Abstract
Brightness and color constancy is a fundamental problem faced in computer vision and by our own visual system. We easily recognize objects despite changes in illumination, but without a mechanism to cope with this, many object and face recognition systems perform poorly. In this paper we compare approaches in computer vision and computational neuroscience for inducing brightness and color constancy based on their ability to improve recognition. We analyze the relative performance of the algorithms on the AR face and ALOI datasets using both a SIFT-based recognition system and a simple pixel-based approach. Quantitative results demonstrate that color constancy methods can significantly improve classification accuracy. We also evaluate the approaches on the Caltech-101 dataset to determine how these algorithms affect performance under relatively normal illumination conditions.
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Kanan, C., Flores, A., Cottrell, G.W. (2010). Color Constancy Algorithms for Object and Face Recognition. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2010. Lecture Notes in Computer Science, vol 6453. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17289-2_20
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DOI: https://doi.org/10.1007/978-3-642-17289-2_20
Publisher Name: Springer, Berlin, Heidelberg
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