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Summary: ESTIMATING ILLUMINATION CHROMATICITY via KERNEL REGRESSION
Vivek Agarwal, Andrei V. Gribok, Andreas Koschan, and Mongi A. Abidi
Imaging, Robotics and Intelligent Systems Laboratory
The University of Tennessee, Knoxville
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.
Index Terms-- Kernel regression, Color constancy
1. INTRODUCTION
A color image can be represented as a product of three
functions of the wavelength over a visible spectrum .
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