Improving color correction across camera and illumination changes by contextual sample selection

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Improving color correction across camera and illumination changes by contextual sample selection

In many tasks of machine vision applications, it is important that recorded colors remain constant, in the real world scene, even under changes of the illuminants and the cameras. Contrary to the human vision system, a machine vision system exhibits inadequate adaptability to the variation of lighting conditions. Automatic white balance control available in commercial cameras is not sufficient ...

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This paper adresses the problem of color constancy on a large image database acquired with varying digital cameras and lighting conditions. Automatic white balance control proposed by an available commercial camera is not sufficient to provide reproducible color classification. A device-independent color representation may be obtained by applying a chromatic adaptation transform, from a calibra...

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This paper presents a new statistical approach for learning automatic color image correction. The goal is to parameterize color independently of illumination and to correct color for changes of illumination. This is useful in many image processing applications, such as color image segmentation or background subtraction. The motivation for using a learning approach is to deal with changes of lig...

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Color constancy supports cross-illumination color selection.

We rely on color to select objects as the targets of our actions (e.g., the freshest fish, the ripest fruit). To be useful for selection, color must provide accurate guidance about object identity across changes in illumination. Although the visual system partially stabilizes object color appearance across illumination changes, how such color constancy supports object selection is not understoo...

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ژورنال

عنوان ژورنال: Journal of Electronic Imaging

سال: 2012

ISSN: 1017-9909

DOI: 10.1117/1.jei.21.2.023015