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

  • Salua Nassabay
  • Ingo R. Keck
  • Carlos G. Puntonet
  • Rubén M. Clemente
  • Elmar W. Lang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4507)

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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Hyvärinen, A.: Sparse code shrinkage: Denoising of nongaussian data by maximum likelihood estimation. In: Neural Computation, pp. 1739–1768 (1999)Google Scholar
  2. 2.
    Hyvärinen, A., Hoyer, P., Oja, E.: Imagen denoising by sparse code shrinkage. In: Intelligent Signal Processing (2001)Google Scholar
  3. 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. 4.
    Chalupa, L.M., Werner, J.S. (eds.): The Visual Neurosciences. MIT Press, Cambridge (2003)Google Scholar
  5. 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. 6.
    Jutten, C., Herault, J.: Blind separation of sources, part i: An adaptive algorithm based on neuromimetic architecture. Signal Processing 24, 1–10 (1991)CrossRefMATHGoogle Scholar
  7. 7.
    Comon, P.: Independent component analysis - a new concept. Signal Processing 36, 287–314 (1994)CrossRefMATHGoogle Scholar
  8. 8.
    Hyvärinen, A., Karhunen, J., Oja, E.: Independent Component Analysis. Wiley Interscience, New York (2001)Google Scholar
  9. 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)CrossRefGoogle Scholar
  10. 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)CrossRefGoogle Scholar
  11. 11.
    Bell, A., Sejnowski, T.: The independent component of natural scenes are adge filters. Vision Research, 3327–3338 (1997)Google Scholar
  12. 12.
    Hyvärinen, A., Oja, E.: A fast fixed-point algorithm for independent component analysis. Neural Computation, 1483–1492 (1997)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Salua Nassabay
    • 1
  • Ingo R. Keck
    • 1
  • Carlos G. Puntonet
    • 1
  • Rubén M. Clemente
    • 2
  • Elmar W. Lang
    • 3
  1. 1.Department of Architecture and Technology of Computers, University of Granada, 18071 GranadaSpain
  2. 2.Department of Signals and Communication, University of Sevilla, 41004 SevillaSpain
  3. 3.Institute of Biophysics, University of Regensburg, 93040 RegensburgGermany

Personalised recommendations