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    Convex Relaxations for a Generalized Chan-Vese Model

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    Type
    Book Chapter
    Authors
    Bae, Egil
    Lellmann, Jan
    Tai, Xue-Cheng
    KAUST Grant Number
    KUK-I1-007-43
    Date
    2013
    Permanent link to this record
    http://hdl.handle.net/10754/597879
    
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    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.
    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.
    Publisher
    Springer Nature
    Journal
    Energy Minimization Methods in Computer Vision and Pattern Recognition
    DOI
    10.1007/978-3-642-40395-8_17
    ae974a485f413a2113503eed53cd6c53
    10.1007/978-3-642-40395-8_17
    Scopus Count
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