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Illumination Estimation, Illuminant Estimation

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Computer Vision
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Related Concepts

Color Constancy; Incident Light Measurement

Definition

The purpose of illumination estimation is to determine the direction, intensity, and/or color of the lighting in a scene. In contrast to direct measurement of lighting, the illumination information is inferred from cues within the scene, without use of a special probe or color calibration chart.

Background

The appearance of objects and scenes can vary considerably with respect to illumination conditions. In [1], differences in face appearance due to lighting were found to be greater than those due to identity. Since such appearance variations can affect the performance of certain computer vision algorithms, much research has focused on illumination estimation, so that lighting can be accounted for in image understanding.

To simplify inference, methods for illumination estimation typically assume that the illumination originates from distant light sources. With this assumption, the illumination can be considered to...

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References

  1. Moses Y, Adini Y, Ullman S (1994) Face recognition: the problem of compensating for changes in illumination direction. In: Proceedings of European conference on computer vision (ECCV). Springer, Heidelberg/Berlin, pp 286–296

    Google Scholar 

  2. Zheng Q, Chellappa R (1991) Estimation of illuminant direction, albedo, and shape from shading. IEEE Trans Pattern Anal Mach Intell 13:680–702

    Article  Google Scholar 

  3. Samaras D, Metaxas D (1999) Coupled lighting direction and shape estimation from single images. In: Proceedings of the international conference on computer vision. IEEE Computer Society, Washington, DC, pp 868–874

    Google Scholar 

  4. Hougen DR, Ahuja N (1993) Estimation of the light source distribution and its use in integrated shape recovery from stereo and shading. In: Proceedings of the international conference on computer vision. IEEE Computer Society, Washington, DC, pp 148–155

    Google Scholar 

  5. Yang Y, Yuille AL (1991) Sources from shading. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR). IEEE Computer Society, Washington, DC, pp 534–539

    Google Scholar 

  6. Ramamoorthi R, Hanrahan P (2001) A signal-processing framework for inverse rendering. In: Proceedings of ACM SIGGRAPH. ACM, New York, pp 117–128

    Google Scholar 

  7. Zhang Y, Yang YH (2001) Multiple illuminant direction detection with application to image synthesis. IEEE Trans Pattern Anal Mach Intell 23:915–920

    Article  Google Scholar 

  8. Wang Y, Samaras D (2002) Estimation of multiple illuminants from a single image of arbitrary known geometry. In: Proceedings of European conference on computer vision (ECCV). Lecture notes in computer science, vol 2352. Springer, Berlin/Heidelberg, pp 272–288

    Google Scholar 

  9. Basri R, Jacobs D, Kemelmacher I (2007) Photometric stereo with general, unknown lighting. Int J Comput Vis 72:239–257

    Article  Google Scholar 

  10. Sato I, Sato Y, Ikeuchi K (1999) Illumination distribution from brightness in shadows: adaptive estimation of illumination distribution with unknown reflectance properties in shadow regions. In: Proceedings of the international conference on computer vision. IEEE Computer Society, Washington, DC, pp 875–883

    Google Scholar 

  11. Sato I, Sato Y, Ikeuchi K (1999) Illumination distribution from shadows. In: Proceeding of the IEEE Conference on computer vision and pattern recognition (CVPR). IEEE Computer Society, Washington, DC, pp 306–312

    Google Scholar 

  12. Sato I, Sato Y, Ikeuchi K (2001) Stability issues in recovering illumination distribution from brightness in shadows. Proc IEEE Conf Comput Vis Pattern Recognit (CVPR) II:400–407

    Google Scholar 

  13. Sato I, Sato Y, Ikeuchi K (2003) Illumination from shadows. IEEE Trans Pattern Anal Mach Intell 25:290–300

    Article  Google Scholar 

  14. Okabe T, Sato I, Sato Y (2004) Spherical harmonics vs. haar wavelets: basis for recovering illumination from cast shadows. Proc IEEE Conf Comput Vis Pattern Recognit (CVPR) I:50–57

    Google Scholar 

  15. Kim T, Hong K (2005) A practical single image based approach for estimating illumination distribution from shadows. In: Proceedings of the international conference on computer vision. IEEE Computer Society, Washington, DC, pp 266–271

    Google Scholar 

  16. Nishino K, Zhang Z, Ikeuchi K (2001) Determining refle- ctance parameters and illumination distribution from sparse set of images for viewdependent image synthesis. In: Proceedings of the international conference on computer vision. IEEE Computer Society, Washington, DC, pp 599–606

    Google Scholar 

  17. Li Y, Lin S, Lu H, Shum HY (2003) Multiple-cue illumination estimation in textured scenes. In: Proceedings of the international conference on computer vision. IEEE Computer Society, Washington, DC, pp 1366–1373

    Google Scholar 

  18. Nishino K, Belhumeur P, Nayar S (2005) Using eye reflections for face recognition under varying illumination. Proc Int Conf Comput Vis I:519–526

    Google Scholar 

  19. Wang H, Lin S, Liu X, Kang SB (2005) Separating reflections in human iris images for illumination estimation. In: Proceedings of the international conference on computer vision. IEEE Computer Society, Washington, DC, pp 1691–1698

    Google Scholar 

  20. Buchsbaum G (1980) A spatial processor model for object colour perception. J Franklin Inst 310:1–26

    Article  Google Scholar 

  21. Land EH (1977) The retinex theory of color vision. Sci Am 237:108–128

    Article  Google Scholar 

  22. Brainard DH, Freeman WT (1997) Bayesian color constancy. J Opt Soc Am A 14:1393–1411

    Article  Google Scholar 

  23. Finlayson GD, Hordley S, Hubel PM (2001) Color by correlation: a simple, unifying framework for color constancy. IEEE Trans Pattern Anal Mach Intell 23:1209–1221

    Article  Google Scholar 

  24. Forsyth DA (1990) A novel algorithm for colour constancy. Int J Comput Vis 5:5–36

    Article  Google Scholar 

  25. Finlayson GD, Hordley S, Tastl I (2006) Gamut constrained illuminant estimation. Int J Comput Vis 67:93–109

    Article  Google Scholar 

  26. Ebner M (2007) Color constancy. Wiley, Chichester

    MATH  Google Scholar 

  27. Sato I, Sato Y, Ikeuchi K (1999) Acquiring a radiance distribution to superimpose virtual objects onto a real scene. IEEE Trans Vis Comput Graph 5:1–12

    Article  Google Scholar 

  28. Lalonde JF, Efros AA, Narasimhan SG (2009) Estimating natural illumination from a single outdoor image. In: Proceedings of the international conference on computer Vision. IEEE Computer Society, Washington, DC

    Google Scholar 

  29. Funt B, Barnard K, Martin L (1998) Is colour constancy good enough? In: Proceedings of the European conference on computer vision (ECCV). Springer, London, pp 445–459

    Google Scholar 

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Lin, S. (2014). Illumination Estimation, Illuminant Estimation. In: Ikeuchi, K. (eds) Computer Vision. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-31439-6_516

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