Markov Random Field Modeling in Image Analysis
2nd Edition
Published on 1. June 2001
Book
Paperback/Softback
XIX, 323 pages
978-4-431-70309-9 (ISBN)
Description
Markov random field (MRF) theory provides a basis for modeling contextual constraints in visual processing and interpretation. It enables us to develop optimal vision algorithms systematically when used with optimization principles. This book presents a comprehensive study on the use of MRFs for solving computer vision problems. The book covers the following parts essential to the subject: introduction to fundamental theories, formulations of MRF vision models, MRF parameter estimation, and optimization algorithms. Various vision models are presented in a unified framework, including image restoration and reconstruction, edge and region segmentation, texture, stereo and motion, object matching and recognition, and pose estimation. This second edition includes the most important progress in Markov modeling in image analysis in recent years such as Markov modeling of images with "macro" patterns (e.g. the FRAME model), Markov chain Monte Carlo (MCMC) methods, reversible jump MCMC. This book is an excellent reference for researchers working in computer vision, image processing, statistical pattern recognition and applications of MRFs. It is also suitable as a text for advanced courses in these areas.
More details
Series
Edition
2nd ed.
Language
English
Place of publication
Tokyo
Japan
Target group
College/higher education
Professional and scholarly
Edition type
Revised edition
Illustrations
99 figs
Dimensions
Height: 23.5 cm
Width: 15.5 cm
Thickness: 18 mm
Weight
490 gr
ISBN-13
978-4-431-70309-9 (9784431703099)
DOI
10.1007/978-4-431-67044-5
Schweitzer Classification
Other editions
New editions
Book
04/2009
3rd Edition
Springer
€160.49
Shipment within 15-20 days
Additional editions
E-Book
03/2013
2nd Edition
Springer
€85.59
Available for download
Previous edition
Book
11/1995
Springer
€149.03
Article exhausted; check for reprint
Content
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.