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Segmentation d'Images Texturées Couleur à l'aide de modèles paramétriques pour approcher la distribution des erreurs de prédiction linéaires

Abstract : We propose novel a priori parametric models to approximate the distribution of the two dimensional multichannel linear prediction error in order to improve the performance of color texture segmentation algorithms. Two dimensional linear prediction models are used to characterize the spatial structures in color images. The multivariate linear prediction error of these texture models is approximated with Wishart distribution and multivariate Gaussian mixture models. A novel color texture segmentation framework based on these models and a spatial regularization model of initial class label fields is presented. For the proposed method and with different color spaces, experimental results show better performances in terms of percentage segmentation error, in comparison with the use of a multivariate Gaussian law.
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https://hal.archives-ouvertes.fr/hal-00717372
Contributor : Jean-Christophe Burie Connect in order to contact the contributor
Submitted on : Thursday, July 21, 2016 - 2:28:00 PM
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Imtnan-Ul-Haque Qazi, Olivier Alata, Jean-Christophe Burie, Ahmed Moussa, Christine Fernandez-Maloigne. Segmentation d'Images Texturées Couleur à l'aide de modèles paramétriques pour approcher la distribution des erreurs de prédiction linéaires. Revue Africaine de la Recherche en Informatique et Mathématiques Appliquées, INRIA, 2011, Volume 14 - 2011 - Special issue CARI'10, pp.31-43. ⟨10.46298/arima.1942⟩. ⟨hal-00717372⟩

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