The Chan-Vese Model With Elastica and Landmark Constraints for Image Segmentation

In order to completely separate objects with large sections of occluded boundaries in an image, we devise a new variational level set model for image segmentation combining the Chan-Vese model with elastica and landmark constraints. For computational efficiency, we design its Augmented Lagrangian Me...

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Autores principales:	Jintao Song, Huizhu Pan, Wanquan Liu, Zisen Xu, Zhenkuan Pan
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Image segmentation Chan-Vese model elastica landmarks variational level set method ADMM method Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/9f4a4ab0970d4d31b603782a93b0536e
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id	oai:doaj.org-article:9f4a4ab0970d4d31b603782a93b0536e
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spelling	oai:doaj.org-article:9f4a4ab0970d4d31b603782a93b0536e2021-11-19T00:05:33ZThe Chan-Vese Model With Elastica and Landmark Constraints for Image Segmentation2169-353610.1109/ACCESS.2020.3047848https://doaj.org/article/9f4a4ab0970d4d31b603782a93b0536e2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9309289/https://doaj.org/toc/2169-3536In order to completely separate objects with large sections of occluded boundaries in an image, we devise a new variational level set model for image segmentation combining the Chan-Vese model with elastica and landmark constraints. For computational efficiency, we design its Augmented Lagrangian Method (ALM) or Alternating Direction Method of Multiplier (ADMM) method by introducing some auxiliary variables, Lagrange multipliers, and penalty parameters. In each loop of alternating iterative optimization, the sub-problems of minimization can be easily solved via the Gauss-Seidel iterative method and generalized soft thresholding formulas with projection, respectively. Numerical experiments show that the proposed model can not only recover larger broken boundaries but can also improve segmentation efficiency, as well as decrease the dependence of segmentation on parameter tuning and initialization.Jintao SongHuizhu PanWanquan LiuZisen XuZhenkuan PanIEEEarticleImage segmentationChan-Vese modelelasticalandmarksvariational level set methodADMM methodElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 3508-3516 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Image segmentation Chan-Vese model elastica landmarks variational level set method ADMM method Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Image segmentation Chan-Vese model elastica landmarks variational level set method ADMM method Electrical engineering. Electronics. Nuclear engineering TK1-9971 Jintao Song Huizhu Pan Wanquan Liu Zisen Xu Zhenkuan Pan The Chan-Vese Model With Elastica and Landmark Constraints for Image Segmentation
description	In order to completely separate objects with large sections of occluded boundaries in an image, we devise a new variational level set model for image segmentation combining the Chan-Vese model with elastica and landmark constraints. For computational efficiency, we design its Augmented Lagrangian Method (ALM) or Alternating Direction Method of Multiplier (ADMM) method by introducing some auxiliary variables, Lagrange multipliers, and penalty parameters. In each loop of alternating iterative optimization, the sub-problems of minimization can be easily solved via the Gauss-Seidel iterative method and generalized soft thresholding formulas with projection, respectively. Numerical experiments show that the proposed model can not only recover larger broken boundaries but can also improve segmentation efficiency, as well as decrease the dependence of segmentation on parameter tuning and initialization.
format	article
author	Jintao Song Huizhu Pan Wanquan Liu Zisen Xu Zhenkuan Pan
author_facet	Jintao Song Huizhu Pan Wanquan Liu Zisen Xu Zhenkuan Pan
author_sort	Jintao Song
title	The Chan-Vese Model With Elastica and Landmark Constraints for Image Segmentation
title_short	The Chan-Vese Model With Elastica and Landmark Constraints for Image Segmentation
title_full	The Chan-Vese Model With Elastica and Landmark Constraints for Image Segmentation
title_fullStr	The Chan-Vese Model With Elastica and Landmark Constraints for Image Segmentation
title_full_unstemmed	The Chan-Vese Model With Elastica and Landmark Constraints for Image Segmentation
title_sort	chan-vese model with elastica and landmark constraints for image segmentation
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/9f4a4ab0970d4d31b603782a93b0536e
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The Chan-Vese Model With Elastica and Landmark Constraints for Image Segmentation

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