Using a hopfield network for rotation and scale independent pattern recognition

Thomas Lee Hemminger, Carlos A. Raez

Research output: Contribution to conferencePaperpeer-review

2 Scopus citations

Abstract

This paper outlines a two-dimensional pattern recognition paradigm which is independent of rotation and scale. It is based on the Hopfield neural network, using a configuration similar to that employed for the traveling salesman problem. Enhancements to the original Hopfield design are included based on the eigenvalues of the connection matrix and through extensive simulations. The goal is to determine the underlying linear transformation between a binary valued test image and an unknown input pattern by minimizing the energy within the network. Experiment has demonstrated that this unconventional scheme performs successfully on a variety of rotated and scaled images and is robust against additive noise.

Original languageEnglish (US)
Pages3003-3008
Number of pages6
StatePublished - Dec 1 1994
EventProceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7) - Orlando, FL, USA
Duration: Jun 27 1994Jun 29 1994

Other

OtherProceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7)
CityOrlando, FL, USA
Period6/27/946/29/94

All Science Journal Classification (ASJC) codes

  • Software

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