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Journal of Electrical and Computer Engineering
Volume 2017 (2017), Article ID 1735176, 12 pages
https://doi.org/10.1155/2017/1735176
Research Article

Fast Image Segmentation Using Two-Dimensional Otsu Based on Estimation of Distribution Algorithm

1Engineering Research Center of Industrial Computed Tomography Nondestructive Testing of the Education Ministry of China, Chongqing University, Chongqing, China
2College of Mechanical Engineering, Chongqing University, Chongqing, China
3College of Information and Control Engineering, China University of Petroleum (East China), Qingdao, China
4Chongqing Huayu Heavy Machinery & Electrical Co., Ltd., Chongqing, China

Correspondence should be addressed to Liming Duan; moc.361@361gnimilnaud

Received 27 May 2017; Revised 25 July 2017; Accepted 2 August 2017; Published 11 September 2017

Academic Editor: Tongliang Liu

Copyright © 2017 Wuli Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Traditional two-dimensional Otsu algorithm has several drawbacks; that is, the sum of probabilities of target and background is approximate to 1 inaccurately, the details of neighborhood image are not obvious, and the computational cost is high. In order to address these problems, a method of fast image segmentation using two-dimensional Otsu based on estimation of distribution algorithm is proposed. Firstly, in order to enhance the performance of image segmentation, the guided filtering is employed to improve neighborhood image template instead of mean filtering. Additionally, the probabilities of target and background in two-dimensional histogram are exactly calculated to get more accurate threshold. Finally, the trace of the interclass dispersion matrix is taken as the fitness function of estimation of distributed algorithm, and the optimal threshold is obtained by constructing and sampling the probability model. Extensive experimental results demonstrate that our method can effectively preserve details of the target, improve the segmentation precision, and reduce the running time of algorithms.