Share Email Print
cover

Journal of Electronic Imaging

Noised image segmentation based on rough set and orthogonal polynomial density model
Author(s): Zhe Liu; Yu-qing Song; Zheng Tang
Format Member Price Non-Member Price
PDF $20.00 $25.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper 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.

Paper Details

Date Published: 10 March 2015
PDF: 7 pages
J. Electron. Imaging. 24(2) 023010 doi: 10.1117/1.JEI.24.2.023010
Published in: Journal of Electronic Imaging Volume 24, Issue 2
Show Author Affiliations
Zhe Liu, Jiangsu Univ. (China)
Jilin Normal Univ. (China)
Yu-qing Song, Jiangsu Univ. (China)
Zheng Tang, Jiangsu Univ. (China)


© SPIE. Terms of Use
Back to Top