http://www.cnr.it/ontology/cnr/individuo/prodotto/ID77638
Automatic Recognition of Hand Gestures with Differential Evolution (Contributo in atti di convegno)
- Type
- Label
- Automatic Recognition of Hand Gestures with Differential Evolution (Contributo in atti di convegno) (literal)
- Anno
- 2008-01-01T00:00:00+01:00 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
- 10.1007/978-3-540-68111-3_105 (literal)
- Alternative label
De Falco Ivanoe, Della Cioppa Antonio, Maisto Domenico, Scafuri Umberto, Tarantino Ernesto (2008)
Automatic Recognition of Hand Gestures with Differential Evolution
in EvoWorkshops 2008, Napoli, Italia, 26-28 Marzo 2008
(literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
- De Falco Ivanoe, Della Cioppa Antonio, Maisto Domenico, Scafuri Umberto, Tarantino Ernesto (literal)
- Pagina inizio
- Pagina fine
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#titoloVolume
- Applications of Evolutionary Computing, Evoworkshops 2008 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#volumeInCollana
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#note
- Lecture Notes in Computer Science vol. 4974, Springer, 2008
(literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#pagineTotali
- Note
- Scopu (literal)
- ISI Web of Science (WOS) (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
- I. De Falco1, A. Della Cioppa2, D. Maisto1, U. Scafuri1, and E. Tarantino1
1 ICAR-CNR, Via P. Castellino 111, 80131 Naples, Italy
2 DIIIE Lab, University of Salerno,
Via Ponte don Melillo 1, 84084 Fisciano (SA), Italy (literal)
- Titolo
- Automatic Recognition of Hand Gestures with Differential Evolution (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn
- 978-3-540-78760-0 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#curatoriVolume
- Mario Giacobini et alii (literal)
- Abstract
- Automatic recognition of hand gestures is a crucial step in facing human-computer interaction. Differential Evolution is used to perform automatic classification of hand gestures in a thirteen-class database. Performance of the resulting best individual is computed in terms of error rate on the testing set, and is compared against those of other ten classification techniques well known in literature. Results show the effectiveness and the efficiency of the approach in solving the classification task. Furthermore, the implemented tool allows to extract the most significant parameters for differentiating the collected gestures. (literal)
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