Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

The measurement of highlights in color images

  • Published:
International Journal of Computer Vision Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. D.H. Ballard and C.M. Brown,Computer Vision. Prentice Hall: Englewood Cliffs, NJ 07632, 1982.

    Google Scholar 

  2. P. Beckmann and A. Spizzichino,The Scattering of Electromagnetic Waves from Rough Surfaces. MacMillan: New York, 1963, pp. 1–33, 70–96.

    Google Scholar 

  3. J.D.E. Beynon and D.R. Lamb (eds.),Charge-coupled devices and their application. McGraw-Hill: London, 1980.

    Google Scholar 

  4. W. Budde,Optical Radiation Measurements. Volume 4:Physical Detectors of Optical Radiation. Academic Press: New York, 1983.

    Google Scholar 

  5. R.L. Cook and K.E. Torrance, “A reflectance model for computer graphics,”ACM Trans. Graphics 1(1): 7–24, January 1982. Also published in COMPUTER GRAPHICS 15(3), SIGGRAPH'81

    Google Scholar 

  6. M. D'Zmura and P. Lennie, “Mechanisms of color constancy,”J. Opt. Soc. Amer. A (JOSA-A), 3(10):1662–1672. October 1986.

    Google Scholar 

  7. L. Dreschler and H.-H. Nagel, “Volumetric model and 3D trajectory of a moving car derived from monocular TV frame Sequences of a Street Scene,”Comput. Graphics Image Process., 20:199–228, 1982.

    Google Scholar 

  8. R. Gershon, “The use of color in computational vision.” PhD thesis, Department of Computer Science, Univ. of Toronto, 1987.

  9. R. Gershon, A.D. Jepson, and J.K. Tsotsos, “Highlight identification using chromatic information,” InProc. first Int. Conf. Computer Vision (ICCV), London, Computer Society Press (IEEE), June 8–11, 1987, pp. 161–171.

  10. H. Grassmann, “On the theory of compound colors,”Phil. Mag., April 1854.

  11. H.H. Harman,Modern Factor Analysis, 2nd ed. University of Chicago Press: Chicago and London, 1967.

    Google Scholar 

  12. G. Healey and T.O. Binford, “Local shape from specularity,” In J.M. Brady and A. Rosenfeld (eds.),Proc. First Int. Conf. Computer Vision (ICCV), Computer Society Press (IEEE), London, June 8–11, 1987, pp. 151–161. Also appeared in DARPA-Image Understanding Workshop. Los Angeles, CA, February 1987, pp. 874–887.

    Google Scholar 

  13. G. Healey and T.O. Binford, “The role and use of color in a general vision system,” In L.S. Bauman (ed.),DARPA-Image Understanding (IUS) workshop, Los Angeles, CA, February 1987, pp. 599–613.

  14. B.K.P. Horn, “Understanding image intensities,”Artifical Interlligence 8(11):201–231, 1977.

    Google Scholar 

  15. B.K.P. Horn, “Exact reproduction of colored images,”Computer Vision, Graphics, and Image Processing (CVGIP) 26:135–167, 1984.

    Google Scholar 

  16. R.S. Hunter,The Measurement of Appearance Wiley: New York, 1975.

    Google Scholar 

  17. R.M. Johnston, “Geometric metamerism,”Color Engineering 5(3):42, May-June, 1967.

    Google Scholar 

  18. R.M. Johnston, “Color theory,”Pigment Handbook. Wiley: New York, 1974, pp. 229–287, ch. III-D-b.

    Google Scholar 

  19. D.B. Judd and G. Wyszecki,Color in Business, Science and Industry. Wiley, New York, 1975.

    Google Scholar 

  20. G.J. Klinker, S.A. Shafer, and T. Kanade, “Measurement of gloss from color images.” InInter-Society Color Council (ISCC) 87 Conf. on Appearance, Williamsburg, VA, February 8–11, 1987, pp. 9–12.

  21. G.J. Klinker, S.A. Shafer, and T. Kanade, “Using a color reflection model to separate highlights from object color,” In J.M. Brady and A. Rosenfeld (ed.),Proc. First Int. Conf. Computer Vision (ICCV), Computer Society Press (IEEE), London, June 8–11, 1987, pp. 145–150. Also appeared in DARPA-Image Understanding Workshop, Los Angeles, CA, February 1987, pp. 614–619.

    Google Scholar 

  22. Y. LeClerc, “A Method for Spectral Linearization.” Private communication, 1986.

  23. H.-C. Lee, “Method for computing the scene-illuminant chromaticity from specular highlights,”J. Opt. Soc. Amer. A (JOSA-A) 3(10):1694–1699, October 1986.

    Google Scholar 

  24. L.T. Maloney and B.A. Wandell, “Color constancy: a method for recovering surface spectral reflectance,”J. Opt. Soc. Amer. A (JOSA-A) 3(1):29–33, January 1986.

    Google Scholar 

  25. P. Moon, “A table of Fresnel reflection,”J. Math. Phys. 19(1), 1940.

  26. F.E. Nicodemus, J.C. Richmond, J.J. Hsia, I.W. Ginsberg, and T. Limperis,Geometrical Considerations and Nomenclature for Reflectance, Tech. Report NBS Monograph 160, National Bureau of Standards, October 1977.

  27. R. Ohlander, K. Price, and D.R. Reddy, “Picture segmentation using a recursive region splitting method,”Computer Graphics and Image Processing 8:313–333, 1978.

    Google Scholar 

  28. Y. Ohta, T. Kanade, and T. Sakai, “Color information for region segmentation,”Computer Graphics and Image Processing 13:222–231, 1980.

    Google Scholar 

  29. T. Pavlidis,Structural Pattern Recognition. Springer Verlag: Berlin, Heidelberg, New York, 1977.

    Google Scholar 

  30. B.T. Phong. “Illumination for computer generated pictures,”Communications of the ACM 18:311–317, 1975.

    Google Scholar 

  31. J.M. Rubin and W.A. Richards, “Color vision and image intensities: When are changes material?”Biological Cybernetics 45:215–226, 1982.

    Google Scholar 

  32. S.A. Shafer, “Describing light mixtures through linear algebra,”J. Opt. Soc. Amer. (JOSA) 72(2):299–300, February 1982.

    Google Scholar 

  33. S.A. Shafer, “Optical phenomena in computer vision,”Canad. Soc. Computational Studies of Intelligence (CSCSI-84), Ontario, May 1984. Also available as Tech. Report TR-135, Computer Science Department, Univ. Rochester, March 1984.

  34. S.A. Shafer, “Using color to separate reflection components,”COLOR res. appl. 10(4):210–218, Winter 1985. Also avaible as Tech. Report TR-136, Computer Science Department, Univ. of Rochester, NY, April 1984.

    Google Scholar 

  35. C.E. Thorpe, “FIDO: Vision and navigation for a robot rover.” PhD thesis, Computer Science Department, Carnegie-Mellon University, December 1984. Available as Tech. Report CMU-CS-84-168.

  36. K.E. Torrance and E.M. Sparrow, “Theory of off-specular reflection from roughened surfaces,”J. Opt. Soc. Amer. 57:1105–1114, September 1967.

    Google Scholar 

  37. S.J. Williamson and H.Z. Cummins,Light and Color in Nature and Art. Wiley, New York, 1983.

    Google Scholar 

  38. G.J. Klinker, S.A. Shafer, and T. Kanade, “Image segmentation and reflection analysis through color.” In SPIE's 1988 Techn. Symp. on Ophics, Electro-Ophics, and Sensors, April 4–8, 1988, Orlando, Florida. (Also in DARPA-Image Understanding Workshop, Boston, MA, April 1988.)

Download references

Author information

Authors and Affiliations

  1. Computer Science Department, Carnegie Mellon University, 15213-3890, Pittsburgh, PA

    Gudrun J. Klinker, Steven A. Shafer & Takeo Kanade

Authors
  1. Gudrun J. Klinker
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Steven A. Shafer
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Takeo Kanade
    View author publications

    You can also search for this author in PubMed Google Scholar

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.

Rights and permissions

Reprints and permissions

About this article

Cite this article

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

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF00836279

Keywords

Search

Navigation