Skip to main content
SpringerLink
Log in

Automatic Detection of Filters in Images with Gaussian Noise Using Independent Component Analysis

  • Conference paper
Computational and Ambient Intelligence (IWANN 2007)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4507))

Included in the following conference series:

  • 1667 Accesses

Abstract

In this article we present the results of a study carried out using the popular fastica algorithm applied to the detection of filters in natural images in gray-scale, contaminated with gaussian noise. The detection of filters has been accomplished by using the statistical distribution measures kurtosis and skewness.

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 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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. Hyvärinen, A.: Sparse code shrinkage: Denoising of nongaussian data by maximum likelihood estimation. In: Neural Computation, pp. 1739–1768 (1999)

    Google Scholar 

  2. Hyvärinen, A., Hoyer, P., Oja, E.: Imagen denoising by sparse code shrinkage. In: Intelligent Signal Processing (2001)

    Google Scholar 

  3. Pajares Martin Sanz, G., De la Cruz García, J.: Visión por computador. Imágenes digitales y aplicaciones. RA-MA Editorial, Madrid (2001)

    Google Scholar 

  4. Chalupa, L.M., Werner, J.S. (eds.): The Visual Neurosciences. MIT Press, Cambridge (2003)

    Google Scholar 

  5. Hérault, J., Jutten, C., Ans, B.: Detection de grandeurs primitives dans un message composite par une architecture de calcul neuromimetique en apprentissage non supervise. In: X Colloque GRETSI, pp. 1017–1022 (1985)

    Google Scholar 

  6. Jutten, C., Herault, J.: Blind separation of sources, part i: An adaptive algorithm based on neuromimetic architecture. Signal Processing 24, 1–10 (1991)

    Article  MATH  Google Scholar 

  7. Comon, P.: Independent component analysis - a new concept. Signal Processing 36, 287–314 (1994)

    Article  MATH  Google Scholar 

  8. Hyvärinen, A., Karhunen, J., Oja, E.: Independent Component Analysis. Wiley Interscience, New York (2001)

    Google Scholar 

  9. Olshausen, B.A., Field, D.J.: Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 381, 607–609 (1996)

    Article  Google Scholar 

  10. Olshausen, B.A., Field, D.J.: Sparse coding with an overcomplete basis set: A strategy employed by v1? Vision Research 37, 3311–3325 (1997)

    Article  Google Scholar 

  11. Bell, A., Sejnowski, T.: The independent component of natural scenes are adge filters. Vision Research, 3327–3338 (1997)

    Google Scholar 

  12. Hyvärinen, A., Oja, E.: A fast fixed-point algorithm for independent component analysis. Neural Computation, 1483–1492 (1997)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Architecture and Technology of Computers, University of Granada, 18071 Granada, Spain

    Salua Nassabay, Ingo R. Keck & Carlos G. Puntonet

  2. Department of Signals and Communication, University of Sevilla, 41004 Sevilla, Spain

    Rubén M. Clemente

  3. Institute of Biophysics, University of Regensburg, 93040 Regensburg, Germany

    Elmar W. Lang

Authors
  1. Salua Nassabay
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ingo R. Keck
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Carlos G. Puntonet
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rubén M. Clemente
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Elmar W. Lang
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Francisco Sandoval Alberto Prieto Joan Cabestany Manuel Graña

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Nassabay, S., Keck, I.R., Puntonet, C.G., Clemente, R.M., Lang, E.W. (2007). Automatic Detection of Filters in Images with Gaussian Noise Using Independent Component Analysis. In: Sandoval, F., Prieto, A., Cabestany, J., Graña, M. (eds) Computational and Ambient Intelligence. IWANN 2007. Lecture Notes in Computer Science, vol 4507. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73007-1_83

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-73007-1_83

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-73006-4

  • Online ISBN: 978-3-540-73007-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation