Translator Disclaimer
8 February 2005 Iterative relaxation algorithm for noisy jacquard image segmentation
Author Affiliations +
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.
© (2005) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Zhilin Feng, Jianwei Yin, Lingwu Wang, Gang Chen, and Jinxiang Dong "Iterative relaxation algorithm for noisy jacquard image segmentation", Proc. SPIE 5637, Electronic Imaging and Multimedia Technology IV, (8 February 2005);


Back to Top