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Invariant Pattern Recognition: Towards Neural Network Classifiers

  • G. Lebreton
  • E. Marom
  • N. Konforti
  • D. Mendlovic
Part of the ESPRIT Basic Research Series book series (ESPRIT BASIC)

Abstract

From the many studies on invariant pattern recognition, the coauthors have selected single harmonic filters [1,2]. A real-time optical implementation of such filters was demonstrated on photographic input images [3], but its 10-F length yielded a poor signal-to-noise ratio, making it unusable with the limited dynamic range and flatness of a light valve for real-time video input. For real applications, the other difficulty is the usual requirement of all invariances simultaneously: scale and projection can be processed in a single filter [1], but a pre-processing is necessary for the rotation before any object recognition, since the projection and scale invariant filter do not work on rotated objects.

Keywords

Associative Memory Photographic Plate Neural Network Classifier Limited Dynamic Range Complex Filter 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

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    Mendlovic, D., Marom, E., Konforti, N.: Improved rotation or scale invariant matched filter. Appl. Opt. 28 (1989) 3814–3819.CrossRefGoogle Scholar
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    Mendlovic, D., Marom, E., Konforti, N.: Scale and projection invariant pattern recognition. Appl. Opt. 28 (1989) 4982–4986.CrossRefGoogle Scholar
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Copyright information

© ECSC — EEC — EAEC, Brussels — Luxembourg 1993

Authors and Affiliations

  • G. Lebreton
    • 1
  • E. Marom
    • 2
  • N. Konforti
    • 2
  • D. Mendlovic
    • 2
  1. 1.University of ToulonLa Garde cedexFrance
  2. 2.University of Tel AvivTel AvivIsrael

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