Handle URI:
http://hdl.handle.net/10754/597879
Title:
Convex Relaxations for a Generalized Chan-Vese Model
Authors:
Bae, Egil; Lellmann, Jan; Tai, Xue-Cheng
Abstract:
We 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.
Citation:
Bae 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.
Publisher:
Springer Science + Business Media
Journal:
Energy Minimization Methods in Computer Vision and Pattern Recognition
KAUST Grant Number:
KUK-I1-007-43
Issue Date:
2013
DOI:
10.1007/978-3-642-40395-8_17
Type:
Book Chapter
ISSN:
0302-9743; 1611-3349
Sponsors:
This 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.
Appears in Collections:
Publications Acknowledging KAUST Support

Full metadata record

DC FieldValue Language
dc.contributor.authorBae, Egilen
dc.contributor.authorLellmann, Janen
dc.contributor.authorTai, Xue-Chengen
dc.date.accessioned2016-02-25T12:58:15Zen
dc.date.available2016-02-25T12:58:15Zen
dc.date.issued2013en
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.en
dc.identifier.issn0302-9743en
dc.identifier.issn1611-3349en
dc.identifier.doi10.1007/978-3-642-40395-8_17en
dc.identifier.urihttp://hdl.handle.net/10754/597879en
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.en
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.en
dc.publisherSpringer Science + Business Mediaen
dc.titleConvex Relaxations for a Generalized Chan-Vese Modelen
dc.typeBook Chapteren
dc.identifier.journalEnergy Minimization Methods in Computer Vision and Pattern Recognitionen
dc.contributor.institutionUniversity of California, Los Angeles, Los Angeles, United Statesen
dc.contributor.institutionUniversity of Cambridge, Cambridge, United Kingdomen
dc.contributor.institutionUniversitetet i Bergen, Bergen, Norwayen
kaust.grant.numberKUK-I1-007-43en
All Items in KAUST are protected by copyright, with all rights reserved, unless otherwise indicated.