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Reference TypeConference Proceedings
Identifier8JMKD3MGPBW34M/3D85H2S
Repositorysid.inpe.br/sibgrapi/2012/12.17.13.52
Metadatasid.inpe.br/sibgrapi/2012/12.17.13.52.37
Sitesibgrapi.sid.inpe.br
ISBN978-85-7669-271-3
Citation KeyCostaJooKöbe:1993:DiNeNe
Author1 Costa, Luciano da Fontoura
2 Joo, Javier Montenegro
3 Köberle, Roland
Affiliation1 Instituto de Física e Química de São Carlos (IFQSC) da Universidade de São Paulo (USP)
2 Instituto de Física e Química de São Carlos (IFQSC) da Universidade de São Paulo (USP)
3 Instituto de Física e Química de São Carlos (IFQSC) da Universidade de São Paulo (USP)
TitleDistance-discriminator neural networks for classification and pattern recognition
Conference NameSimpósio Brasileiro de Computação Gráfica e Processamento de Imagens, 6 (SIBGRAPI)
Year1993
EditorFigueiredo, Luiz Henrique de
Gomes, Jonas de Miranda
Volume1
Book TitleAnais
Date19 - 22 out. 1993
Publisher CityPorto Alegre
PublisherSociedade Brasileira de Computação
Conference LocationRecife
Keywordsdiscriminator neural, Distance-discriminator neurons, Distance-discriminator Neural Networks, interfaces.
AbstractDistance-discriminator neurons DDNs and their combination in Distance-discriminator Neural Networks DDNNs are proposed and discussed. DDNs, based on distance metric concepts, are able to discriminate whether a given point (x,y) belongs to a closed region such as diamond-, rectangle and ellipse-bound regions, which are tasks traditionally performed by perceptrons. DDNs can also be straightforwardly modified in order to discriminate hollow regions having as outer boundaries the above mentioned geometrical figures or even combinations of them. The principal advantage of DDNNs over perceptrons is a substantial reduction of execution time and/or the amount of required hardware operators: many polygonal classification regions which would otherwise demand large perceptron structures can be discriminated with only a few DDNNs. DDNNs can also be easily programmed by design or automatically with the help of the hough transform. Such issues as well as the relative advantages of DDNNs and perceptrons and a complete application example are presented and discussed in the present paper.
Pages221-229
Languageen
TypeVisão por Computador
Tertiary TypeArtigo
FormatImpresso, On-line.
Size5635 KiB
Number of Files1
Target File26 Distance discriminator neural networks.pdf
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History2012-12-17 13:52:37 :: cintiagraziele.silva@gmail.com -> administrator ::
2013-01-03 01:24:55 :: administrator -> cintiagraziele.silva@gmail.com :: 1993
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