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Open Access Illuminant Estimation using Ensembles of Multivariate Regression Trees

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White balancing is a fundamental step in the image processing pipeline. The process involves estimating the chromaticity of the illuminant source and using the estimate to correct the image to remove any color cast. Given the importance of the problem, there has been much previous work on illuminant estimation. Previous work is either more accurate but slow and complex, or fast and simple but less accurate. In this paper, we propose a method for illuminant estimation that uses (i) fast features known to be predictive in illuminant estimation and (ii) single feature decision boundaries in ensembles of multivariate regression trees, (iii) each of which has been constructed to minimize a multivariate distance measure appropriate for illuminant estimation. The result is an illuminant estimation method that is simultaneously fast, simpler, and more accurate.
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Keywords: ILLUMINANT ESTIMATION; IMAGE PROCESSING; SUPERVISED MACHINE LEARNING; WHITE BALANCING

Document Type: Research Article

Publication date: January 1, 2018

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  • For more than 30 years, the Electronic Imaging Symposium has been serving those in the broad community - from academia and industry - who work on imaging science and digital technologies. The breadth of the Symposium covers the entire imaging science ecosystem, from capture (sensors, camera) through image processing (image quality, color and appearance) to how we and our surrogate machines see and interpret images. Applications covered include augmented reality, autonomous vehicles, machine vision, data analysis, digital and mobile photography, security, virtual reality, and human vision. IS&T began sole sponsorship of the meeting in 2016. All papers presented at EIs 20+ conferences are open access.

    Please note: For purposes of its Digital Library content, IS&T defines Open Access as papers that will be downloadable in their entirety for free in perpetuity. Copyright restrictions on papers vary; see individual paper for details.

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