Markov Random Field Modeling in Image Analysis / Edition 3

Markov Random Field Modeling in Image Analysis / Edition 3

by Stan Z. Li
     
 

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ISBN-10: 1848002785

ISBN-13: 9781848002784

Pub. Date: 03/10/2009

Publisher: Springer London

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. Various vision models are presented

Overview

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. 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 third edition includes the most recent advances and has new and expanded sections on topics such as: Bayesian Network; Discriminative Random Fields; Strong Random Fields; Spatial-Temporal Models; Learning MRF for Classification. 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.

Product Details

ISBN-13:
9781848002784
Publisher:
Springer London
Publication date:
03/10/2009
Series:
Advances in Computer Vision and Pattern Recognition Series
Edition description:
3rd ed. 2009
Pages:
362
Product dimensions:
6.30(w) x 9.30(h) x 1.00(d)

Table of Contents

Mathematical MRF Models.- Low-Level MRF Models.- High-Level MRF Models.- Discontinuities in MRF#x0027;s.- MRF Model with Robust Statistics.- MRF Parameter Estimation.- Parameter Estimation in Optimal Object Recognition.- Minimization – Local Methods.- Minimization – Global Methods.

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