MLP Neural Network Implementation on a SIMD Architecture (Articolo in rivista)

Type
Label
  • MLP Neural Network Implementation on a SIMD Architecture (Articolo in rivista) (literal)
Anno
  • 2002-01-01T00:00:00+01:00 (literal)
Alternative label
  • Dammone G.B., Gentile Antonio, Sorbello Filippo, Vitabile Salvatore (2002)
    MLP Neural Network Implementation on a SIMD Architecture
    in Lecture notes in computer science
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Dammone G.B., Gentile Antonio, Sorbello Filippo, Vitabile Salvatore (literal)
Pagina inizio
  • 99 (literal)
Pagina fine
  • 108 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 2486- (literal)
Rivista
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#descrizioneSinteticaDelProdotto
  • An Automatic Road Sign Recognition System (ARSRS) is aimed at detection and recognition of one or more road signs from realworld color images. The authors have proposed an ARSRS able to detect and extract sign regions from real world scenes on the basis of their color and shape features. Classification is then performed on extracted candidate regions using Multi-Layer Perceptron neural networks. Although system performances are good in terms of both sign detection and classification rates, the entire process requires a large computational time, so real-time applications are not allowed. In this paper we present the implementation of the neural layer on the Georgia Institute of Technology SIMD Pixel Processor. Experimental trials supporting the feasibility of real-time processing on this platform are also reported. (literal)
Note
  • ISI Web of Science (WOS) (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • 1- ICAR-CNR; 2,3,4- Dipartimento di Ingegneria Informatica, Università degli Studi di Palermo (literal)
Titolo
  • MLP Neural Network Implementation on a SIMD Architecture (literal)
Abstract
  • An Automatic Road Sign Recognition System (ARSRS) is aimed at detection and recognition of one or more road signs from realworld color images. The authors have proposed an ARSRS able to detect and extract sign regions from real world scenes on the basis of their color and shape features. Classification is then performed on extracted candidate regions using Multi-Layer Perceptron neural networks. Although system performances are good in terms of both sign detection and classification rates, the entire process requires a large computational time, so real-time applications are not allowed. In this paper we present the implementation of the neural layer on the Georgia Institute of Technology SIMD Pixel Processor. Experimental trials supporting the feasibility of real-time processing on this platform are also reported. (literal)
Prodotto di
Insieme di parole chiave

Incoming links:


Prodotto
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#rivistaDi
Insieme di parole chiave di
data.CNR.it