Belief-function based framework for ...
Type de document :
Communication dans un congrès avec actes
Titre :
Belief-function based framework for deformable 3D-shape retrieval
Auteur(s) :
Benhabiles, Halim [Auteur]
Institut de Recherche en Systèmes Electroniques Embarqués [IRSEEM]
Tabia, Hedi [Auteur]
Equipes Traitement de l'Information et Systèmes [ETIS - UMR 8051]
Vandeborre, Jean Philippe [Auteur]
Institut TELECOM/TELECOM Lille1
FOX MIIRE [LIFL]

Institut de Recherche en Systèmes Electroniques Embarqués [IRSEEM]
Tabia, Hedi [Auteur]
Equipes Traitement de l'Information et Systèmes [ETIS - UMR 8051]
Vandeborre, Jean Philippe [Auteur]

Institut TELECOM/TELECOM Lille1
FOX MIIRE [LIFL]
Titre de la manifestation scientifique :
International Conference on Pattern Recognition
Ville :
Stockholm
Pays :
Suède
Date de début de la manifestation scientifique :
2014-08-24
Discipline(s) HAL :
Informatique [cs]/Vision par ordinateur et reconnaissance de formes [cs.CV]
Résumé en anglais : [en]
The need for efficient tools to index and retrieve 3D content becomes even more acute. This paper presents a fully automatic 3D-object retrieval method. It consists of two main steps namely shape signature extraction to ...
Lire la suite >The need for efficient tools to index and retrieve 3D content becomes even more acute. This paper presents a fully automatic 3D-object retrieval method. It consists of two main steps namely shape signature extraction to describe the shape of objects, and similarity computing to compute similarity between objects. In the first step (signature extraction), we use a shape descriptor called geodesic cords. This descriptor can be seen as a probability distribution sampled from a shape function. In the second step (similarity computing), a global distance, based on belief function theory, is computed between each pairwise of descriptors corresponding respectively to an object query and an object from a given database. Experiments on commonly-used benchmarks demonstrate that our method obtains competitive performance compared to 3D-object retrieval methods from the state-of-the-art.Lire moins >
Lire la suite >The need for efficient tools to index and retrieve 3D content becomes even more acute. This paper presents a fully automatic 3D-object retrieval method. It consists of two main steps namely shape signature extraction to describe the shape of objects, and similarity computing to compute similarity between objects. In the first step (signature extraction), we use a shape descriptor called geodesic cords. This descriptor can be seen as a probability distribution sampled from a shape function. In the second step (similarity computing), a global distance, based on belief function theory, is computed between each pairwise of descriptors corresponding respectively to an object query and an object from a given database. Experiments on commonly-used benchmarks demonstrate that our method obtains competitive performance compared to 3D-object retrieval methods from the state-of-the-art.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Collections :
Source :