Hybrid functional-neural approach for surface reconstruction
type: Item
instance of: scholarly article
title: Hybrid functional-neural approach for surface reconstruction
zbMATH Open document ID: 1407.68512
DOI: 10.1155/2014/351648
author: Andrés Iglesias
author: Akemi Gálvez
published in: Mathematical Problems in Engineering
publication date: 2019-02-08T00:00:00Z
review text: Summary: This paper introduces a new hybrid functional-neural approach for surface reconstruction. Our approach is based on the combination of two powerful artificial intelligence paradigms: on one hand, we apply the popular Kohonen neural network to address the data parameterization problem. On the other hand, we introduce a new functional network, called NURBS functional network, whose topology is aimed at reproducing faithfully the functional structure of the NURBS surfaces. These neural and functional networks are applied in an iterative fashion for further surface refinement. The hybridization of these two networks provides us with a powerful computational approach to obtain a NURBS fitting surface to a set of irregularly sampled noisy data points within a prescribed error threshold. The method has been applied to two illustrative examples. The experimental results confirm the good performance of our approach.
Mathematics Subject Classification ID: 68U05
Mathematics Subject Classification ID: 68U07
Mathematics Subject Classification ID: 68T05
Mathematics Subject Classification ID: 65D17
zbMATH DE Number: 7016218
describes a project that uses: FITPACK
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2014/351648
OpenAlex ID: W2007448576
Wikidata QID: Q57661678
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description: scientific article; zbMATH DE number 7016218