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Title: A fast method for segmenting images with additive intensity value
Authors: Lau, T.S.
Yip, A.M. 
Keywords: Additive model
Euler's elastica
Image segmentation
Level set methods
Mumford-shah segmentation model
Overlapping objects
Issue Date: 2012
Citation: Lau, T.S., Yip, A.M. (2012). A fast method for segmenting images with additive intensity value. SIAM Journal on Imaging Sciences 5 (3) : 993-1021. ScholarBank@NUS Repository.
Abstract: The soft additive segmentation model attempts to solve the problem related to the segmentation of overlapping objects with additive intensity value. An issue in optimizing the soft additive segmentation functional is that a high-order nonlinear partial differential equation needs to be solved, which, for most standard algorithms, involves high computational cost. In this paper, we propose a fast and efficient numerical algorithm to optimize the soft additive segmentation model. We reformulate the original minimization problem into a sequence of simpler minimization problems that can be solved efficiently by using the augmented Lagrangian method. Numerical tests on real and synthetic cases are presented to demonstrate the efficiency of our algorithm. © 2012 Society for Industrial and Applied Mathematics.
Source Title: SIAM Journal on Imaging Sciences
ISSN: 19364954
DOI: 10.1137/120863617
Appears in Collections:Staff Publications

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