A new image thresholding method based on Gaussian mixture model

被引：148
|
作者：
Huang, Zhi-Kai [1 ,3 ]
Chau, Kwok-Wing [2 ]
机构：
[1] Chinese Acad Sci, Hefei Inst Intelligent Machines, Intelligent Comp Lab, POB 1130, Hefei 230031, Anhui, Peoples R China
[2] Hong Kong Polytech Univ, Dept Civil & Struct Engn, Hong Kong, Hong Kong, Peoples R China
[3] Nanchang Inst Technol, Dept Machinery & Dynam Engn, Nanchang 330099, Jiangxi, Peoples R China
来源：
APPLIED MATHEMATICS AND COMPUTATION | 2008年 / 205卷 / 02期
基金：
中国国家自然科学基金;
关键词：
Histogram; Optimization; Thresholding;
D O I：
10.1016/j.amc.2008.05.130
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
In this paper, an efficient approach to search for the global threshold of image using Gaussian mixture model is proposed. Firstly, a gray-level histogram of an image is represented as a function of the frequencies of gray-level. Then to fit the Gaussian mixtures to the histogram of image, the expectation maximization (EM) algorithm is developed to estimate the number of Gaussian mixture of such histograms and their corresponding parameterization. Finally, the optimal threshold which is the average of these Gaussian mixture means is chosen. And the experimental results show that the new algorithm performs better. (C) 2008 Published by Elsevier Inc.
引用
收藏
页码：899 / 907
页数：9
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