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Proceedings Paper

Invariant pattern recognition using higher-order neural networks
Author(s): S. Sunthankar; Viktor A. Jaravine
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Paper Abstract

The paper provides a discussion of the results derived from the theory of invariant higher- order neural networks to design a system which will produce an invariant classification solution for a particular pattern recognition problem. This is done by employing a generalized to higher-orders back-propagation algorithm with reduced network connectivity. In special case optimal solution is obtained using linear equation technique. In both cases the volume of computations in the algorithm is much less, than that of the other methods. We demonstrate that the system can correctly classify shifted, rotated, scaled and distorted patterns with a certain amount of noise.

Paper Details

Date Published: 1 November 1992
PDF: 8 pages
Proc. SPIE 1826, Intelligent Robots and Computer Vision XI: Biological, Neural Net, and 3D Methods, (1 November 1992); doi: 10.1117/12.131596
Show Author Affiliations
S. Sunthankar, Kingston Polytechnic (United Kingdom)
Viktor A. Jaravine, Kingston Polytechnic (United Kingdom)


Published in SPIE Proceedings Vol. 1826:
Intelligent Robots and Computer Vision XI: Biological, Neural Net, and 3D Methods
David P. Casasent, Editor(s)

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