Color constancy algorithms for object and face recognition
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part I
Computational Intelligence and Neuroscience - Special issue on Selected Papers from the 4th International Conference on Bioinspired Systems and Cognitive Signal Processing
User-guided white balance for mixed lighting conditions
ACM Transactions on Graphics (TOG) - Proceedings of ACM SIGGRAPH Asia 2012
A flexible auto white balance based on histogram overlap
ACCV'12 Proceedings of the 11th international conference on Computer Vision - Volume Part I
A linear system form solution to compute the local space average color
Machine Vision and Applications
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Light, which is reflected from an object, varies with the type of illuminant used. Nevertheless, the color of an object appears to be approximately constant to a human observer. The ability to compute color constant descriptors from reflected light, is called color constancy. In order to solve the problem of color constancy, some assumptions have to be made. One frequently made assumption is that on average, the world is gray. We address the problem of color constancy and focus on the use of space average color for color constancy. Instead of computing global space average color we suggest to use local space average color as the illuminant frequently varies across an image. We discuss several different methods on how to compute local space average color. The performance of the different algorithms as well as related algorithms is evaluated on an object recognition task. Algorithms based on local space average color are simple, yet highly effective for the problem of color constancy. Such algorithms are particularly suited for object recognition tasks.