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A Hybrid Level Set With Semantic Shape Constraint for Object Segmentation
Wang, Bin1; Yuan, Xiuying1; Gao, Xinbo1; Li, Xuelong2; Tao, Dacheng3,4
Department光学影像学习与分析中心
2019-05
Source PublicationIEEE TRANSACTIONS ON CYBERNETICS
ISSN2168-2267;2168-2275
Volume49Issue:5Pages:1558–1569
Contribution Rank2
Abstract

This paper presents a hybrid level set method for object segmentation. The method deconstructs segmentation task into two procedures, i.e., shape transformation and curve evolution, which are alternately optimized until convergence. In this framework, only one shape prior encoded by shape context is utilized to estimate a transformation allowing the curve to have the same semantic expression as shape prior, and curve evolution is driven by an energy functional with topology-preserving and kernelized terms. In such a way, the proposed method is featured by the following advantages: 1) hybrid paradigm makes the level set framework possess the ability of incorporating other shape-related techniques about shape descriptor and distance; 2) shape context endows one single prior with semanticity, and hence leads to the competitive performance compared to the ones with multiple shape priors; and 3) additionally, combining topology-preserving and kernelization mechanisms together contributes to realizing a more reasonable segmentation on textured and noisy images. As far as we know, we propose a hybrid level set framework and utilize shape context to guide curve evolution for the first time. Our method is evaluated with synthetic, healthcare, and natural images, as a result, it shows competitive and even better performance compared to the counterparts.

KeywordActive contour model (ACMs) image segmentation kernelization level set method (LSM) shape context shape prior topology-preserving
DOI10.1109/TCYB.2018.2799999
Indexed BySCI
Language英语
WOS IDWOS:000460667400001
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Document Type期刊论文
Identifierhttp://ir.opt.ac.cn/handle/181661/31329
Collection光学影像学习与分析中心
Corresponding AuthorGao, Xinbo
Affiliation1.Xidian Univ, Sch Elect Engn, Xian 710071, Peoples R China
2.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Xian 710119, Shaanxi, Peoples R China
3.Univ Sydney, UBTECH Sydney Artificial Intelligence Ctr, Darlington, NSW 2008, Australia
4.Univ Sydney, Fac Engn & Informat Technol, Sch Informat Technol, Darlington, NSW 2008, Australia
Recommended Citation
GB/T 7714
Wang, Bin,Yuan, Xiuying,Gao, Xinbo,et al. A Hybrid Level Set With Semantic Shape Constraint for Object Segmentation[J]. IEEE TRANSACTIONS ON CYBERNETICS,2019,49(5):1558–1569.
APA Wang, Bin,Yuan, Xiuying,Gao, Xinbo,Li, Xuelong,&Tao, Dacheng.(2019).A Hybrid Level Set With Semantic Shape Constraint for Object Segmentation.IEEE TRANSACTIONS ON CYBERNETICS,49(5),1558–1569.
MLA Wang, Bin,et al."A Hybrid Level Set With Semantic Shape Constraint for Object Segmentation".IEEE TRANSACTIONS ON CYBERNETICS 49.5(2019):1558–1569.
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