UM
Affiliated with RCfalse
Status已發表Published
An active contour model based on adaptively variable exponent combining Legendre polynomial for image segmentation
Zhu, Jiajie1,2; Fang, Bin1; Zhou, Mingliang1,2; Luo, Futing1,2; Xian, Weizhi1,2; Wang, Gang3
2022-03
Source PublicationMultimedia Tools and Applications
ISSN1380-7501
Abstract

Images with intensity inhomogeneity and blurred boundaries are common in image segmentation tasks, which inevitably result in many difficulties in accurate image segmentation. Massive active contour models (ACMs) have been proposed to solve the problems of intensity inhomogeneity or blurred boundaries respectively. However, there is almost no way to effectively solve the above two problems at the same time, and they are sensitive to the initial contour and noise, or their segmentation speed is relatively slow. In this paper, we propose an active contour model (ACM) based on adaptively variable exponent combining Legendre polynomial (LP) for image segmentation. First, the Legendre polynomial intensity (LPI) is defined, which employs a linear combination of Legendre basis functions for region intensity approximation. Second, an adaptively LPI term is defined, which adopts an adaptively variable exponent function as an acceleration term to drive the curve to quickly evolve to the object boundaries. Third, the distance regularization term is introduced into the active contour as a regularization term to eliminate the need for reinitialization and restrict the behavior of level set function (LSF). Experimental results show that our method offers robustness to gray unevenness, noise and initial curve placement, and adaptability to low contrast and blurred boundaries and outperforms other state-of-the-art algorithms.

KeywordActive Contour Model Image Segmentation Legendre Polynomial Level Set Method
DOI10.1007/s11042-022-12340-1
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Information Systems ; Computer Science, Software Engineering ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS IDWOS:000780464800001
Scopus ID2-s2.0-85127256137
Fulltext Access
Citation statistics
Cited Times [WOS]:0   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
Corresponding AuthorFang, Bin; Zhou, Mingliang
Affiliation1.College of Computer Science, Chongqing University, Chongqing, 400030, China
2.State Key Lab of Internet of Things for Smart City, University of Macau, 999078, Macao
3.School of Computing and Data Engineering, NingboTech University, Ningbo, China
First Author AffilicationUniversity of Macau
Corresponding Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Zhu, Jiajie,Fang, Bin,Zhou, Mingliang,et al. An active contour model based on adaptively variable exponent combining Legendre polynomial for image segmentation[J]. Multimedia Tools and Applications,2022.
APA Zhu, Jiajie,Fang, Bin,Zhou, Mingliang,Luo, Futing,Xian, Weizhi,&Wang, Gang.(2022).An active contour model based on adaptively variable exponent combining Legendre polynomial for image segmentation.Multimedia Tools and Applications.
MLA Zhu, Jiajie,et al."An active contour model based on adaptively variable exponent combining Legendre polynomial for image segmentation".Multimedia Tools and Applications (2022).
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Zhu, Jiajie]'s Articles
[Fang, Bin]'s Articles
[Zhou, Mingliang]'s Articles
Baidu academic
Similar articles in Baidu academic
[Zhu, Jiajie]'s Articles
[Fang, Bin]'s Articles
[Zhou, Mingliang]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Zhu, Jiajie]'s Articles
[Fang, Bin]'s Articles
[Zhou, Mingliang]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.