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Abstract: This paper analyzes the variation of the color of objects with respect to existing models of daylight and surface reflectance, and shows that the apparent color of objects can be represented by characteristic distributions in RGB space. These distributions can "learned" from training samples using non-parametric approximation techniques. Two approximation techniques -- Multivariate Decision Trees (MDT's) [4] and Neural Networks (with Back-propagation) [36] are used to approximate decision... (Update)
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BibTeX entry: (Update)
S. Buluswar, "Color recognition in outdoor scenes by non-parametric learning", Proc. International Conference on Computer Vision, 1998. http://citeseer.ist.psu.edu/buluswar98color.html More
@inproceedings{ buluswar98color,
author = "Sashi D. Buluswar and Bruce A. Draper",
title = "Color Recognition in Outdoor Images",
booktitle = "{ICCV}",
pages = "171-177",
year = "1998",
url = "citeseer.ist.psu.edu/buluswar98color.html" }
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