dc.contributor.authorGu, Ying
dc.contributor.authorWang, Li-Lian
dc.contributor.authorTai, Xue Cheng
dc.date.accessioned2013-09-16T07:02:25Z
dc.date.available2013-09-16T07:02:25Z
dc.date.copyright2012en_US
dc.date.issued2012
dc.identifier.citationGu, Y., Wang, L.-L., & Tai, X.-C. (2012). A Direct Approach Toward Global Minimization for Multiphase Labeling and Segmentation Problems. IEEE Transactions on Image Processing, 21(5), 2399-2411.
dc.identifier.issn1057-7149en_US
dc.identifier.urihttp://hdl.handle.net/10220/13483
dc.description.abstractThis paper intends to extend the minimization algorithm developed by Bae, Yuan and Tai [IJCV, 2011] in several directions. First, we propose a new primal-dual approach for global minimization of the continuous Potts model with applications to the piecewise constant Mumford-Shah model for multiphase image segmentation. Different from the existing methods, we work directly with the binary setting without using convex relaxation, which is thereby termed as a direct approach. Second, we provide the sufficient and necessary conditions to guarantee a global optimum. Moreover, we provide efficient algorithms based on a reduction in the intermediate unknowns from the augmented Lagrangian formulation. As a result, the underlying algorithms involve significantly fewer parameters and unknowns than the naive use of augmented Lagrangian-based methods; hence, they are fast and easy to implement. Furthermore, they can produce global optimums under mild conditions.en_US
dc.language.isoenen_US
dc.relation.ispartofseriesIEEE transactions on image processingen_US
dc.rights© 2012 IEEEen_US
dc.titleA direct approach toward global minimization for multiphase labeling and segmentation problemsen_US
dc.typeJournal Article
dc.contributor.schoolSchool of Physical and Mathematical Sciencesen_US
dc.identifier.doihttp://dx.doi.org/10.1109/TIP.2011.2182522


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