Improved illumination invariance using a color edge representation based on Double Opponent neurons

We describe an evaluation framework that provides a quantitative measure on the performance of a neural network color constancy model. In this framework, the responses of three models of color constancy to a set of color edges under varying illuminating conditions are computed. We study a model based on double opponent cells, as well as two variants of the Retinex model. Evaluation metrics on the modelspsila capabilities to discriminate among different color edges and resist illuminant induced changes are measured using this framework, we confirm the advantage of incorporating spectral opponency into the color constancy model.