Name
Abstract | ||
|---|---|---|
In this paper, we compare two different approaches for semiautomatic 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. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/ICIP.2010.5652072 | Image Processing |
Keywords | Field | DocType |
blind source separation,image classification,support vector machines,SVM,blind source separation,data classification,independent component analysis,multispectral image analysis,semiautomatic detection,skin hyper-pigmentation,skin pigmentation classification,support vector machine,data reduction,independent component analysis,multi-spectral images,skin hyper-pigmentation,support vector machine | Computer vision,Algorithm design,Dimensionality reduction,Pattern recognition,Computer science,Support vector machine,Artificial intelligence,Pixel,Independent component analysis,Data classification,Contextual image classification,Blind signal separation | Conference |
ISSN | ISBN | Citations |
1522-4880 E-ISBN : 978-1-4244-7993-1 | 978-1-4244-7993-1 | 5 |
PageRank | References | Authors |
0.71 | 6 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sylvain Prigent | 1 | 20 | 3.32 |
| Xavier Descombes | 2 | 693 | 79.43 |
| Didier Zugaj | 3 | 35 | 4.56 |
| Martel, P. | 4 | 6 | 1.14 |