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www.jprr.org Journal of Pattern Recognition Research 1 (2009) 92-109

Summary: www.jprr.org
Journal of Pattern Recognition Research 1 (2009) 92-109
Received August 28, 2008. Accepted November 12, 2008.
Illumination Chromaticity Estimation Using
Linear Learning Methods
Vivek Agarwal agarwal1@purdue.edu
School of Nuclear Engineering, Purdue University
400 Central Dr, West Lafayette, IN 47907, USA
Andrei V. Gribok agribok@bioanalysis.org
BHSAI/MRMC, Building 363 Miller Dr, Fort Detrick, MD 21792, USA
Andreas Koschan akoschan@utk.edu
Besma R. Abidi besma@utk.edu
Mongi A. Abidi abidi@utk.edu
Department of Electrical and Computer Engineering, The University of Tennessee
331 Ferris Hall, Knoxville, TN 37996, USA
In this paper, we present the application of two linear machine learning techniques; ridge
regression and kernel regression for the estimation of illumination chromaticity. A number
of machine learning techniques, neural networks and support vector machines in particu-
lar, are used to estimate the illumination chromaticity. These nonlinear approaches are


Source: Abidi, Mongi A. - Department of Electrical and Computer Engineering, University of Tennessee
Koschan, Andreas - Imaging, Robotics, and Intelligent Systems, University of Tennessee


Collections: Computer Technologies and Information Sciences; Engineering