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Estimating Illumination Chromaticity Via Support Vector Regression

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Support vector regression is applied to the problem of estimating the chromaticity of the light illuminating a scene from a color histogram of an image of the scene. Illumination estimation is fundamental to white balancing digital color images and to understanding human color constancy. Under controlled experimental conditions, the support vector method is shown to perform well. Its performance is compared to other published methods including neural network color constancy, color by correlation, and shades of gray.
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Document Type: Research Article

Affiliations: School of Computing Science, Simon Fraser University, Vancouver, Canada

Publication date: 01 July 2006

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  • The Journal of Imaging Science and Technology (JIST) is dedicated to the advancement of imaging science knowledge, the practical applications of such knowledge, and how imaging science relates to other fields of study. The pages of this journal are open to reports of new theoretical or experimental results, and to comprehensive reviews. Only original manuscripts that have not been previously published, nor currently submitted for publication elsewhere, should be submitted.

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