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Publications about parameterized joint density
Result of the query in the list of publications :
Conference article |
1 - Hidden fuzzy Markov chain model with K discrete classes.
A. Gamal Eldin and Fabien Salzenstein and Christophe Collet. In Information Sciences Signal Processing and their Applications (ISSPA), May 2010.
Keywords : hidden fuzzy Markov chain, multispectral image segmentation, parameterized joint density.
@INPROCEEDINGS{fuzzy_segmentation10,
|
| author |
= |
{Gamal Eldin, A. and Salzenstein, Fabien and Collet, Christophe}, |
| title |
= |
{Hidden fuzzy Markov chain model with K discrete classes}, |
| year |
= |
{2010}, |
| month |
= |
{May}, |
| booktitle |
= |
{Information Sciences Signal Processing and their Applications (ISSPA)}, |
| url |
= |
{http://hal.inria.fr/hal-00616372}, |
| keyword |
= |
{hidden fuzzy Markov chain, multispectral image segmentation, parameterized joint density} |
| } |
Abstract :
This paper deals with a new unsupervised fuzzy Bayesian segmentation method based on the hidden Markov chain model, in order to separate continuous from discrete components in the hidden data. We present a new F-HMC (fuzzy hidden Markov chain) related to three hard classes, based on a general extension of the previously algorithms proposed. For a given observation, the hidden variable owns a density according to a measure containing Dirac and Lebesgue components. We have performed our approach in the multispectral context. The hyper-parameters are estimated using a Stochastic Expectation Maximization (SEM) algorithm. We present synthetic simulations and also segmentation results related to real multi-band data. |
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