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Projectivity invariant pattern recognition with high-order neural networks

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New Trends in Neural Computation (IWANN 1993)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 686))

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Abstract

The need to provide a neural network for pattern recognition with invariance to a new transformation, projectivity, is considered. This invariance is justified when working with object images that can appear rotated in relation to an axis contained in its own plane. An invariable relation to the transformation is found, the double ratio of four points, and incorporated to the network as a restriction to the weights. A projectivity invariant pattern classifier has been simulated. Besides, some considerations about high order neural networks are expounded.

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Author information

Authors and Affiliations

  1. Dept. Arquitectura y Tecnología de Computadores y Electrónica, Universidad de Málaga, Pza. El Ejido S/N, 29013, Málaga, Spain

    G. Joya

  2. Dept. Tecnología Electrónica, Universidad de Málaga, Pza. El Ejido S/N, 29013, Málaga, Spain

    F. Sandoval

Authors
  1. G. Joya
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  2. F. Sandoval
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Editor information

José Mira Joan Cabestany Alberto Prieto

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© 1993 Springer-Verlag Berlin Heidelberg

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Joya, G., Sandoval, F. (1993). Projectivity invariant pattern recognition with high-order neural networks. In: Mira, J., Cabestany, J., Prieto, A. (eds) New Trends in Neural Computation. IWANN 1993. Lecture Notes in Computer Science, vol 686. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-56798-4_195

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  • DOI: https://doi.org/10.1007/3-540-56798-4_195

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-56798-1

  • Online ISBN: 978-3-540-47741-9

  • eBook Packages: Springer Book Archive

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