Markov Models for Image Labeling

被引：65
作者：
Chen, S. Y. [1 ]
Tong, Hanyang [1 ]
Cattani, Carlo [2 ]
机构：
[1] Zhejiang Univ Technol, Coll Comp Sci, Hangzhou 310023, Zhejiang, Peoples R China
[2] Univ Salerno, Dept Math, I-84084 Fisciano, Sa, Italy
来源：
MATHEMATICAL PROBLEMS IN ENGINEERING | 2012年 / 2012卷
基金：
中国国家自然科学基金;
关键词：
EFFICIENT BELIEF PROPAGATION; RANDOM-FIELD MODEL; GRAPH CUTS; ENERGY MINIMIZATION; SEGMENTATION; COLOR; STEREO; MRF;
D O I：
10.1155/2012/814356
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
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.
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页数：18
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