| Paper: | MLSP-P4.7 | ||
| Session: | Machine Learning Applications | ||
| Time: | Thursday, May 20, 09:30 - 11:30 | ||
| Presentation: | Poster | ||
| Topic: | Machine Learning for Signal Processing: Learning Theory and Modeling | ||
| Title: | UNSUPERVISED IMAGE SEGMENTATION BASED ON A NEW FUZZY HMC MODEL | ||
| Authors: | Cyril Carincotte; GSM, Inst. Fresnel (CNRS - UMR 6133) | ||
| Stéphane Derrode; GSM, Inst. Fresnel (CNRS - UMR 6133) | |||
| Guillaume Sicot; GET / ENST Bretagne FRE CNRS 2658 TAMCIC | |||
| Jean-Marc Boucher; GET / ENST Bretagne FRE CNRS 2658 TAMCIC | |||
| Abstract: | In this paper, we propose a technique, based on a fuzzy Hidden Markov Chain (HMC) model, for the unsupervised segmentation of images. The main contribution of this work is to simultaneously use Dirac and Lebesgue measures at the class chain level. This model allows the coexistence of hard and fuzzy pixels in the same picture. In this way, the fuzzy approach enriches the classical model by adding a fuzzy class, which has several interpretations in signal processing. One such interpretation in image segmentation is the simultaneous appearance of several thematic classes on the same pixel. Model parameter estimation is performed through an extension of the Iterative Conditional Estimation (ICE) algorithm to take into account the fuzzy part. The fuzzy segmentation of a real image of clouds is studied and compared to the classification obtained with a ``classical'' hard HMC model. | ||
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