Colour in Context
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Computer Vision Center

Color Constancy algorithms: Psychophysical evaluaton on a new dataset

The estimation of the illuminant of a scene from a digital image has been the goal of a large amount of research in computer vision. Color constancy algorithms have dealt with this problem by defining different heuristics to select a unique solution from within the feasible set. The performance of these algorithms has shown that there is still a long way to go to globally solve this problem as a preliminary step in computer vision. In general, performance evaluation has been done by comparing the angular error between the estimated chromaticity and the chromaticity of a canonical illuminant, which is highly dependent on the image dataset. Recently, some workers have used high-level constraints to estimate illuminants; in this case selection is based on increasing the performance on the subsequent steps of the systems.

Experimental schedule

In this paper we propose a new performance measure, the perceptual angular error. It evaluates the performance of a color constancy algorithm according to the perceptual preferences of humans, or naturalness (instead of the actual optimal solution) and is independent of the visual task. We show the results of a new psychophysical experiment comparing solutions from three different color constancy algorithms. Our results show that in more than a half of the judgments the preferred solution is not the one closest to the optimal solution. Our experiments were performed on a new dataset of images acquired with a calibrated camera with an attached neutral grey sphere, which better copes with the illuminant variations of the scene.




Color Constancy algorithms: Psychophysical evaluaton on a new dataset J Vazquez-Corral, C.A Párraga, M Vanrell, R Baldrich, Journal of Imaging Science and Technology , Volume 53, Number 3 - May-June 2009

Full paper PDF

[VPV 2009] -  Color Constancy Algorithms: Psychophysical Evaluation on a New Dataset



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