Is machine colour constancy good enough?

  • Brian Funt
  • Kobus Barnard
  • Lindsay Martin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1406)


This paper presents a negative result: current machine colour constancy algorithms are not good enough for colour-based object recognition. This result has surprised us since we have previously used the better of these algorithms successfully to correct the colour balance of images for display. Colour balancing has been the typical application of colour constancy, rarely has it been actually put to use in a computer vision system, so our goal was to show how well the various methods would do on an obvious machine colour vision task, namely, object recognition. Although all the colour constancy methods we tested proved insufficient for the task, we consider this an important finding in itself. In addition we present results showing the correlation between colour constancy performance and object recognition performance, and as one might expect, the better the colour constancy the better the recognition rate.


Convex Hull Colour Constancy Colour Correction Colour Balance Clipping Level 
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-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Brian Funt
    • 1
  • Kobus Barnard
    • 1
  • Lindsay Martin
    • 1
  1. 1.School of Computing ScienceSimon Fraser UniversityBurnabyCanada

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