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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
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)
Epanechnikov, V.A.: Nonparametric Estimation of a Multivariate Probability Density. Theory of Probab. and its Appl. 14, 153–158 (1969)
Michalak, M.: Time series prediction using new adaptive kernel estimators. Adv. in Intell. and Soft Comput. 57, 229–236 (2009)
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)
Nadaraya, E.A.: On estimating regression. Theory of Probab. and its Appl. 9, 141–142 (1964)
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)
Silverman, B.W.: Density Estimation for Statistics and Data Analysis. Chapman & Hall, Boca Raton (1986)
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)
Turlach, B.A.: Bandwidth Selection in Kernel Density Estimation: A Review. C.O.R.E. and Institut de Statistique, Universite Catholique de Louvain (1993)
Watson, G.S.: Smooth Regression Analysis. Sankhya - The Indian J. of Stat. 26, 359–372 (1964)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Michalak, M., Świtoński, A. (2011). Kernel Postprocessing of Multispectral Images. In: Burduk, R., Kurzyński, M., Woźniak, M., Żołnierek, A. (eds) Computer Recognition Systems 4. Advances in Intelligent and Soft Computing, vol 95. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20320-6_41
Download citation
DOI: https://doi.org/10.1007/978-3-642-20320-6_41
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-20319-0
Online ISBN: 978-3-642-20320-6
eBook Packages: EngineeringEngineering (R0)