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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