Neural Networks versus Image Pyramids

  • Horst Bischof
  • Walter G. Kropatsch


Neural networks and image pyramids are massively parallel processing structures. In this paper we exploit the similarities as well as the differences between these structures. The general goal is to exchange knowledge between these two fields. After introducing the basic concepts of neural networks and image pyramids we give a translation table of the vocabulary used in image pyramids and those used in neural networks. Image pyramids which store and process numerical information (e.g. grey values of pixels) are very similar to neural networks. Therefore we concentrate on ”symbolic pyramids”. The main idea is to replace a cell of the pyramid by a small neural network, in order to represent and process symbolic information. We will consider local as well as distributed representations for symbolic information. In particular we present a neural implementation of the 2 x 2/2 curve pyramid. We derive some general rules for implementing symbolic pyramids by neural networks. Finally we briefly discuss the role of learning in image pyramids.


Neural Network Transitive Closure Neighborhood Graph Symbolic Information Curve Relation 
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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Copyright information

© Springer-Verlag/Wien 1993

Authors and Affiliations

  • Horst Bischof
    • 1
  • Walter G. Kropatsch
    • 1
  1. 1.Inst. for Automation Dept. for Pattern Recognition and Image ProcessingTechnical University ViennaAustria

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