A new image thresholding method based on Gaussian mixture model
A new image thresholding method based on Gaussian mixture model
复制标题
一种基于高斯混合模型的图像阈值处理新方法
DOI：
10.1016/j.amc.2008.05.130
复制
发表时间：
2008-11-15
影响因子：
4
通讯作者：
Chau, Kwok-Wing
中科院分区：
文献类型：
--
作者：
Huang, Zhi-Kai;Chau, Kwok-Wing
本文提出了一种基于高斯混合模型的图像全局阈值搜索方法。首先，将图像的灰度直方图表示为灰度频率的函数。然后，高斯混合的图像直方图拟合，期望最大化（EM）算法被开发来估计高斯混合的直方图和相应的参数化的数量。最后，选择最佳阈值，即这些高斯混合均值的平均值。实验结果表明，该算法具有较好的性能. (C)2008年由Elsevier Inc.出版
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.