Extension of higher-order HMC modeling with application to image segmentation
| Autor: | Lamia Benyoussef, Stéphane Derrode, Cyril Carincotte |
|---|---|
| Přispěvatelé: | Institut FRESNEL (FRESNEL), Centre National de la Recherche Scientifique (CNRS)-École Centrale de Marseille (ECM)-Aix Marseille Université (AMU), Multitel Asbl, Entreprise privée, Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS) |
| Rok vydání: | 2008 |
| Předmět: |
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Scale-space segmentation 02 engineering and technology Markov model Artificial Intelligence 0202 electrical engineering electronic engineering information engineering Electrical and Electronic Engineering Hidden Markov model Mathematics Markov chain business.industry Applied Mathematics Maximum-entropy Markov model Variable-order Markov model 020206 networking & telecommunications Pattern recognition Image segmentation Computational Theory and Mathematics [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR] Signal Processing 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition Artificial intelligence Forward algorithm Statistics Probability and Uncertainty business |
| Zdroj: | Digital Signal Processing Digital Signal Processing, Elsevier, 2008, 18 (5), pp.849-860. ⟨10.1016/J.dsp.2007.10.010⟩ Digital Signal Processing, 2008, 18 (5), pp.849-860. ⟨10.1016/J.dsp.2007.10.010⟩ |
| ISSN: | 1051-2004 1095-4333 |
| Popis: | In this work, we propose to improve the neighboring relationship ability of the hidden Markov chain (HMC) model, by extending the memory lengths of both the Markov chain process and the data-driven densities arising in the model. The new model is able to learn more complex noise structures, with respect to the configuration of several previous states and observations. Model parameters estimation is performed from an extension of the general iterative conditional estimation (ICE) method to take into account memories, which makes the classification algorithm unsupervised. The higher-order HMC model is then evaluated in the image segmentation context. A comparative study conducted on a simulated image is carried out according to the order of the chain. Experimental results on a synthetic aperture radar (SAR) image show that higher-order model can provide more homogeneous segmentations than the classical model, but to the cost of higher memory and computing time requirements. |
| Databáze: | OpenAIRE |
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