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

Zhe Liu; Yu-qing Song; Zheng Tang

doi:10.1117/1.JEI.24.2.023010

10 March 2015 Noised image segmentation based on rough set and orthogonal polynomial density model

Zhe Liu, Yu-qing Song, Zheng Tang

Author Affiliations +

Journal of Electronic Imaging, Vol. 24, Issue 2, 023010 (March 2015). https://doi.org/10.1117/1.JEI.24.2.023010

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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Zhe Liu, Yu-qing Song, and Zheng Tang "Noised image segmentation based on rough set and orthogonal polynomial density model," Journal of Electronic Imaging 24(2), 023010 (10 March 2015). https://doi.org/10.1117/1.JEI.24.2.023010

Published: 10 March 2015

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