Fuzzy aura matrices for texture classification
Type de document :
Compte-rendu et recension critique d'ouvrage
Titre :
Fuzzy aura matrices for texture classification
Auteur(s) :
Hammouche, Kamal [Auteur]
Chercheur indépendant
Losson, Olivier [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Macaire, Ludovic [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Chercheur indépendant
Losson, Olivier [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Macaire, Ludovic [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Titre de la revue :
Pattern Recognition
Pagination :
212-228
Éditeur :
Elsevier
Date de publication :
2016-05-01
ISSN :
0031-3203
Mot(s)-clé(s) en anglais :
Fuzzy aura matrix
Fuzzy aura set
Spatially-variant neighborhood
Texture classification
Fuzzy aura set
Spatially-variant neighborhood
Texture classification
Discipline(s) HAL :
Informatique [cs]
Résumé en anglais : [en]
The aura concept has been developed from the set theory and is an efficient tool to characterize texture images. It is based on the notion of “aura set” and on the associated “aura measure” that involve the neighborhood ...
Lire la suite >The aura concept has been developed from the set theory and is an efficient tool to characterize texture images. It is based on the notion of “aura set” and on the associated “aura measure” that involve the neighborhood of each image pixel. In this paper, we propose to extend this concept to the framework of fuzzy sets in order to take the imprecise nature of images into account. We define the notions of fuzzy aura sets and of aura measures to compute fuzzy aura matrices as texture descriptors. Fuzzy aura measures assume no restrictions about the neighborhood shape, size, and spatial invariance. Extensive tests of texture classification on Outex benchmark datasets show that fuzzy aura matrices computed with spatially-variant neighborhoods oft en outperform other power ful texture descriptors on both gray-level and color imagesLire moins >
Lire la suite >The aura concept has been developed from the set theory and is an efficient tool to characterize texture images. It is based on the notion of “aura set” and on the associated “aura measure” that involve the neighborhood of each image pixel. In this paper, we propose to extend this concept to the framework of fuzzy sets in order to take the imprecise nature of images into account. We define the notions of fuzzy aura sets and of aura measures to compute fuzzy aura matrices as texture descriptors. Fuzzy aura measures assume no restrictions about the neighborhood shape, size, and spatial invariance. Extensive tests of texture classification on Outex benchmark datasets show that fuzzy aura matrices computed with spatially-variant neighborhoods oft en outperform other power ful texture descriptors on both gray-level and color imagesLire moins >
Langue :
Anglais
Vulgarisation :
Non
Projet ANR :
Collections :
Source :
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