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Credit to Download: 0 | Page Numbers 4 | Abstract Views: 288
Year: 2008
COI code: JR_IJIEPR-19-1_005
Paper Language: English

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  Rahman Farnoosh - Department of Applied Mathematics, Iran University of Science and Technology
  Behnam Zarpak - Department of Applied Mathematics, Iran University of Science and Technology


Stochastic models such as mixture models, graphical models, Markov random fields and hidden Markov models have key role in probabilistic data analysis. In this paper, we used Gaussian mixture model to the pixels of an image. The parameters of the model were estimated by EM-algorithm.In addition pixel labeling corresponded to each pixel of true image was made by Bayes rule. In fact, a new numerically method was introduced for finding the maximum a posterior estimation by using EM-algorithm and Gaussians mixture distribution. In this algorithm, we were made a sequence of priors, posteriors were made and then converged to a posterior probability that is called the reference posterior probability. Maximum a posterior estimated can determine by the reference posterior probability which can make labeled image. This labeled image shows our segmented image with reduced noises. We presented this method in several experiments.


Bayesian Rule, Gaussian Mixture Model (GMM), Maximum a Posterior (MAP), Expectation- Maximization (EM) Algorithm, Reference Analysis

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COI code: JR_IJIEPR-19-1_005

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If you want to refer to this article in your research, you can easily use the following in the resources and references section:
Farnoosh, Rahman & Behnam Zarpak, 2008, IMAGE SEGMENTATION USING GAUSSIAN MIXTURE MODEL, International Journal of Industrial Engineering & Production Research 19 (1), the text, wherever referred to or an achievement of this article is mentioned, after mentioning the article, inside the parental, the following specifications are written.
First Time: (Farnoosh, Rahman & Behnam Zarpak, 2008)
Second and more: (Farnoosh & Zarpak, 2008)
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The University/Research Center Information:
Type: state university
Paper No.: 20366
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