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dc.contributor.authorBae, Egil
dc.contributor.authorLellmann, Jan
dc.contributor.authorTai, Xue-Cheng
dc.date.accessioned2016-02-25T12:58:15Z
dc.date.available2016-02-25T12:58:15Z
dc.date.issued2013
dc.identifier.citationBae E, Lellmann J, Tai X-C (2013) Convex Relaxations for a Generalized Chan-Vese Model. Energy Minimization Methods in Computer Vision and Pattern Recognition: 223–236. Available: http://dx.doi.org/10.1007/978-3-642-40395-8_17.
dc.identifier.issn0302-9743
dc.identifier.issn1611-3349
dc.identifier.doi10.1007/978-3-642-40395-8_17
dc.identifier.urihttp://hdl.handle.net/10754/597879
dc.description.abstractWe revisit the Chan-Vese model of image segmentation with a focus on the encoding with several integer-valued labeling functions. We relate several representations with varying amount of complexity and demonstrate the connection to recent relaxations for product sets and to dual maxflow-based formulations. For some special cases, it can be shown that it is possible to guarantee binary minimizers. While this is not true in general, we show how to derive a convex approximation of the combinatorial problem for more than 4 phases. We also provide a method to avoid overcounting of boundaries in the original Chan-Vese model without departing from the efficient product-set representation. Finally, we derive an algorithm to solve the associated discretized problem, and demonstrate that it allows to obtain good approximations for the segmentation problem with various number of regions. © 2013 Springer-Verlag.
dc.description.sponsorshipThis research has been supported by the Norwegian Re-search Council eVita project 214889, Award No. KUK-I1-007-43, made by KingAbdullah University of Science and Technology (KAUST), EPSRC first grantNo. EP/J009539/1, EPSRC/Isaac Newton Trust Small Grant, and Royal SocietyInternational Exchange Award No. IE110314.
dc.publisherSpringer Science + Business Media
dc.titleConvex Relaxations for a Generalized Chan-Vese Model
dc.typeBook Chapter
dc.identifier.journalEnergy Minimization Methods in Computer Vision and Pattern Recognition
dc.contributor.institutionUniversity of California, Los Angeles, Los Angeles, United States
dc.contributor.institutionUniversity of Cambridge, Cambridge, United Kingdom
dc.contributor.institutionUniversitetet i Bergen, Bergen, Norway
kaust.grant.numberKUK-I1-007-43


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