Evolving color constancy

The ability to compute color constant descriptors of objects in view irrespective of the light illuminating the scene is called color constancy. We have used genetic programming to evolve an algorithm for color constancy. The algorithm runs on a grid of processing elements. Each processing element is connected to neighboring processing elements. Information exchange can therefore only occur locally. Randomly generated color Mondrians were used as test cases. The evolved individual was tested on synthetic as well as real input images. Encouraged by these results we developed a parallel algorithm for color constancy. This algorithm is based on the computation of local space average color. Local space average color is used to estimate the illuminant locally for each image pixel. Given an estimate of the illuminant, we can compute the reflectances of the corresponding object points. The algorithm can be easily mapped to a neural architecture and could be implemented directly in CCD or CMOS chips used in todays cameras.