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dc.contributor.authorRahmat, Roushanak
dc.contributor.authorHarris-Birtill, David
dc.date.accessioned2018-08-29T14:30:06Z
dc.date.available2018-08-29T14:30:06Z
dc.date.issued2018-12-06
dc.identifier255606132
dc.identifiera59daffb-bb7e-4abc-8ff4-0a551188d6b5
dc.identifier85057857524
dc.identifier000451759800010
dc.identifier.citationRahmat , R & Harris-Birtill , D 2018 , ' A comparison of level set models in image segmentation ' , IET Image Processing , vol. 12 , no. 12 , pp. 2212-2221 . https://doi.org/10.1049/iet-ipr.2018.5796en
dc.identifier.issn1751-9659
dc.identifier.otherORCID: /0000-0002-0740-3668/work/47929003
dc.identifier.urihttps://hdl.handle.net/10023/15903
dc.description.abstractImage segmentation is one of the most important tasks in modern imaging applications, which leads to shape reconstruction, volume estimation, object detection and classification. One of the most popular active segmentation models are level set models which are used extensively as an important category of modern image segmentation technique with many different available models to tackle different image applications. Level sets are designed to overcome the topology problems during the evolution of curves in their process of segmentation while the previous algorithms cannot deal with this problem effectively. As a result there is often considerable investigation into the performance of several level set models for a given segmentation problem. It would therefore be helpful to know the characteristics of a range of level set models before applying to a given segmentation problem. In this paper we review a range of level set models and their application to image segmentation work and explain in detail their properties for practical use.
dc.format.extent11
dc.format.extent1430079
dc.language.isoeng
dc.relation.ispartofIET Image Processingen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectT Technologyen
dc.subject3rd-DASen
dc.subject.lccQA75en
dc.subject.lccTen
dc.titleA comparison of level set models in image segmentationen
dc.typeJournal articleen
dc.contributor.institutionUniversity of St Andrews. School of Computer Scienceen
dc.identifier.doi10.1049/iet-ipr.2018.5796
dc.description.statusPeer revieweden


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