A Markov Random Field and Adaptive Regularization Embedded Level Set Segmentation Model Solving by Graph Cuts
Author
Source
Journal of Electrical and Computer Engineering
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-01-14
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Information Technology and Computer Science
Abstract EN
This paper presents a novel Markov random field (MRF) and adaptive regularization embedded level set model for robust image segmentation and uses graph cuts optimization to numerically solve it.
Firstly, a special MRF-based energy term in the form of level set formulation is constructed for strong local neighborhood modeling.
Secondly, a regularization constraint with adaptive properties is imposed onto the proposed model with the following purposes: reduce the influence of noise, force the power exponent of the regularization process to change adaptively with image coordinates, and ensure the active contour does not pass through the weak object boundaries.
Thirdly, graph cuts optimization is used to implement the numerical solution of the proposed model to obtain extremely fast convergence performance.
The extensive and promising experimental results on wide variety of images demonstrate the excellent performance of the proposed method in both segmentation accuracy and convergence rate.
American Psychological Association (APA)
Wang, Dengwei. 2019. A Markov Random Field and Adaptive Regularization Embedded Level Set Segmentation Model Solving by Graph Cuts. Journal of Electrical and Computer Engineering،Vol. 2019, no. 2019, pp.1-13.
https://search.emarefa.net/detail/BIM-1173839
Modern Language Association (MLA)
Wang, Dengwei. A Markov Random Field and Adaptive Regularization Embedded Level Set Segmentation Model Solving by Graph Cuts. Journal of Electrical and Computer Engineering No. 2019 (2019), pp.1-13.
https://search.emarefa.net/detail/BIM-1173839
American Medical Association (AMA)
Wang, Dengwei. A Markov Random Field and Adaptive Regularization Embedded Level Set Segmentation Model Solving by Graph Cuts. Journal of Electrical and Computer Engineering. 2019. Vol. 2019, no. 2019, pp.1-13.
https://search.emarefa.net/detail/BIM-1173839
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1173839