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Noised image segmentation based on rough set and orthogonal polynomial density model

[+] Author Affiliations
Zhe Liu

Jiangsu University, School of Computer Science and Telecommunication, Zhenjiang, Jiangsu Province 212013, China

Jilin Normal University, School of Computer Science, Siping, Jilin Province 136000, China

Yu-qing Song, Zheng Tang

Jiangsu University, School of Computer Science and Telecommunication, Zhenjiang, Jiangsu Province 212013, China

J. Electron. Imaging. 24(2), 023010 (Mar 10, 2015). doi:10.1117/1.JEI.24.2.023010
History: Received August 19, 2014; Accepted January 30, 2015
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Abstract.  In order to segment a noised image, a method is proposed based on the rough set and orthogonal polynomial density model, in which the nonparametric mixture model can accurately fit the image gray distribution and the rough set can deal with the inaccuracy and uncertainty problems. First, the nonparametric mixture density model is constructed based on the upper and lower approximations of the rough set which can address the problem of over-relying on the prior presumption. Second, the nonparametric expectation-maximization is used to estimate the mixture model parameters. Finally, image pixels are classified according to Bayesian criterion. Experiments on different datasets show that our method is effective in solving the problem of model mismatch, restraining the noise, and preserving the boundary for the noised image segmentation.

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Citation

Zhe Liu ; Yu-qing Song and Zheng Tang
"Noised image segmentation based on rough set and orthogonal polynomial density model", J. Electron. Imaging. 24(2), 023010 (Mar 10, 2015). ; http://dx.doi.org/10.1117/1.JEI.24.2.023010


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