Related Concepts
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...
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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
Zheng Q, Chellappa R (1991) Estimation of illuminant direction, albedo, and shape from shading. IEEE Trans Pattern Anal Mach Intell 13:680–702
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
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
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
Ramamoorthi R, Hanrahan P (2001) A signal-processing framework for inverse rendering. In: Proceedings of ACM SIGGRAPH. ACM, New York, pp 117–128
Zhang Y, Yang YH (2001) Multiple illuminant direction detection with application to image synthesis. IEEE Trans Pattern Anal Mach Intell 23:915–920
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
Basri R, Jacobs D, Kemelmacher I (2007) Photometric stereo with general, unknown lighting. Int J Comput Vis 72:239–257
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
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
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
Sato I, Sato Y, Ikeuchi K (2003) Illumination from shadows. IEEE Trans Pattern Anal Mach Intell 25:290–300
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
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
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
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
Nishino K, Belhumeur P, Nayar S (2005) Using eye reflections for face recognition under varying illumination. Proc Int Conf Comput Vis I:519–526
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
Buchsbaum G (1980) A spatial processor model for object colour perception. J Franklin Inst 310:1–26
Land EH (1977) The retinex theory of color vision. Sci Am 237:108–128
Brainard DH, Freeman WT (1997) Bayesian color constancy. J Opt Soc Am A 14:1393–1411
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
Forsyth DA (1990) A novel algorithm for colour constancy. Int J Comput Vis 5:5–36
Finlayson GD, Hordley S, Tastl I (2006) Gamut constrained illuminant estimation. Int J Comput Vis 67:93–109
Ebner M (2007) Color constancy. Wiley, Chichester
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
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
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
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer Science+Business Media New York
About this entry
Cite this entry
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
Download citation
DOI: https://doi.org/10.1007/978-0-387-31439-6_516
Published:
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-30771-8
Online ISBN: 978-0-387-31439-6
eBook Packages: Computer ScienceReference Module Computer Science and Engineering