Skip to main content
SpringerLink
Log in
Book cover

International Workshop on Machine Learning and Data Mining in Pattern Recognition

MLDM 1999: Machine Learning and Data Mining in Pattern Recognition pp 64–74Cite as

Unsupervised Learning of Local Mean Gray Values for Image Pre-processing

  • Conference paper
  • First Online:

  • 717 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1715))

Abstract

A parallel-sequential unsupervised learning method for image smoothing is presented which can be implemented with a Multi Layer Neural Network. In contrast to older work of the author which has used 4-connectivity of processing elements (neurons) leading to a very big number of recursions now each neuron of network layer t+1 is connected with (2M+1)✻(2M+1) neurons of layer t guaranteeing a significant reduction of network layers with the same good smoothing results.

This is a preview of subscription content, 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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. H. Jahn; “Image preprocessing and segmentation with a cellular neural network”; SPIE Proceedings, Vol. 3304, pp. 120–131, 1998

    Google Scholar 

  2. H. Jahn; “Dot pattern clustering using a cellular neural network”; SPIE Proceedings 3346, pp. 298–307, 1998

    Article  Google Scholar 

  3. H. Jahn;A Neural Network for Image Smoothing and Segmentation; Lecture Notes in Computer Science 1451, pp. 329–338, 1998

    Google Scholar 

  4. H. Jahn, W. Halle; “Texture segmentation with a neural network”; SPIE Proceedings, Vol. 3646, “Nonlinear Image Processing X”, pp. 92–99, 1999

    Google Scholar 

  5. L.O. Chua and L. Yang; “Cellular neural networks: Theory”; IEEE Trans. on Circuits and Systems, Vol. 35, pp. 1257–1272, 1988

    Article  MATH  MathSciNet  Google Scholar 

  6. B. Müller, J. Reinhardt; Neural Networks; Springer, Berlin, 1991

    Google Scholar 

  7. B. Kosko; Neural Networks and Fuzzy Systems; Prentice-Hall Inc., Englewood Cliffs,New Jersey, 1992

    MATH  Google Scholar 

  8. D. Hubel; Eye, Brain, and Vision; Scientific American Library New York, 1995

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR), Institut für Weltraumsensorik und Planetenerkundung, Rutherfordstraße 2, D-12489, Berlin, Germany

    Herbert Jahn

Authors
  1. Herbert Jahn
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Institut für Bildverarbeitung und angewandte Informatik, Arno-Nitzsche-Str. 45, D-04277, Leipzig, Germany

    Petra Perner

  2. School of Electronic Engineering, Information Technology and Mathematics, University of Surrey, Guilford, GU2 5XH, UK

    Maria Petrou

Rights and permissions

Reprints and permissions

Copyright information

© 1999 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Jahn, H. (1999). Unsupervised Learning of Local Mean Gray Values for Image Pre-processing. In: Perner, P., Petrou, M. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 1999. Lecture Notes in Computer Science(), vol 1715. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48097-8_6

Download citation

  • DOI: https://doi.org/10.1007/3-540-48097-8_6

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-66599-1

  • Online ISBN: 978-3-540-48097-6

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation