A radial basis function neural network for parts identification of three-dimensional shapes:

Abstract: "The 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 superquadrics from range data can be reliably mapped into a set of qualitative volumet...

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Bibliographic Details
Main Authors: Borges, Díbio L. (Author), Orr, Mark J. (Author), Fisher, Robert B. (Author)
Format: Book
Language:English
Published: Edinburgh 1994
Series:University <Edinburgh> / Department of Artificial Intelligence: DAI research paper 728
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Summary:Abstract: "The 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 superquadrics 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 regularised RBF classifier and the results are shown to be both reliable and efficient in the context of range image understanding."
Physical Description:8 S.

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