3D-shape retrieval using curves and HMM
Type de document :
Communication dans un congrès avec actes
Titre :
3D-shape retrieval using curves and HMM
Auteur(s) :
Tabia, Hedi [Auteur]
Laboratoire d'Automatique, Génie Informatique et Signal [LAGIS]
Colot, Olivier [Auteur]
LAGIS-SI
Daoudi, Mohamed [Auteur]
FOX MIIRE [LIFL]
Vandeborre, Jean-Philippe [Auteur correspondant]
FOX MIIRE [LIFL]
Laboratoire d'Automatique, Génie Informatique et Signal [LAGIS]
Colot, Olivier [Auteur]
LAGIS-SI
Daoudi, Mohamed [Auteur]
FOX MIIRE [LIFL]
Vandeborre, Jean-Philippe [Auteur correspondant]
FOX MIIRE [LIFL]
Éditeur(s) ou directeur(s) scientifique(s) :
IEEE
Titre de la manifestation scientifique :
20th IEEE International Conference on Pattern Recognition (ICPR 2010)
Ville :
Istanbul
Pays :
Turquie
Date de début de la manifestation scientifique :
2010-08-23
Titre de l’ouvrage :
20th IEEE International Conference on Pattern Recognition (ICPR 2010)
Éditeur :
IEEE
Date de publication :
2010-08
Mot(s)-clé(s) en anglais :
3D-shape retrieval
HMM
Curve analysis
HMM
Curve analysis
Discipline(s) HAL :
Informatique [cs]/Vision par ordinateur et reconnaissance de formes [cs.CV]
Résumé en anglais : [en]
In this paper, we propose a new approach for 3D-shape matching. This approach encloses an off-line step and an on-line step. In the off-line one, an alphabet, of which any shape can be composed, is constructed. First, ...
Lire la suite >In this paper, we propose a new approach for 3D-shape matching. This approach encloses an off-line step and an on-line step. In the off-line one, an alphabet, of which any shape can be composed, is constructed. First, 3D-objects are subdivided into a set of 3D-parts. The subdivision consists to extract from each object a set of feature points with associated curves. Then the whole set of 3D-parts is clustered into different classes from a semantic point of view. After that, each class is modeled by a Hidden Markov Model (HMM). The HMM, which represents a character in the alphabet, is trained using the set of curves corresponding to the class parts. Hence, any 3D-object can be represented by a set of characters. The on-line step consists to compare the set of characters representing the 3D-object query and that of each object in the given dataset. The experimental results obtained on the TOSCA dataset show that the system efficiently performs in retrieving similar 3D-models.Lire moins >
Lire la suite >In this paper, we propose a new approach for 3D-shape matching. This approach encloses an off-line step and an on-line step. In the off-line one, an alphabet, of which any shape can be composed, is constructed. First, 3D-objects are subdivided into a set of 3D-parts. The subdivision consists to extract from each object a set of feature points with associated curves. Then the whole set of 3D-parts is clustered into different classes from a semantic point of view. After that, each class is modeled by a Hidden Markov Model (HMM). The HMM, which represents a character in the alphabet, is trained using the set of curves corresponding to the class parts. Hence, any 3D-object can be represented by a set of characters. The on-line step consists to compare the set of characters representing the 3D-object query and that of each object in the given dataset. The experimental results obtained on the TOSCA dataset show that the system efficiently performs in retrieving similar 3D-models.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Collections :
Source :
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