international journal of industrial Engineering & Production Research
نشریه بین المللی مهندسی صنایع و تحقیقات تولید
IJIEPR
Engineering & Technology
http://ijiepr.iust.ac.ir
18
agent2
2008-4889
2345-363X
en
jalali
1386
12
1
gregorian
2008
3
1
19
1
online
1
fulltext
en
Image Segmentation using Gaussian Mixture Model
Material Managment
Material Managment
پژوهشي
Research
<p><strong><i><font face="times new roman,times,serif" size="2">Abstract: </font></i></strong><i><font face="times new roman,times,serif" size="2">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. </font></i></p><p align="justify"><font face="times new roman,times,serif" size="2"> </font><i><font face="times new roman,times,serif" size="2"> 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.</font> </i></p>
Keywords : Bayesian Rule, Gaussian Mixture Model (GMM), Maximum a Posterior (MAP), Expectation- Maximization (EM) Algorithm, Reference Analysis
29
32
http://ijiepr.iust.ac.ir/browse.php?a_code=A-10-1-19&slc_lang=en&sid=1
R.
Farnoosh
`18003194753284600314`

18003194753284600314
Yes
B.
Zarpak
zarpak@iust.ac.ir
`18003194753284600315`

18003194753284600315
No