Color Constancy through Inverse-Intensity Chromaticity Space

Existing color constancy methods cannot handle both uniformly colored surfaces and highly textured surfaces in a single integrated framework. Statistics-based methods require many surface colors, and become error prone when there are only a few surface colors. In contrast, dichromatic-based methods can successfully handle uniformly colored surfaces, but cannot be applied to highly textured surfaces since they require precise color segmentation. In this chapter, we present a single integrated method to estimate illumination chromaticity from single-colored and multi-colored surfaces. Unlike existing dichromatic-based methods, our proposed method requires only rough highlight regions, without segmenting the colors inside them. We show that, by analyzing highlights, a direct correlation between illumination chromaticity and image chromaticity can be obtained. This correlation is clearly described in “inverse-intensity chromaticity space”, a novel two-dimensional space we introduce. In addition, by utilizing the Hough transform and histogram analysis in this space, illumination chromaticity can be estimated robustly, even for a highly textured surface.