Belief-function based framework for ...
Document type :
Communication dans un congrès avec actes
Title :
Belief-function based framework for deformable 3D-shape retrieval
Author(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]
Conference title :
International Conference on Pattern Recognition
City :
Stockholm
Country :
Suède
Start date of the conference :
2014-08-24
HAL domain(s) :
Informatique [cs]/Vision par ordinateur et reconnaissance de formes [cs.CV]
English abstract : [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 ...
Show more >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.Show less >
Show more >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.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
Collections :
Source :