Spectrum evaluation on multispectral images by machine learning techniques

Authors:
Marcin Michalak;Adam Świtoński
Affiliations:
Central Mining Institute, Katowice, Poland and Silesian University of Technology, Gliwice, Poland;Polish-Japanese Institute of Information Technology, Bytom, Poland and Silesian University of Technology, Gliwice, Poland
Venue:
ICCVG'10 Proceedings of the 2010 international conference on Computer vision and graphics: Part II
Year:
2010

Citing 3
Cited 0

A training algorithm for optimal margin classifiers

COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Working Set Selection Using Second Order Information for Training Support Vector Machines

The Journal of Machine Learning Research
The WEKA data mining software: an update

ACM SIGKDD Explorations Newsletter

Quantified Score

Hi-index	0.00

Visualization

Abstract

Multispectral pictures of skin are considered as the way of detection of regions with tumor. This article raises the problem of postprocessing of the color spectrum for the improvement of the tumor region detection accuracy. As the reference point spectra of 24 model colors were aquisited and then compared with their original spectra. Difference betweeen the original and aquisited spectra motivated the authors to use data mining nonparametrical techniques to find the measured spectra postprocessing technique. Two different approaches are described: classificational and regressional.