One-click white balance using human skin reflectance
Authors: 
Jeremy Long, Amy A. GoochAuthors Info & Claims
Jeremy Long, Amy A. GoochAuthors Info & ClaimsPages 55 - 62
Abstract
Existing methods for white balancing photographs tend to rely on skilled interaction from the user, which is prohibitive for most amateur photographers. We propose a minimal interaction system for white balancing photographs that contain humans. Many of the pictures taken by amateur photographers fall into this category. Our system matches a user-selected patch of skin in a photograph to an entry in a skin reflectance function database. The estimate of the illuminant that emerges from the skin matching can be used to white balance the photograph, allowing users to compensate for biased illumination in an image with a single click. We compare the quality of our results to output from three other low-interaction methods, including commercial approaches such as Google Picasa's one-click relighting [19], a whitepoint-based algorithm [16], and Ebner's localized gray-world algorithm [7]. The comparisons indicate that our approach offers several advantages for amateur photographers.
References
[1]
Adobe. http://www.adobe.com/products/photoshop/main.html. 2008.
[2]
L. Arend and A. Reeves. Simultaneous color constancy. Optical Soc. Am., 3(10):1743--1751, 1986.
[3]
K. Barnard. Improvements to gamut mapping colour constancy algorithms. In ECCV (1), pages 390--403, 2000.
[4]
D. H. Brainard and W. T. Freeman. Bayesian color constancy. J. Opt. Soc. Am. A, 14(7):1393--1411, 1997.
[5]
G. Buchsbaum. A spatial processor model for object colour perception. Journal of the Franklin Institute, (310): 1--26, 1980.
[6]
H. Y. Chong, S. J. Gortler, and T. Zickler. A perception-based color space for illumination-invariant image processing. In SIGGRAPH '08: ACM SIGGRAPH 2008 papers, pages 1--7, New York, NY, USA, 2008. ACM.
[7]
M. Ebner. Color constancy using local color shifts. pages Vol III: 276--287, 2004.
[8]
G. D. Finlayson and B. V. Funt. Colour constancy using shadows. Perception, 23:89--90, 1994.
[9]
G. D. Finlayson, S. D. Hordley, and P. M. Hubel. Color by correlation: A simple, unifying framework for color constancy. IEEE Trans. Pattern Anal. Mach. Intell., 23(11):1209--1221, 2001.
[10]
G. D. Finlayson, S. D. Hordley, and I. Tastl. Gamut constrained illuminant estimation. Int. J. Comput. Vision, 67(1):93--109, 2006.
[11]
D. A. Forsyth. A novel algorithm for color constancy. Int. J. Comput. Vision, 5(1):5--36, 1990.
[12]
R. Gershon, A. Jepson, and J. Tsotsos. From {R, G, B} to surface reflectance: Computing color constant descriptors in images. Perception, pages 755--758, 1988.
[13]
E. Hsu, T. Mertens, S. Paris, S. Avidan, and F. Durand. Light mixture estimation for spatially varying white balance. In SIGGRAPH '08: ACM SIGGRAPH 2008 papers, pages 1--7, New York, NY, USA, 2008. ACM.
[14]
J. T. Kajiya. The rendering equation. In SIGGRAPH '86: Proceedings of the 13th annual conference on Computer graphics and interactive techniques, pages 143--150, New York, NY, USA, 1986. ACM.
[15]
J. Kuang, G. M. Johnson, and M. D. Fairchild. icam06: A refined image appearance model for hdr image rendering. J. Vis. Comun. Image Represent., 18(5):406--414, 2007.
[16]
E. Land. The retinex theory of color constancy. Scientific Am., pages 108--129, 1977.
[17]
H.-C. Lee. Estimating the illuminant color from the shading of a smooth surface. Technical report, Cambridge, MA, USA, 1988.
[18]
G. Petschnigg, R. Szeliski, M. Agrawala, M. Cohen, H. Hoppe, and K. Toyama. Digital photography with flash and no-flash image pairs. ACM Trans. Graph., 23(3):664--672, 2004.
[19]
G. Picasa. http://picasa.google.com/. 2008.
[20]
E. Reinhard, M. Ashikhmin, B. Gooch, and P. Shirley. Color transfer between images. IEEE Comput. Graph. Appl., 21(5):34--41, 2001.
[21]
Shutterfly. http://www.shutterfly.com/. 2008.
[22]
R. Stanikunas, H. Vaitkevicius, and J. J. Kulikowski. Investigation of color constancy with a neural network. Neural Netw., 17(3):327--337, 2004.
[23]
M. Storring, H. J. Andersen, and E. Granum. Estimation of the illuminant colour from human skin colour. In Proceedings of the Int. Conference on Automatic Face and Gesture Recognition (FG '00, pages 64--69, 2000.
[24]
E. Talvala, A. Adams, M. Horowitz, and M. Levoy. Veiling glare in high dynamic range imaging. In SIGGRAPH '07: ACM SIGGRAPH 2007papers, page 37, New York, NY, USA, 2007. ACM.
[25]
A. Veeraraghavan, R. Raskar, A. Agrawal, A. Mohan, and J. Tumblin. Dappled photography: mask enhanced cameras for heterodyned light fields and coded aperture refocusing. In SIGGRAPH '07: ACM SIGGRAPH 2007 papers, page 69, New York, NY, USA, 2007. ACM.
[26]
G. Ward and E. Eydelberg-Vileshin. Picture perfect rgb rendering using spectral prefiltering and sharp color primaries. In EGRW '02: Proceedings of the 13th Eurographics workshop on Rendering, pages 117--124, Aire-la-Ville, Switzerland, Switzerland, 2002. Eurographics Association.
[27]
Y. Weiss. Deriving intrinsic images from image sequences, pages 68--75, 2001.
[28]
T. Weyrich, W. Matusik, H. Pfister, B. Bickel, C. Donner, C. Tu, J. McAndless, J. Lee, A. Ngan, H. W. Jensen, and M. Gross. Analysis of human faces using a measurement-based skin reflectance model. ACM Trans. Graph., 25(3):1013--1024, 2006.
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- One-click white balance using human skin reflectance
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- The Canadian Human-Computer Communications Society / Société Canadienne du Dialogue Humaine Machine (CHCCS/SCDHM)
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Canadian Information Processing Society
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Published: 25 May 2009
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