A new image thresholding method based on Gaussian mixture model

Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/990

Title:	A new image thresholding method based on Gaussian mixture model
Authors:	Huang, ZK Chau, KW
Issue Date:	15-Nov-2008
Source:	Applied mathematics and computation, 15 Nov. 2008, v. 205, no. 2, p. 899-907
Abstract:	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.
Keywords:	Histogram Optimization Thresholding
Publisher:	Elsevier
Journal:	Applied mathematics and computation
ISSN:	0096-3003
EISSN:	1873-5649
DOI:	10.1016/j.amc.2008.05.130
Rights:	Applied Mathematics and Computation © 2008 Published by Elsevier Inc. The journal web site is located at http://www.sciencedirect.com.
Appears in Collections:	Journal/Magazine Article

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Status	open access
File Version	Final Accepted Manuscript

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