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Proceedings Paper

Iterative relaxation algorithm for noisy jacquard image segmentation
Author(s): Zhilin Feng; Jianwei Yin; Lingwu Wang; Gang Chen; Jinxiang Dong
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Paper Abstract

The Mumford-Shah model has been well acknowledged as an important method for image segmentation. This paper discussed the problem of simultaneous image segmentation and smoothing by approaching the Mumford-Shah paradigm from a numerical approximation perspective. In particular, a novel iterative relaxation algorithm for the numerical solving of the Mumford-Shah model was proposed. First, the paper presented mathematically the existence of a solution in the weak formulation of GSBV space. Second, some approximations and numerical methods for computing the weak solution were discussed. Finally, a minimization method based on a quasi-Newton algorithm was put forward. The proposed algorithm found accurately the absolute minimum of the functional at each iteration. Considering the important role of a discrete finite element approximation method in the sense of Γ-convergence, an adjustment scheme for adaptive triangulation was applied to improve the efficiency of iteration. Experimental results on noisy synthetic and jacquard images demonstrate the efficacy of the proposed algorithm.

Paper Details

Date Published: 8 February 2005
PDF: 9 pages
Proc. SPIE 5637, Electronic Imaging and Multimedia Technology IV, (8 February 2005); doi: 10.1117/12.581249
Show Author Affiliations
Zhilin Feng, Zhejiang Univ. of Technology (China)
Zhejiang Univ. (China)
Jianwei Yin, Zhejiang Univ. (China)
Lingwu Wang, Zhejiang Univ. (China)
Gang Chen, Zhejiang Univ. (China)
Jinxiang Dong, Zhejiang Univ. (China)

Published in SPIE Proceedings Vol. 5637:
Electronic Imaging and Multimedia Technology IV
Chung-Sheng Li; Minerva M. Yeung, Editor(s)

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