Based on a new correspondence matching invariant called \emph{Illumination Chromaticity Constancy}, we present a new solution for illumination chromaticity estimation, correspondence searching and specularity removal. Using as few as two images, the core of our method is the computation of a vote distribution for a number of illumination chromaticity hypotheses via correspondence matching. The hypothesis with the highest vote is accepted as correct. The estimated illumination chromaticity is then used together with the new matching invariant to match highlights, which inherently provides solutions for correspondence searching and specularity removal. Our method differs from the previous approaches: those treat these vision problems separately, and generally require specular highlights be detected in a pre-processing step. Also, our method uses more images than previous illumination chromaticity estimation methods, which increases its robustness as more inputs/constraints are used. Experimental results on both synthetic and real images demonstrate the effectiveness of the proposed method.

• Qingxiong Yang, Shengnan Wang, N. Ahuja and Ruigang Yang, A Uniform Framework for Estimating Illumination Chromaticity, Correspondence and Specular Reflection, IEEE Transactions on Image Processing (TIP), 2011.