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Reference TypeConference Paper (Conference Proceedings)
Last Update2013: (UTC) administrator
Metadata Last Update2013: (UTC) administrator
Citation KeyBorgesOrrFish:1994:RaBaFu
TitleA radial basis function neural network for parts identification of three dimensional shapes
FormatImpresso, On-line.
Access Date2022, Jan. 21
Number of Files1
Size5229 KiB
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Author1 Borges, Díbio Leandro
2 Orr, Mark J.
3 Fisher, Robert B.
Affiliation1 Department of Artificial Intelligence of Edinburgh University
2 Centre for Cognitive Science of Edinburgh University
3 Centre for Cognitive Science of Edinburgh University
EditorFreitas, Carla dal Sasso
Geus, Klaus de
Scheer, Sérgio
Conference NameSimpósio Brasileiro de Computação Gráfica e Processamento de Imagens, 7 (SIBGRAPI)
Conference LocationCuritiba
Date9 - 11 nov. 1994
PublisherSociedade Brasileira de Computação
Publisher CityPorto Alegre
Book TitleAnais
Tertiary TypeArtigo
History (UTC)2013-01-21 16:33:42 :: -> administrator ::
2013-02-02 13:16:02 :: administrator -> :: 1994
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Is the master or a copy?is the master
Content Stagecompleted
Keywordsfunction neural
function neural
image understanding
AbstractThis discrimination of volumetric pieces or parts of objects from range data is one key element for achieving 3-D object recognition. In this paper it is shown that previously segmented and acquired super quadrics from range data can be reliably mapped into a set of qualitative volumetric shapes (geons) by means of an RBF (Radial Basis Function) neural network classifier. We use a regularized RBF classifier and the results are shown to be both reliable and efficient in the context of range image understanding.
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