ICIP 2006, Atlanta, GA
 

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Atlanta Conv. & Vis. Bureau

 

Technical Program

Paper Detail

Paper:MP-P6.5
Session:Color and Multispectral Processing
Time:Monday, October 9, 14:20 - 17:00
Presentation: Poster
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.