Evolving color constancy

author = "Marc Ebner",

title = "Evolving color constancy",

journal = "Pattern Recognition Letters",

year = "2006",

volume = "27",

number = "11",

pages = "1220--1229",

month = aug,

note = "Evolutionary Computer Vision and Image Understanding",

keywords = "genetic algorithms, genetic programming, Colour constancy, Local space average colour",

DOI = "

doi:10.1016/j.patrec.2005.07.020",

abstract = "The ability to compute colour 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 colour constancy. The algorithm runs on a grid of processing elements. Each processing element is connected to neighbouring processing elements. Information exchange can therefore only occur locally. Randomly generated colour 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 colour constancy. This algorithm is based on the computation of local space average colour. Local space average colour 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.",