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Linear Quadtrees for Neural Network Based Position Invariant Pattern Recognition

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Neural Network Applications

Part of the book series: Perspectives in Neural Computing ((PERSPECT.NEURAL))

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Abstract

In the present paper a new method is proposed which involves teaching an Artificial Neural Network with a hierarchical data structure. This method results in a recognition process that significantly reduces the duration of a training process that is generally time consuming. The proposed method uses an invariant Octree data structure to describe the volume of a three dimensional object. The object is thus transformed in such a way that it is independent of rotation, translation and scaling and provides a significant storage capacity compression rate.

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Author information

Authors and Affiliations

  1. Advanced Computer Architecture Group Department of Computer Science, University of York, UK

    Emmanouel C. Mertzanis & James Austin

Authors
  1. Emmanouel C. Mertzanis
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  2. James Austin
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Editor information

Editors and Affiliations

  1. Centre for Neural Networks, Department of Mathematics, King’ College, WC2R 2LS, Strand, London, UK

    J. G. Taylor BA, BSc, MA, PhD, FlnstP (Director) (Director)

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© 1992 Springer-Verlag London Limited

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Mertzanis, E.C., Austin, J. (1992). Linear Quadtrees for Neural Network Based Position Invariant Pattern Recognition. In: Taylor, J.G. (eds) Neural Network Applications. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-2003-2_7

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  • DOI: https://doi.org/10.1007/978-1-4471-2003-2_7

  • Publisher Name: Springer, London

  • Print ISBN: 978-3-540-19772-0

  • Online ISBN: 978-1-4471-2003-2

  • eBook Packages: Springer Book Archive

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