Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/98959
Title: A direct approach toward global minimization for multiphase labeling and segmentation problems
Authors: Tai, Xue Cheng
Gu, Ying
Wang, Li-Lian
Issue Date: 2012
Source: Gu, 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.
Series/Report no.: IEEE transactions on image processing
Abstract: This 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.
URI: https://hdl.handle.net/10356/98959
http://hdl.handle.net/10220/13483
ISSN: 1057-7149
DOI: 10.1109/TIP.2011.2182522
Rights: © 2012 IEEE
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SPMS Journal Articles

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