1995 MarkovRandomFieldModeling

• (Li, 1995) ⇒ Stan Z. Li. (1995). “Markov Random Field Modeling in Computer Vision.” In: Springer-Verlag. ISBN:4431701451 doi:10.1007/BFb0028368

Subject Headings: Textbook, Markov Random Field Modeling, Computer Vision.

Notes

• Website http://www.cbsr.ia.ac.cn/users/szli/mrf_book/book.html
• Table of Contents
  • 7.2.3 Linear Classification Function

Cited By

~1084 http://scholar.google.com/scholar?cites=7758552661055981974

Quotes

Abstract

• A variety of computer vision problems can be optimally posed as Bayesian labeling in which the solution of a problem is defined as the maximum a posteriori (MAP) probability estimate of the true labeling. The posterior probability is usually derived from a prior model and a likelihood model. The latter relates to how data is observed and is problem domain dependent. The former depends on how various prior constraints are expressed. Markov Random Field Models (MRF) theory is a tool to encode contextual constraints into the prior probability. This paper presents a unified approach for MRF modeling in low and high level computer vision. The unification is made possible due to a recent advance in MRF modeling for high level object recognition. Such unification provides a systematic approach for vision modeling based on sound mathematical principles.
• 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 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.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
1995 MarkovRandomFieldModeling	Stan Z. Li			Markov Random Field Modeling in Computer Vision		Springer-Verlag	http://www.springerlink.com/content/w615j5q411564233/	10.1007/BFb0028368		1995