http://www.cnr.it/ontology/cnr/individuo/prodotto/ID182397
A comparison Framework for 3D Object Classification Methods (Contributo in atti di convegno)
- Type
- Label
- A comparison Framework for 3D Object Classification Methods (Contributo in atti di convegno) (literal)
- Anno
- 2006-01-01T00:00:00+01:00 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
- 10.1007/11848035_42 (literal)
- Alternative label
S. Biasotti; D. Giorgi; S. Marini; M. Spagnuolo; B. Falcidieno (2006)
A comparison Framework for 3D Object Classification Methods
in International Workshop on Multimedia Content Representation, Classification and Security (MRCS 2006), Istanbul, 11-13 September 2006
(literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
- S. Biasotti; D. Giorgi; S. Marini; M. Spagnuolo; B. Falcidieno (literal)
- Pagina inizio
- Pagina fine
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
- http://www.springerlink.com/content/3g5888562724m68l/ (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#titoloVolume
- MULTIMEDIA CONTENT REPRESENTATION, CLASSIFICATION AND SECURITY (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#volumeInCollana
- Note
- ISI Web of Science (WOS) (literal)
- Scopu (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
- CNR, IMATI, Genoa, Italy (literal)
- Titolo
- A comparison Framework for 3D Object Classification Methods (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#curatoriVolume
- B. Gunsel; AK. Jain; AM Tekalp; B. Sankur (literal)
- Abstract
- 3D shape classification plays an important role in the process of organizing and retrieving models in large databases. Classifying shapes
means to assign a query model to the most appropriate class of objects: knowledge about the membership of models to classes can be very useful to speed up and improve the shape retrieval process, by allowing the reduction of the candidate models to compare with the query.
The main contribution of this paper is the setting of a framework to compare the effectiveness of different query-to-class membership measures, defined independently of specific shape descriptors. The classification performances are evaluated against a set of popular 3D shape descriptors, using a dataset consisting of 14 classes made up of 20 objects each. (literal)
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