Surface reconstruction using neural networks

Author

Chen, David S. ; Jain, Ramesh C. ; Schunck, Brian G.

Author_Institution

Artificial Intelligence Lab., Michigan Univ., Ann Arbor, MI, USA

fYear

1992

fDate

15-18 Jun 1992

Firstpage

815

Lastpage

817

Abstract

A surface reconstruction method using multilayer feedforward neural networks is proposed. The parametric form represented by multilayer neural networks can model piecewise smooth surfaces in a way that is more general and flexible than many of the classical methods. The approximation method is based on a robust backpropagation (BP) algorithm, which extends the basic BP algorithm to handle errors, especially others, in the training data

Keywords

feedforward neural nets; image processing; multilayer feedforward neural networks; neural networks; parametric form; piecewise smooth surfaces; surface reconstruction; Approximation algorithms; Approximation methods; Backpropagation algorithms; Feedforward neural networks; Multi-layer neural network; Neural networks; Reconstruction algorithms; Robustness; Surface reconstruction; Training data;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Vision and Pattern Recognition, 1992. Proceedings CVPR '92., 1992 IEEE Computer Society Conference on

Conference_Location

Champaign, IL

ISSN

1063-6919

Print_ISBN

0-8186-2855-3

Type

conf

DOI

10.1109/CVPR.1992.223251

Filename

223251

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3207844