Shape collaborative representation with fuzzy energy based active contour model

NCUIR > College of Electrical Engineering & Computer Science > Department of Electrical Engineering > journal & Dissertation > Item 987654321/108200

Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/108200

Title:	Shape collaborative representation with fuzzy energy based active contour model
Authors:	徐國鎧;Pham, Van-Truong;Tran, Thi-Thao;Shyu, Kuo-Kai;Lin, Chen;Wang, Pa-Chun;Lo, Men-Tzung
Contributors:	資訊電機學院電機工程學系
Keywords:	Fuzzy energy;Image segmentation;Level set method;Shape collaborative representation;Shape prior;Shape sparse representation
Date:	2016-11-01
Issue Date:	2026-04-23 14:38:37 (UTC+8)
Publisher:	Elsevier Ltd.;Elsevier Ltd
Abstract:	摘要： This paper presents a fuzzy energy-based active contour model for image segmentation with shape prior based on collaborative representation of training shapes. In the paper, a fuzzy energy functional including a data term and a shape prior term is proposed. The data term relies on image information to guide the evolution of the contour. Meanwhile, the shape prior term constrains the evolving contour with respect to the priori shape to handle background clutter and object occlusion. Especially, in this study, the prior shape is represented as the combination of atoms in the shape dictionary based on collaborative representation. In particular, instead of using ℓ1-norm regularization as in sparse representation, we utilize ℓ2-regularized linear regression scheme which can obtain algebraic solution for the coding coefficients, and significantly reduces the computation time. The proposed model therefore can segment images with background clutter and object occlusion even when the training set includes shapes with large variation. In addition, the proposed shape collaborative representation model also takes less computational time compared to shape sparse representation approach. Experimental results on various images and comparisons with other models show the desired performances of the proposed model. 出版者： Elsevier Ltd 出版日期： 2016-11-01 出處： Engineering applications of artificial intelligence, 2016-11, Vol.56, p.60-74 版權： 2016 Elsevier Ltd 識別號： ISSN: 0952-1976 識別號： EISSN: 1873-6769 識別號： DOI: 10.1016/j.engappai.2016.08.015
Appears in Collections:	[Department of Electrical Engineering] journal & Dissertation

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