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MULTI-SPECTRAL IMAGE ANALYSIS FOR SKIN PIGMENTATION CLASSIFICATION

Sylvain Prigent 1 Xavier Descombes 1 Didier Zugaj 2 Philippe Martel 2 Josiane Zerubia 1
1 ARIANA - Inverse problems in earth monitoring
CRISAM - Inria Sophia Antipolis - Méditerranée , Laboratoire I3S - SIS - Signal, Images et Systèmes
Abstract : In this paper, we compare two different approaches for semi- automatic detection of skin hyper-pigmentation on multi- spectral images. These two methods are support vector machine (SVM) and blind source separation. To apply SVM, a dimension reduction method adapted to data classification is proposed. It allows to improve the quality of SVM classification as well as to have reasonable computation time. For the blind source separation approach we show that, using independent component analysis, it is possible to extract a relevant cartography of skin pigmentation.
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https://hal.inria.fr/inria-00499492
Contributor : Sylvain Prigent <>
Submitted on : Friday, July 9, 2010 - 5:23:39 PM
Last modification on : Monday, October 12, 2020 - 10:30:17 AM
Long-term archiving on: : Monday, October 11, 2010 - 9:43:04 AM

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Sylvain Prigent, Xavier Descombes, Didier Zugaj, Philippe Martel, Josiane Zerubia. MULTI-SPECTRAL IMAGE ANALYSIS FOR SKIN PIGMENTATION CLASSIFICATION. IEEE International Conference on Image Processing (ICIP), Sep 2010, Hong Kong, China. ⟨inria-00499492⟩

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