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A system based on neural architectures for the reconstruction of 3-D shapes from images

  • E. Ardizzone
  • A. Chella
  • R. Pirrone
  • F. Sorbello
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 549)

Abstract

The connectionist approach to the recovery of 3-D shape information from 2-D images developed by the authors, is based on a system made up by two cascaded neural networks. The first network is an implementation of the BCS, an architecture which derives from a biological model of the low level visual processes developed by Grossberg and Mingolla: this architecture extracts a sort of brightness gradient map from the image. The second network is a backpropagation architecture that supplies an estimate of the geometric parameters of the objects in the scene under consideration, starting from the outputs of the BCS. A detailed description of the system and the experimental results obtained by simulating it are reported in the paper.

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References

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Copyright information

© Springer-Verlag Berlin Heidelberg 1991

Authors and Affiliations

  • E. Ardizzone
    • 1
  • A. Chella
    • 1
    • 2
  • R. Pirrone
    • 1
  • F. Sorbello
    • 1
  1. 1.DIE-Dipartimento di Ingegneria ElettricaUniversity of PalermoPalermoItaly
  2. 2.CRES-Centro per la Ricerca Elettronica in SiciliaPalermoItaly

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