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Color Constancy through Inverse-Intensity Chromaticity Space

  • Robby T. Tan
  • Katsushi Ikeuchi
  • Ko Nishino

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.

Keywords

Optical Society Surface Color Color Constancy Green Channel Blue Channel 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Robby T. Tan
  • Katsushi Ikeuchi
    • 1
  • Ko Nishino
  1. 1.Institute of Industrial ScienceThe University of TokyoMeguro-kuJapan

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