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
و موضوعات مربوط
Other Related Subject
پژوهشي
Research
<p> <a name="OLE_LINK2"></a><a name="OLE_LINK1"> 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 have learned Gaussian mixture model to the pixels of an image. The parameters of the model have estimated by EM-algorithm. </a></p><p> In addition pixel labeling corresponded to each pixel of true image is made by Bayes rule. In fact, we introduce a new numerically method of finding maximum a posterior estimation by using EM-algorithm and Gaussians mixture distribution. In this algorithm, we have made a sequence of priors, posteriors and they converge to a posterior probability that is called the reference posterior probability. Maximum a posterior estimated can determine by the reference posterior probability that will make labeled image. This labeled image shows our segmented image with reduced noises. We show this method in several experiments. </p>
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-10-1-110&slc_lang=en&sid=1
Rahman
Farnoosh
`18003194753284600596`

18003194753284600596
Yes
Behnam
Zarpak
zarpak@iust.ac.ir
`18003194753284600665`

18003194753284600665
No