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Kernel Postprocessing of Multispectral Images

  • Marcin Michalak
  • Adam Świtoński
Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 95)

Abstract

Multispectral analysis is the one of possible ways of skin desease detection. This short paper describes the nonparametrical way of multispectral image postprocessing that improves the quality of obtained pictures. The method below may be described as the regressional approach because it uses kernel regression function estimator as its essence. The algorithm called HASKE was developed as the time series predictor. Its simplification may be used for the postprocessing of multispectral images.

Keywords

Multispectral images analysis nonparametrical regression machine learning HASKE 

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References

  1. 1.
    Blum, A., Zalaudek, I., Argenziano, G.: Digital Image Analysis for Diagnosis of Skin Tumors. Seminars in Cutaneous Medicine and Surgery 27(1), 11–15 (2008)CrossRefGoogle Scholar
  2. 2.
    Epanechnikov, V.A.: Nonparametric Estimation of a Multivariate Probability Density. Theory of Probab. and its Appl. 14, 153–158 (1969)CrossRefGoogle Scholar
  3. 3.
    Michalak, M.: Time series prediction using new adaptive kernel estimators. Adv. in Intell. and Soft Comput. 57, 229–236 (2009)Google Scholar
  4. 4.
    Michalak, M., Świtoński, A.: Spectrum evaluation on multispectral images by machine learning techniques. In: Bolc, L., Tadeusiewicz, R., Chmielewski, L.J., Wojciechowski, K. (eds.) ICCVG 2010. LNCS, vol. 6375, pp. 126–133. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  5. 5.
    Nadaraya, E.A.: On estimating regression. Theory of Probab. and its Appl. 9, 141–142 (1964)CrossRefGoogle Scholar
  6. 6.
    Prigent, S., Descombes, X., Zugaj, D., Martel, P., Zerubia, J.: Multi-spectral image analysis for skin pigmentation classification. In: Proc. of IEEE Int. Conf. on Image Process (ICIP), pp. 3641–3644 (2010)Google Scholar
  7. 7.
    Silverman, B.W.: Density Estimation for Statistics and Data Analysis. Chapman & Hall, Boca Raton (1986)zbMATHGoogle Scholar
  8. 8.
    Świtoński, A., Michalak, M., Josiński, H., Wojciechowski, K.: Detection of tumor tissue based on the multispectral imaging. In: Bolc, L., Tadeusiewicz, R., Chmielewski, L.J., Wojciechowski, K. (eds.) ICCVG 2010. LNCS, vol. 6375, pp. 325–333. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  9. 9.
    Turlach, B.A.: Bandwidth Selection in Kernel Density Estimation: A Review. C.O.R.E. and Institut de Statistique, Universite Catholique de Louvain (1993)Google Scholar
  10. 10.
    Watson, G.S.: Smooth Regression Analysis. Sankhya - The Indian J. of Stat. 26, 359–372 (1964)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Marcin Michalak
    • 1
    • 2
  • Adam Świtoński
    • 2
    • 3
  1. 1.Central Mining InstituteKatowicePoland
  2. 2.Silesian University of TechnologyGliwicePoland
  3. 3.Polish-Japanese Institute of Information TechnologyWarszawaPoland

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