Name
Abstract | ||
|---|---|---|
In this paper, we deal with the problem of reflectance recovery from multispectral camera output using Support Vector Regression (SVR). As standard, SVR is unidimensional, the spectral reflectance recovery requires a multi-dimensional output. We propose two ways of adaptation: the transformation of the dataset (camera output) to a scalar-valued composite data model on the one hand, and the adaptation of a recent multi-output SVR on the other hand. We compare both performances to a Wiener-based reflectance recovery. The results are quite satisfactory and the comparison points out the advantages and drawbacks of each one of the proposed methods. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/SITIS.2012.121 | Signal Image Technology and Internet Based Systems |
Keywords | Field | DocType |
support vector regression,multispectral camera output,reflectance recovery,comparison point,spectral reflectance recovery,recent multi-output svr,single-output support,wiener-based reflectance recovery,multi-dimensional output,camera output,data models,regression analysis,support vector machines | Data modeling,Computer vision,Pattern recognition,Computer science,Regression analysis,Support vector machine,Multispectral image,Artificial intelligence,Reflectivity,Data model | Conference |
ISBN | Citations | PageRank |
978-1-4673-5152-2 | 2 | 0.38 |
References | Authors | |
4 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ferdinand Deger | 1 | 20 | 2.09 |
| Alamin Mansouri | 2 | 137 | 22.29 |
| Marius Pedersen | 3 | 171 | 32.96 |
| Jon Y. Hardeberg | 4 | 26 | 4.94 |
| Yvon Voisin | 5 | 65 | 12.66 |