Hierarchical non‐parametric Markov random field for image segmentation-Reference-Cited by-同舟云学术

Hierarchical non‐parametric Markov random field for image segmentation

Published:2017-09-25 Issue:8 Volume:11 Page:717-724
ISSN:1751-9640
Container-title:IET Computer Vision
language:en
Short-container-title:IET Computer Vision

Author:

Wang Xiangrong¹²,Zhao Jieyu¹

Affiliation:

1. Information Science and Engineering CollegeNingbo UniversityNo. 818, Fenghua RoadNingboPeople's Republic of China

2. School of Information EngineeringNingbo Dahongying CollegeNingboPeople's Republic of China

Funder

National Natural Science Foundation of China

Natural Science Foundation of Ningbo

Publisher

Institution of Engineering and Technology (IET)

Subject

Computer Vision and Pattern Recognition,Software

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/iet-cvi.2016.0429

Reference39 articles.

1. Boykov Y. Lee V.S. Rusinek H. et al: ‘Segmentation of dynamic n‐d data sets via graph cuts using Markov models’.Medical Image Computing and Computer Assisted Intervention 2001 p.1058

2. Normalized cuts and image segmentation;Shi J.;Comput. Vis. Pattern Recognit.,2000

3. Turek M.W. Freedman D.: ‘Multiscale modeling and constraints for maxow/min‐cut problems in computer vision’ Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'06) 2006 p.180

4. Unsupervised hierarchical image segmentation through fuzzy entropy maximization

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