Markov Models for Image Labeling

Markov Models for Image Labeling

Markov random field (MRF) is a widely used probabilistic model for expressing interaction of different events. One of the most successful applications is to solve image labeling problems in computer vision. This paper provides a survey of recent advances in this field. We give the background, basic...

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Main Authors: S. Y. Chen, Hanyang Tong, Carlo Cattani
Format: Article
Language:English
Published: Hindawi Limited 2012-01-01
Series:Mathematical Problems in Engineering
Online Access:http://dx.doi.org/10.1155/2012/814356
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spelling doaj-b02d2f29f9354beea30bc8e79fc049db2020-11-24T22:31:05ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472012-01-01201210.1155/2012/814356814356Markov Models for Image LabelingS. Y. Chen0Hanyang Tong1Carlo Cattani2College of Computer Science, Zhejiang University of Technology, Hangzhou 310023, ChinaCollege of Computer Science, Zhejiang University of Technology, Hangzhou 310023, ChinaDepartment of Mathematics, University of Salerno, Via Ponte Don Melillo, 84084 Fisciano (Sa), ItalyMarkov random field (MRF) is a widely used probabilistic model for expressing interaction of different events. One of the most successful applications is to solve image labeling problems in computer vision. This paper provides a survey of recent advances in this field. We give the background, basic concepts, and fundamental formulation of MRF. Two distinct kinds of discrete optimization methods, that is, belief propagation and graph cut, are discussed. We further focus on the solutions of two classical vision problems, that is, stereo and binary image segmentation using MRF model.http://dx.doi.org/10.1155/2012/814356
collection DOAJ
language English
format Article
sources DOAJ
author S. Y. Chen
Hanyang Tong
Carlo Cattani
spellingShingle S. Y. Chen
Hanyang Tong
Carlo Cattani
Markov Models for Image Labeling
Mathematical Problems in Engineering
author_facet S. Y. Chen
Hanyang Tong
Carlo Cattani
author_sort S. Y. Chen
title Markov Models for Image Labeling
title_short Markov Models for Image Labeling
title_full Markov Models for Image Labeling
title_fullStr Markov Models for Image Labeling
title_full_unstemmed Markov Models for Image Labeling
title_sort markov models for image labeling
publisher Hindawi Limited
series Mathematical Problems in Engineering
issn 1024-123X
1563-5147
publishDate 2012-01-01
description Markov random field (MRF) is a widely used probabilistic model for expressing interaction of different events. One of the most successful applications is to solve image labeling problems in computer vision. This paper provides a survey of recent advances in this field. We give the background, basic concepts, and fundamental formulation of MRF. Two distinct kinds of discrete optimization methods, that is, belief propagation and graph cut, are discussed. We further focus on the solutions of two classical vision problems, that is, stereo and binary image segmentation using MRF model.
url http://dx.doi.org/10.1155/2012/814356
work_keys_str_mv AT sychen markovmodelsforimagelabeling
AT hanyangtong markovmodelsforimagelabeling
AT carlocattani markovmodelsforimagelabeling
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