3D-model retrieval using bag-of-features ...
Type de document :
Communication dans un congrès avec actes
Titre :
3D-model retrieval using bag-of-features based on closed curves
Auteur(s) :
El Khoury, Rachid [Auteur]
FOX MIIRE [LIFL]
Vandeborre, Jean-Philippe [Auteur correspondant]
FOX MIIRE [LIFL]
Daoudi, Mohamed [Auteur]
FOX MIIRE [LIFL]
FOX MIIRE [LIFL]
Vandeborre, Jean-Philippe [Auteur correspondant]
FOX MIIRE [LIFL]
Daoudi, Mohamed [Auteur]
FOX MIIRE [LIFL]
Titre de la manifestation scientifique :
Eurographics 2013 Workshop on 3D Object Retrieval
Ville :
Girona
Pays :
Espagne
Date de début de la manifestation scientifique :
2013-05-11
Titre de l’ouvrage :
Eurographics 2013 Workshop on 3D Object Retrieval
Date de publication :
2013-05-11
Mot(s)-clé(s) en anglais :
3D-mesh
recognition
indexing
curves
bag-of-features
recognition
indexing
curves
bag-of-features
Discipline(s) HAL :
Informatique [cs]/Vision par ordinateur et reconnaissance de formes [cs.CV]
Résumé en anglais : [en]
Bag-of-feature technique is a popular approach in areas of computer vision and pattern recognition. Recently, it plays an important role in shape analysis community and especially in 3D-model retrieval. We present our ...
Lire la suite >Bag-of-feature technique is a popular approach in areas of computer vision and pattern recognition. Recently, it plays an important role in shape analysis community and especially in 3D-model retrieval. We present our approach for partial 3D-model retrieval using this technique based on closed curves. We define an invariant scalar function on the surface based on the commute-time distance. Our mapping function respects important properties in order to compute robust closed curves. Each scale of our scalar function detects a small region. The form of these regions are encoded in the form of the closed curves. We generate a collection of closed curves from a source point detected automatically. Based on the collection of all closed curves extracted, we construct our bag-of-features. Then we cluster the bag-of-features in the sense in accurate categorization. The centres of classes are defined as keyshapes. This method is particularly interesting in the sense of quantifying the 3D-model by its keyshapes that are accumulated into an histogram. The results shows the robustness of our method (BOF) compared to a method based on indexed closed curves (ICC) on various 3D-models with different poses.Lire moins >
Lire la suite >Bag-of-feature technique is a popular approach in areas of computer vision and pattern recognition. Recently, it plays an important role in shape analysis community and especially in 3D-model retrieval. We present our approach for partial 3D-model retrieval using this technique based on closed curves. We define an invariant scalar function on the surface based on the commute-time distance. Our mapping function respects important properties in order to compute robust closed curves. Each scale of our scalar function detects a small region. The form of these regions are encoded in the form of the closed curves. We generate a collection of closed curves from a source point detected automatically. Based on the collection of all closed curves extracted, we construct our bag-of-features. Then we cluster the bag-of-features in the sense in accurate categorization. The centres of classes are defined as keyshapes. This method is particularly interesting in the sense of quantifying the 3D-model by its keyshapes that are accumulated into an histogram. The results shows the robustness of our method (BOF) compared to a method based on indexed closed curves (ICC) on various 3D-models with different poses.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Collections :
Source :
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