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The measurement of highlights in color images

Abstract

In this paper, we present an approach to color image understanding that accounts for color variations due to highlights and shading. We demonstrate that the reflected light from every point on a dielectric object, such as plastic, can be described as a linear combination of the object color and the highlight color. The colors of all light rays reflected from one object then form a planar cluster in the color space. The shape of this cluster is determined by the object and highlight colors and by the object shape and illumination geometry. We present a method that exploits the difference between object color and highlight color to separate the color of every pixel into a matte component and a highlight component. This generates two intrinsic images, one showing the scene without highlights, and the other one showing only the highlights. The intrinsic images may be a useful tool for a variety of algorithms in computer vision, such as stereo vision, motion analysis, shape from shading, and shape from highlights. Our method combines the analysis of matte and highlight reflection with a sensor model that accounts for camera limitations. This enables us to successfully run our algorithm on real images taken in a laboratory setting. We show and discuss the results.

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Additional information

This material is based upon work supported by the National Science Foundation under Grant DCR-8419990 and by the Defense Advanced Research Projects Agency (DOD), ARPA Order No. 4976, monitored by the Air Force Avionics Laboratory under contract F33615-84-K-1520. Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the authors and do not necessarily reflect the views of the National Science Foundation, the Defense Advanced Research Projects Agency, or the US Government.

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Klinker, G.J., Shafer, S.A. & Kanade, T. The measurement of highlights in color images. Int J Comput Vision 2, 7–32 (1988). https://doi.org/10.1007/BF00836279

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Keywords

  • Computer Vision
  • Color Image
  • Color Space
  • Motion Analysis
  • Color Variation