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


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.


Gaussian Noise Independent Component Analysis Automatic Detection Natural Image Independent Component Analysis 
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 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

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