Show simple item record

dc.contributor.authorTan, Lu
dc.contributor.authorLi, Ling
dc.contributor.authorLiu, Wan-Quan
dc.contributor.authorSun, Jie
dc.contributor.authorZhang, M.
dc.date.accessioned2023-04-16T10:18:49Z
dc.date.available2023-04-16T10:18:49Z
dc.date.issued2020
dc.identifier.citationTan, L. and Li, L. and Liu, W. and Sun, J. and Zhang, M. 2020. A Novel Euler’s Elastica-Based Segmentation Approach for Noisy Images Using the Progressive Hedging Algorithm. Journal of Mathematical Imaging and Vision. 62 (1): pp. 98-119.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/91437
dc.identifier.doi10.1007/s10851-019-00920-0
dc.description.abstract

Euler’s elastica-based unsupervised segmentation models have strong capability of completing the missing boundaries for existing objects in a clean image, but they are not working well for noisy images. This paper aims to establish a Euler’s elastica-based approach that can properly deal with the random noises to improve the segmentation performance for noisy images. The corresponding formulation of stochastic optimization is solved via the progressive hedging algorithm (PHA), and the description of each individual scenario is obtained by the alternating direction method of multipliers. Technically, all the sub-problems derived from the framework of PHA can be solved by using the curvature-weighted approach and the convex relaxation method. Then, an alternating optimization strategy is applied by using some powerful accelerating techniques including the fast Fourier transform and generalized soft threshold formulas. Extensive experiments have been conducted on both synthetic and real images, which displayed significant gains of the proposed segmentation models and demonstrated the advantages of the developed algorithms.

dc.languageEnglish
dc.publisherSPRINGER
dc.subjectScience & Technology
dc.subjectTechnology
dc.subjectPhysical Sciences
dc.subjectComputer Science, Artificial Intelligence
dc.subjectComputer Science, Software Engineering
dc.subjectMathematics, Applied
dc.subjectComputer Science
dc.subjectMathematics
dc.subjectEuler's elastic energy
dc.subjectStochastic noises
dc.subjectProgressive hedging algorithm (PHA )
dc.subjectAlternating direction method of multipliers (ADMM)
dc.subjectCurvature-weighted approach
dc.subjectACTIVE CONTOURS
dc.subjectFRAMEWORK
dc.titleA Novel Euler’s Elastica-Based Segmentation Approach for Noisy Images Using the Progressive Hedging Algorithm
dc.typeJournal Article
dcterms.source.volume62
dcterms.source.number1
dcterms.source.startPage98
dcterms.source.endPage119
dcterms.source.issn0924-9907
dcterms.source.titleJournal of Mathematical Imaging and Vision
dc.date.updated2023-04-16T10:18:46Z
curtin.departmentSchool of Elec Eng, Comp and Math Sci (EECMS)
curtin.accessStatusOpen access
curtin.facultyFaculty of Science and Engineering
curtin.contributor.orcidLiu, Wan-Quan [0000-0003-4910-353X]
curtin.contributor.orcidSun, Jie [0000-0001-5611-1672]
curtin.contributor.orcidTan, Lu [0000-0002-3361-3060]
curtin.contributor.researcheridSun, Jie [B-7926-2016] [G-3522-2010]
dcterms.source.eissn1573-7683
curtin.contributor.scopusauthoridLi, Ling [55636319553] [55636319554] [56697627700]
curtin.contributor.scopusauthoridLiu, Wan-Quan [56510481200] [7407343628]
curtin.contributor.scopusauthoridSun, Jie [16312754600] [57190212842]
curtin.repositoryagreementV3


Files in this item

Thumbnail
Thumbnail

This item appears in the following Collection(s)

Show simple item record