Recognition.- 7.2.1 Optimization-Based Object Recognition.- 7.2.2 Criteria for Parameter Estimation.- 7.2.3 Linear Classification Function.- 7.2.4 A Non-parametric Learning Algorithm.- 7.2.5 Reducing Search Space.- 7.3 Application in MRF Object Recognition.- 7.3.1 Posterior Energy.- 7.3.2 Energy in Linear Form.- 7.3.3 How Minimal Configuration Changes.- 7.3.4 Parametric Estimation under Gaussian Noise.- 7.4 Experiments.- 7.4.1 Recognition of Line Patterns.- 7.4.2 Recognition of Curved Objects.- 7.5 Conclusion.- 8 Minimization - Local Methods.- 8.1 Classical Minimization with Continuous Labels.- 8.2 Minimization with Discrete Labels.- 8.2.1 Iterated Conditional Modes.- 8.2.2 Relaxation Labeling.- 8.2.3 Highest Confidence First.- 8.2.4 Dynamic Programming.- 8.3 Constrained Minimization.- 8.3.1 Penalty Functions.- 8.

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