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Blake A., Kohli P., Rother C. (eds.) Markov Random Fields for Vision and Image Processing

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Blake A., Kohli P., Rother C. (eds.) Markov Random Fields for Vision and Image Processing
Издательство Elsevier, 2011, -472 pp.
This volume demonstrates the power of the Markov random field (MRF) in vision, treating the MRF both as a tool for modeling image data and, utilizing recently developed algorithms, as a means of making inferences about images. These inferences concern underlying image and scene structure as well as solutions to such problems as image reconstruction, image segmentation, 3D vision, and object labeling. It offers key findings and state-of-the-art research on both algorithms and applications. After an introduction to the fundamental concepts used in MRFs, the book reviews some of the main algorithms for performing inference with MRFs; presents successful applications of MRFs, including segmentation, super-resolution, and image restoration, along with a comparison of various optimization methods; discusses advanced algorithmic topics; addresses limitations of the strong locality assumptions in the MRFs discussed in earlier chapters; and showcases applications that use MRFs in more complex ways, as components in bigger systems or with multiterm energy functions. The book will be an essential guide to current research on these powerful mathematical tools.
Introduction to Markov Random Fields
I Algorithms for Inference of MAP Estimates for MRFs
Basic Graph Cut Algorithms
Optimizing Multilabel MRFs Using Move-Making Algorithms
Optimizing Multilabel MRFs with Convex and Truncated Convex Priors
Loopy Belief Propagation, Mean Field Theory, and Bethe Approximations
Linear Programming and Variants of Belief Propagation
II Applications of MRFs, Including Segmentation
Interactive Foreground Extraction: Using Graph Cut
Continuous-Valued MRF for Image Segmentation
Bilayer Segmentation of Video
MRFs for Superresolution and Texture Synthesis
A Comparative Study of Energy Minimization Methods for MRFs
III Further Topics: Inference, Parameter Learning, and Continuous Models
Convex Relaxation Techniques for Segmentation, Stereo, and Multiview Reconstruction
Learning Parameters in Continuous-Valued Markov Random Fields
Message Passing with Continuous Latent Variables
Learning Large-Margin Random Fields Using Graph Cuts
Analyzing Convex Relaxations for MAP Estimation
MAP Inference by Fast Primal-Dual Linear Programming
Fusion-Move Optimization for MRFs with an Extensive Label Space
IV Higher-Order MRFs and Global Constraints
Field of Experts
Enforcing Label Consistency Using Higher-Order Potentials
Exact Optimization for Markov Random Fields with Nonlocal Parameters
Graph Cut-Based Image Segmentation with Connectivity Priors
V Advanced Applications of MRFs
Symmetric Stereo Matching for Occlusion Handling
Steerable Random Fields for Image Restoration
Markov Random Fields for Object Detection
SIFT Flow: Dense Correspondence across Scenes and Its Applications
Unwrap Mosaics: A Model for Deformable Surfaces in Video
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