Color Constancy Via Convex Kernel Optimization

  • Xiaotong Yuan
  • Stan Z. Li
  • Ran He
Conference paper

DOI: 10.1007/978-3-540-76386-4_69

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4843)
Cite this paper as:
Yuan X., Li S.Z., He R. (2007) Color Constancy Via Convex Kernel Optimization. In: Yagi Y., Kang S.B., Kweon I.S., Zha H. (eds) Computer Vision – ACCV 2007. ACCV 2007. Lecture Notes in Computer Science, vol 4843. Springer, Berlin, Heidelberg

Abstract

This paper introduces a novel convex kernel based method for color constancy computation with explicit illuminant parameter estimation. A simple linear render model is adopted and the illuminants in a new scene that contains some of the color surfaces seen in the training image are sequentially estimated in a global optimization framework. The proposed method is fully data-driven and initialization invariant. Nonlinear color constancy can also be approximately solved in this kernel optimization framework with piecewise linear assumption. Extensive experiments on real-scene images validate the practical performance of our method.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Xiaotong Yuan
    • 1
  • Stan Z. Li
    • 1
  • Ran He
    • 1
  1. 1.Center for Biometrics and Security Research, Institute of Automation, Chinese Academy of Science, Beijing,100080China

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