Grade Differentiation Measure of Images

  • Maria Grzegorek
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 57)


This paper describes application of newly developed grade differentiation measure between two datasets to images processing. Pixels of an image are transformed into a dataset of records describing pixels. Each pixel is characterized by its gray level, gradient magnitude and a family of variables n 1,...,n k , where k has been arbitrarily chosen. Grade Correspondence Cluster Analysis procedure implemented in program GradeStat allows reorder a sequence of records and divides pixels onto similar groups/subimages. Procedure GCCA takes a significant amount of time in the case of large images. Comparison of grade differentiation measures between variables allows to decrease the number of variables and the same to decrease processing time.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Szczesny, W., Kowalczyk, T.: On Regularity of Multivariate Datasets. In: Kłopotek, M., Wierzchoń, S.T., Michalewicz, M. (eds.) Intelligent Information Systems 2002, Proceedings of the IIS 2002, Sopot, Poland, June 3-6, 2002. Advances in Soft Computing, pp. 237–246. Physica-Verlag, Heidelberg (2002)CrossRefGoogle Scholar
  2. 2.
    Kowalczyk, T., Pleszczyńska, E., Ruland, F. (eds.): Grade Models and Methods for Data Analysis, With Applications for the Analysis of Data Populations. Studies in Fuzziness and Soft Computing, vol. 151. Springer, Heidelberg (2004)zbMATHGoogle Scholar
  3. 3.
  4. 4.
    Grzegorek, M.: Image Decomposition by Grade Analysis - an Illustration. In: Kurzyński, M., Puchała, E., Woźniak, M., Żołnierek, A. (eds.) Computer Recognition Systems. Advances in Soft Computing, pp. 387–394. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  5. 5.
    Szczesny, W., Kowalczyk, T., Srebrny, M., Such, P.: Testing ciphers quality as random numbers generators: results of experiments explorating grade statistical methods. Technical report ICS PAN, Warsaw, Poland (2006) (in Polish)Google Scholar
  6. 6.
    Grzegorek, M.: Homogeneity of pixel’s neighborhoods in gray level images investigated by the Grade Correspondence Analysis. In: Kurzyński, M., Puchała, E., Woźniak, M., Żołnierek (eds.) Computer Recognition Systems 2. Advances in Soft Computing, pp. 76–83. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  7. 7.
    Pleszczyńska, E.: Application of grade methods to medical data: new examples. Biocybernetics and Biomedical Engineering 27(3), 77–93 (2007)Google Scholar
  8. 8.
    Grzegorek, M.: Variables Applied in a NMR Image Decomposition with the Aid of GCCA. J. of Medical Informatics and Technologies 12, 183–187 (2008)Google Scholar
  9. 9.
    Szczesny, W., Kowalczyk, T.: Grade differentiation between multivariate datasets (in preparation)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Maria Grzegorek
    • 1
  1. 1.Institute of Computer SciencePolish Academy of SciencesWarsawPoland

Personalised recommendations