1 - Multi-spectral Image Analysis for Skin Pigmentation Classification.
S. Prigent and X. Descombes and D. Zugaj and P. Martel and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Hong-Kong, China, September 2010.
Keywords : skin hyper-pigmentation, Multi-spectral images, Support Vector Machines, Independant Component Analysis, Data reduction.
@INPROCEEDINGS{sp02,
|
| author |
= |
{Prigent, S. and Descombes, X. and Zugaj, D. and Martel, P. and Zerubia, J.}, |
| title |
= |
{Multi-spectral Image Analysis for Skin Pigmentation Classification}, |
| year |
= |
{2010}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| address |
= |
{Hong-Kong, China}, |
| pdf |
= |
{http://hal.inria.fr/docs/00/49/94/92/PDF/Article_ICIP.pdf}, |
| keyword |
= |
{skin hyper-pigmentation, Multi-spectral images, Support Vector Machines, Independant Component Analysis, Data reduction} |
| } |
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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