A parts-based approach for automatic 3D shape categorization using belief functions
| Autor: | Jean-Philippe Vandeborre, Mohamed Daoudi, Olivier Colot, Hedi Tabia |
|---|---|
| Přispěvatelé: | LAGIS-SI, Laboratoire d'Automatique, Génie Informatique et Signal (LAGIS), Université de Lille, Sciences et Technologies-Centrale Lille-Centre National de la Recherche Scientifique (CNRS)-Université de Lille, Sciences et Technologies-Centrale Lille-Centre National de la Recherche Scientifique (CNRS), FOX MIIRE (LIFL), Laboratoire d'Informatique Fondamentale de Lille (LIFL), Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS)-Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS), Institut TELECOM/TELECOM Lille1, Institut Mines-Télécom [Paris] (IMT), Vandeborre, Jean-Philippe |
| Rok vydání: | 2013 |
| Předmět: |
Computer science
business.industry Cognitive neuroscience of visual object recognition [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV] 020207 software engineering 3d model Pattern recognition 02 engineering and technology Two stages Theoretical Computer Science Set (abstract data type) [INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV] Categorization Artificial Intelligence 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence 10. No inequality business |
| Zdroj: | ACM Transactions on Intelligent Systems and Technology ACM Transactions on Intelligent Systems and Technology, ACM, 2013, 4 (2), pp.33:1-33:16 ACM Transactions on Intelligent Systems and Technology, 2013, 4 (2), pp.33:1-33:16 |
| ISSN: | 2157-6912 2157-6904 |
| DOI: | 10.1145/2438653.2438668 |
| Popis: | International audience; Grouping 3D-objects into (semantically) meaningful categories is a challenging and important problem in 3D-mining and shape processing. Here, we present a novel approach to categorize 3D-objects. The method described in this paper, is a belief function based approach and consists of two stages. The training stage, where 3D-objects in the same category are processed and a set of representative parts is constructed, and the labeling stage, where unknown objects are categorized. The experimental results obtained on the Tosca- Sumner and the Shrec07 datasets show that the system efficiently performs in categorizing 3D-models. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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