Skip to main content
SpringerLink
Log in

Applying Dynamic Link Matching to Object Recognition in Real World Images

  • Conference paper
  • First Online:
ICANN ’93 (ICANN 1993)

Included in the following conference series:

  • 19 Accesses

Abstract

We apply the dynamic link matching algorithm to object recognition in gray level images. The algorithm is able to map from one view of an object to different-e. g., translated, rotated, or mirror-reflected—views, being at the same time tolerant of small distortions. A sparse representation (10%) of the image data is used as a boundary condition for a self-organizing mechanism which performs the object match within a modest number of iterations (~102). The mechanism can be derived from local neural dynamics [1].

Supported by a grant from the German Federal Ministry for Science and Technology (413-5839-01 IN 101 B/9).

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. W. Konen and C.v.d. Malsburg. Learning symmetries from single examples in the dynamic link architecture. To appear in: Neural Computation, 1993.

    Google Scholar 

  2. J.J. Hopfield. Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences, 79:2554–2558, 1982.

    Article  MathSciNet  Google Scholar 

  3. T. Kohonen. Associative Memory. Springer, Berlin, 1977.

    Book  Google Scholar 

  4. Y. LeCun, B. Boser, J.S. Denker, D. Henderson, R.E. Howard, W. Hubbard and L.D. Jackel. Backpropagation applied to handwritten zip code recognition. Neural Computation, 1:541–551, 1989.

    Article  Google Scholar 

  5. K. Fukushima. Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition. Biological Cybernetics, 36:193, 1980.

    Article  Google Scholar 

  6. C.v.d. Malsburg. Pattern recognition by labeled graph matching. Neural Networks, 1:141–148, 1988.

    Article  Google Scholar 

  7. E. Bienenstock and R. Doursat. Elastic matching and pattern recognition in neural networks. In L. Personnaz and G. Dreyfus, editors, Neural Networks: From Models to Applications. IDSET, Paris, 1989.

    Google Scholar 

  8. M. Lades, J.C. Vorbriiggen, J. Buhmann, J. Lange, C.v.d. Malsburg, R.P. Würtz, and W. Konen. Distortion invariant object recognition in the dynamic link architecture. IEEE Trans. Comp., March 1993.

    Google Scholar 

  9. W. Konen and C.v.d. Malsburg. Unsupervised symmetry detection: A network which learns from single examples. In I. Aleksander, editor, Proceedings of the International Conference on Artificial Neural Networks, pages 121–125. North-Holland, Amsterdam, 1992.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institut für Neuroinformatik, Ruhr-Universität Bochum, Germany

    Wolfgang Konen & Jan C. Vorbrüggen

Authors
  1. Wolfgang Konen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jan C. Vorbrüggen
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Dutch Foundation for Neural Networks, University of Nijmegen, Geert Grooteplein 21, 6525 EZ, Nijmegen, The Netherlands

    Stan Gielen  & Bert Kappen  & 

Rights and permissions

Reprints and permissions

Copyright information

© 1993 Springer-Verlag London Limited

About this paper

Cite this paper

Konen, W., Vorbrüggen, J.C. (1993). Applying Dynamic Link Matching to Object Recognition in Real World Images. In: Gielen, S., Kappen, B. (eds) ICANN ’93. ICANN 1993. Springer, London. https://doi.org/10.1007/978-1-4471-2063-6_288

Download citation

  • DOI: https://doi.org/10.1007/978-1-4471-2063-6_288

  • Published:

  • Publisher Name: Springer, London

  • Print ISBN: 978-3-540-19839-0

  • Online ISBN: 978-1-4471-2063-6

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation