OSA's Digital Library

Journal of the Optical Society of America A

Journal of the Optical Society of America A


  • Vol. 18, Iss. 11 — Nov. 1, 2001
  • pp: 2679–2691

Color line search for illuminant estimation in real-world scenes

Thomas M. Lehmann and Christoph Palm  »View Author Affiliations

JOSA A, Vol. 18, Issue 11, pp. 2679-2691 (2001)

View Full Text Article

Enhanced HTML    Acrobat PDF (388 KB)

Browse Journals / Lookup Meetings

Browse by Journal and Year


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools



The estimation of illuminant color is mandatory for many applications in the field of color image quantification. However, it is an unresolved problem if no additional heuristics or restrictive assumptions apply. Assuming uniformly colored and roundly shaped objects, Lee has presented a theory and a method for computing the scene-illuminant chromaticity from specular highlights [LeeH. C., J. Opt. Soc. Am. A 3, 1694 (1986)]. However, Lee’s method, called image path search, is less robust to noise and is limited in the handling of microtextured surfaces. We introduce a novel approach to estimate the color of a single illuminant for noisy and microtextured images, which frequently occur in real-world scenes. Using dichromatic regions of different colored surfaces, our approach, named color line search, reverses Lee’s strategy of image path search. Reliable color lines are determined directly in the domain of the color diagrams by three steps. First, regions of interest are automatically detected around specular highlights, and local color diagrams are computed. Second, color lines are determined according to the dichromatic reflection model by Hough transform of the color diagrams. Third, a consistency check is applied by a corresponding path search in the image domain. Our method is evaluated on 40 natural images of fruit and vegetables. In comparison with those of Lee’s method, accuracy and stability are substantially improved. In addition, the color line search approach can easily be extended to scenes of objects with macrotextured surfaces.

© 2001 Optical Society of America

OCIS Codes
(100.2000) Image processing : Digital image processing
(150.2950) Machine vision : Illumination
(330.1690) Vision, color, and visual optics : Color

Original Manuscript: January 2, 2001
Revised Manuscript: April 23, 2001
Manuscript Accepted: April 24, 2001
Published: November 1, 2001

Thomas M. Lehmann and Christoph Palm, "Color line search for illuminant estimation in real-world scenes," J. Opt. Soc. Am. A 18, 2679-2691 (2001)

Sort:  Author  |  Year  |  Journal  |  Reset  


  1. D. H. Brainard, “Color constancy in the nearly natural image. 2. Achromatic loci,” J. Opt. Soc. Am. A 15, 307–325 (1998). [CrossRef]
  2. I. Kuriki, K. Uchikawa, “Limitations of surface-color and apparent-color constancy,” J. Opt. Soc. Am. A 13, 1622–1636 (1996). [CrossRef]
  3. K.-H. Bäuml, “Color constancy: the role of image surface in illuminant adjustment,” J. Opt. Soc. Am. A 16, 1521–1530 (1999). [CrossRef]
  4. E. W. Jin, S. K. Shevell, “Color memory and color constancy,” J. Opt. Soc. Am. A 13, 1981–1991 (1996). [CrossRef]
  5. M. D’Zmura, P. Lennie, “Mechanisms of color constancy,” J. Opt. Soc. Am. A 3, 1662–1672 (1986). [CrossRef] [PubMed]
  6. M. D’Zmura, G. Iverson, “Color constancy. III. General linear recovery of spectral descriptions for lights and surfaces,” J. Opt. Soc. Am. A 11, 2389–2400 (1994). [CrossRef]
  7. D. H. Brainard, W. T. Freeman, “Bayesian color constancy,” J. Opt. Soc. Am. A 14, 1393–1411 (1997). [CrossRef]
  8. S. Tominaga, “Multichannel vision system for estimating surface and illuminant functions,” J. Opt. Soc. Am. A 13, 2163–2173 (1996). [CrossRef]
  9. J. Ho, B. V. Funt, M. S. Drew, “Separating a color signal into illumination and surface reflectance components: theory and applications,” IEEE Trans. Pattern Anal. Mach. Intell. 12, 966–977 (1990). [CrossRef]
  10. S. Tominaga, B. A. Wandell, “Standard surface-reflection model and illuminant estimation,” J. Opt. Soc. Am. A 6, 576–584 (1989). [CrossRef]
  11. S. Tominaga, B. A. Wandell, “Component estimation of surface spectral reflectance,” J. Opt. Soc. Am. A 7, 312–317 (1990). [CrossRef]
  12. S. Tominaga, “Surface identification using the dichromatic reflection model,” IEEE Trans. Pattern Anal. Mach. Intell. 13, 658–670 (1991). [CrossRef]
  13. G. Healey, D. Slater, “Global color constancy: recognition of objects by use of illumination-invariant properties of color distributions,” J. Opt. Soc. Am. A 11, 3003–3010 (1994). [CrossRef]
  14. B. V. Funt, G. D. Finlayson, “Color constant color indexing,” IEEE Trans. Pattern Anal. Mach. Intell. 17, 522–533 (1995). [CrossRef]
  15. L. T. Maloney, B. A. Wandell, “Color constancy: a method for recovering surface spectral reflectances,” J. Opt. Soc. Am. A 3, 29–33 (1986). [CrossRef] [PubMed]
  16. C. L. Novak, S. A. Shafer, “Supervised color constancy using a color chart,” (School of Computer Science, Carnegie Mellon University, Pittsburgh, Pa., 1990).
  17. C. Palm, I. Scholl, T. Lehmann, K. Spitzer, “Quantitative color measurement in laryngoscopic images,” in POSTER98 (Faculty of Electrical Engineering, Czech Technical University, Prague, 1998), Paper NS22.
  18. H. Hassan, J. Ilgner, C. Palm, T. Lehmann, K. Spitzer, M. Westhofen, “Objective judgement in laryngoscopic images,” in Advances in Quantitative Laryngoscopy, Voice and Speech Research, T. Lehmann, C. Palm, K. Spitzer, T. Tolxdorff, eds. (RWTH, Aachen, Germany, 1998), pp. 135–142.
  19. G. Buchsbaum, “A spatial processor model for object colour perception,” J. Franklin Inst. 300, 1–26 (1980). [CrossRef]
  20. E. H. Land, “Recent advances in retinex theory,” Vision Res. 26, 7–21 (1986). [CrossRef] [PubMed]
  21. H.-C. Lee, “Method for computing scene-illuminant chromaticity from specular highlights,” J. Opt. Soc. Am. A 3, 1694–1699 (1986). [CrossRef] [PubMed]
  22. S. A. Shafer, “Using color to separate reflection components,” Color Res. Appl. 10, 210–218 (1985). [CrossRef]
  23. M. H. Brill, “Image segmentation by object color: a unifying framework and connection to color constancy,” J. Opt. Soc. Am. A 7, 2041–2047 (1990). [CrossRef] [PubMed]
  24. B. V. Funt, M. S. Drew, “Color space analysis of mutual illumination,” IEEE Trans. Pattern Anal. Mach. Intell. 15, 1319–1326 (1993). [CrossRef]
  25. V. F. Leavers, Shape Detection in Computer Vision Using the Hough Transform (Springer-Verlag, Berlin, 1992).
  26. Y.-L. Tian, H. T. Tsui, “Shape from shading for non-Lambertian surfaces from one color image,” in Proceedings of the 13th International Conference on Pattern Recognition (IEEE Computer Society, Los Alamitos, Calif., 1996), Vol. 1, pp. 258–262.
  27. B. Funt, K. Barnard, L. Martin, “Is machine colour constancy good enough?” in Proceedings of the 5th European Conference on Computer Vision (Springer-Verlag, Berlin, 1998), Vol. 1, pp. 445–459.

Cited By

Alert me when this paper is cited

OSA is able to provide readers links to articles that cite this paper by participating in CrossRef's Cited-By Linking service. CrossRef includes content from more than 3000 publishers and societies. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.

« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited