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Markov Models for Image Labeling
Joint Authors
Tong, Hanyang
Cattani, Carlo
Chen, Sheng-yong
Source
Mathematical Problems in Engineering
Issue
Vol. 2012, Issue 2012 (31 Dec. 2012), pp.1-18, 18 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2011-08-03
Country of Publication
Egypt
No. of Pages
18
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Chen, Sheng-yong& Tong, Hanyang& Cattani, Carlo. 2011. Markov Models for Image Labeling. Mathematical Problems in Engineering،Vol. 2012, no. 2012, pp.1-18.
https://search.emarefa.net/detail/BIM-1029775
Modern Language Association (MLA)
Chen, Sheng-yong…[et al.]. Markov Models for Image Labeling. Mathematical Problems in Engineering No. 2012 (2012), pp.1-18.
https://search.emarefa.net/detail/BIM-1029775
American Medical Association (AMA)
Chen, Sheng-yong& Tong, Hanyang& Cattani, Carlo. Markov Models for Image Labeling. Mathematical Problems in Engineering. 2011. Vol. 2012, no. 2012, pp.1-18.
https://search.emarefa.net/detail/BIM-1029775
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1029775