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Technical Program
Paper Detail
| Paper: | MP-P6.5 |
| Session: | Color and Multispectral Processing |
| Time: | Monday, October 9, 14:20 - 17:00 |
| Presentation: |
Poster
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| Title: |
ESTIMATING ILLUMINATION CHROMATICITY VIA KERNEL REGRESSION |
| Authors: |
Vivek Agarwal; University of Tennessee | | | | | Andrei Gribok; University of Tennessee | | | | | Andreas Koschan; University of Tennessee | | | | | Mongi Abidi; University of Tennessee | | |
| Abstract: |
We propose a simple nonparametric linear regression tool, known as kernel regression (KR), to estimate the illumination chromaticity. We design a Gaussian kernel whose bandwidth is selected empirically. Previously, nonlinear techniques like neural networks (NN) and support vector machines (SVM) are applied to estimate the illumination chromaticity. However, neither of the techniques was compared with linear regression tools. We show that the proposed method performs better chromaticity estimation compared to NN, SVM, and linear ridge regression (RR) approach on the same data set. |
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