Markov random fields for vision and image processing

【实例简介】
A nice book for Markov random fields!
Markov Random Fields for Vision and Image Processing edited by andrew blake, Pushmeet kohli, and carsten rother The mit press Cambridge, massachusetts London england o 2011 Massachusetts Institute of Technology All rights reserved. No part of this book may be reproduced in any form by any electronic or mechanical means (including photocopying, recording, or information storage and retrieval) without permission in writing from the publisher For information about special quantity discounts, please email special_sales @ mitpress mitedu This book was set in Syntax and Times New roman by Westchester Books Composition. Printed and bound in the United States of america Library of Congress Cataloging-in-Publication Data Markov random fields for vision and image processing/edited by Andrew Blake, Pushmeet Kohli, and Carsten Rother P. C Includes bibliographical references and index isbn 978-0-262-01577-6(hardcover: alk. paper) 4. Markov random fields. I. Blake, Andrew 1956-II. Kohli, Pushmeet. III. Rother Carsten on--Mathematics 1. Image processing-Mathematics. 2. Computer graphics--Mathematics. 3. Cor TAI637.M3372011 006.370151dc22 2010046702 10987654321 Contents Introduction to markov random fields Andrew blake and pushmeet kor Algorithms for Inference of MAP Estimates for MRFs 29 2 Basic Graph Cut Algorithms 31 Yuri Boykov and Vladimir Kolmogorov 3 Optimizing Multilabel mRFs Using Move-Making Algorithms 51 Yuri Boykov, Olga veksler, and Ramin Zabih 4 Optimizing Multilabel MRFs with Convex and Truncated Convex Priors 65 Hiroshi Ishikawa and olga Veksler 5 Loopy Belief Propagation, Mean Field Theory, and Bethe Approximations 77 Alan yuill 6 Linear Programming and Variants of Belief Propagation 95 Yair Weiss, Chen Yanover, and Talya Meltzer II Applications of MRFs, Including Segmentation 109 Interactive Foreground Extraction: Using Graph Cut 111 Carsten Rother, Vladimir Kolmogorov, Yuri Boykov, and Andrew Blake 8 Continuous-Valued MRF for Image Segmentation 127 Dheeraj singaraju, leo grady ali Kemal sinop and rene vidal 9 Bilayer Segmentation of video 143 Antonio Criminisi, Geoffrey Cross, Andrew Blake, and Vladimir Kolmogorov 10 MRFs for Superresolution and Texture Synthesis 155 William T Freeman and ce liu Contents 11 A Comparative Study of Energy Minimization Methods for MRFs 167 Richard Szeliski, Ramin Zabih, Daniel Scharstein, Olga veksler, Vladimir Kolmogorov, Aseem Agarwala, Marshall F. Tappen, and Carsten Rother lll Further Topics: Inference, Parameter Learning and Continuous Models 183 12 Convex Relaxation Techniques for Segmentation, Stereo, and Multiview reconstruction 185 Daniel Cremers, Thomas Pock, Kalin Kolev and Antonin Chambolle 13 Learning Parameters in Continuous-Valued Markov Random Fields 201 Marshall F. Tappen 14 Message Passing with Continuous Latent Variables 215 Michael Isard 15 Learning Large-Margin Random Fields Using Graph Cuts 233 Martin Szummer, Pushmeet Kohli, and derek hoiem 16 Analyzing convex relaxations for MAP Estimation 249 M. Pawan Kumar, Vladimir Kolmogorov and Philip h. S. Torr 17 MAP Inference by Fast Primal-Dual Linear Programming 263 Nikos Komodakis 18 Fusion-Move Optimization for mrfs with an Extensive label space 281 Victor Lempitsky, Carsten Rother, Stefan Roth, and Andrew Blake IV Higher-Order mRFs and global Constraints 295 19 Field of Experts 297 Stefan Roth and michael. black 20 Enforcing Label Consistency Using Higher-Order Potentials 311 Pushmeet Kohli, Lubor Ladicky and philip h. S.Torr 21 Exact Optimization for Markov random fields with Nonlocal Parameters 329 Victor Lempitsky, Andrew Blake, and Carsten Rother 22 Graph Cut-Based Image Segmentation with Connectivity Priors 347 Sara Vicente, vladimir Kolmogorov and Carsten Rother v Advanced Applications of MREs 363 23 Symmetric Stereo Matching for Occlusion Handling 365 Jian Sun, Yin Li, Sing Bing Kang, and Heung-Yeung Shum Contents 24 Steerable Random Fields for Image Restoration 377 Stefan roth and michael black 25 Markov Random Fields for Object Detection 389 John Winn and jamie shotton 26 SIFT Flow: Dense Correspondence across Scenes and Its Applications 405 Ce Liu, Jenny Yuen, Antonio Torralba, and William T Freeman 27 Unwrap Mosaics: A Model for Deformable Surfaces in Video 419 Alex Rav-Acha, Pushmeet kohli, Carsten rother and andrew fitzgibbon Biblio graphy 433 Contributors 457 Index 459 Introduction to markov random Fields Andrew blake and Pushmeet kohli This book sets out to demonstrate the power of the markov random field (mrf) in vision It treats the mrf both as a tool for modeling image data and, coupled with a set of recently developed algorithms, as a means of making inferences about images. The inferences con cern underlying image and scene structure to solve problems such as image reconstruction, image segmentation, 3D vision, and object labeling. This chapter is designed to present some of the main concepts used in MRFs, both as a taster and as a gateway to the more detailed chapters that follow as well as a stand-alone introduction to mres The unifying ideas in using MRFs for vision are the following Images are dissected into an assembly of nodes that may correspond to pixels or agglomerations of pixels Hidden variables associated with the nodes are introduced into a model designed to explain"the values(colors )of all the pixels A joint probabilistic model is built over the pixel values and the hidden variables The direct statistical dependencies between hidden variables are expressed by explicitly grouping hidden variables; these groups are often pairs depicted as edges in a graph These properties of MRFs are illustrated in figure 1. 1. The graphs corresponding to such MRF problems are predominantly gridlike, but may also be irregular, as in figure 1.1(c) Exactly how graph connectivity is interpreted in terms of probabilistic conditional depen dency is discussed a little later The notation for image graphs is that the graph g=0, 8) consists of vertices V 1, 2,..., i,..., M)corresponding, for example, to the pixels of the image, and a set of edges& where a ty pical edge is(i,j,i,jE v, and edges are considered to be undirected so that(i,j)and(j, i) refer to the same edge. In the superpixel graph of figure 1.1), the nodes are superpixels, and a pair of superpixels forms an edge in if the two superpixels share a common boundary The motivation for constructing such a graph is to connect the hidden variables associated with the nodes. For example for the task of segmenting an image into foreground and background, each node i(pixel or superpixel) has an associated random variable Xi that 【实例截图】
【核心代码】