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Proceedings Paper

Approach to parallel-hierarchical network learning for real-time image sequence recognition
Author(s): Leonid I. Timchenko; Yuri F. Kutaev; Alexander A. Gertsiy; Lubov V. Zahoruiko; Yaroslav O. Galchenko; Tamer Mansur
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Paper Abstract

In this work, a new approach for parallel-hierarchical (PH) networks learning having applied to the real-time image sequences in extended laser paths is proposed. It is possible to synthesize PH network with learning abilities by using the general idea of artificial neural networks structured organization on the scheme: input layer - hidden layer - output layer. The 1st network level should be used as input layer, next levels should be used as a hidden layer and the last level should be used as an output one, as it is traditionally in artificial neural networks, Using the main PH network feature which determine the length of network algorithm it is possible to determine a number of hidden layer elements. And in this way it formalizes the procedure of obtaining the number of hidden layer elements.

Paper Details

Date Published: 27 August 1999
PDF: 11 pages
Proc. SPIE 3836, Machine Vision Systems for Inspection and Metrology VIII, (27 August 1999); doi: 10.1117/12.360283
Show Author Affiliations
Leonid I. Timchenko, Vinnitsa State Technical Univ. (Ukraine)
Yuri F. Kutaev, State Scientific Enterprise Astrophysica (Russia)
Alexander A. Gertsiy, Vinnitsa State Technical Univ. (Ukraine)
Lubov V. Zahoruiko, Vinnitsa State Technical Univ. (Ukraine)
Yaroslav O. Galchenko, Vinnitsa State Technical Univ. (Ukraine)
Tamer Mansur, Vinnitsa State Technical Univ. (Ukraine)


Published in SPIE Proceedings Vol. 3836:
Machine Vision Systems for Inspection and Metrology VIII
John W. V. Miller; Susan Snell Solomon; Bruce G. Batchelor, Editor(s)

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