Abstract
This paper proposes a method for estimating the scene illuminant spectral power distributions of multiple light sources under a complex illumination environment. The spectral power distributions including natural and artificial illuminants are estimated based on the image data from a high-dimensional spectral imaging system. We note that specular highlights on inhomogeneous dielectric object surfaces includes much information about scene illumination according to the dichromatic reflection model. First, we describe several methods for detecting specular highlight areas. We assume a curved object surface illuminated by multiple light sources from different directions. Then we estimate the illuminant spectrum of each light source from the image data of that highlight area. Based on this principle, we present an algorithm to estimate multiple illuminants. The feasibility of the proposed method is shown in experiments.
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Tominaga, S.: Multichannel vision system for estimating surface and illumination functions. J. Optical Society of America A 13(11), 2163–2173 (1996)
Tominaga, S., Wandell, B.A.: Standard surface-reflectance model and illuminant estimation. J. Optical Society of America A 6(4), 576–584 (1986)
Maloney, L.T.: Evaluation of linear models of surface spectral reflectance with small numbers of parameters. J. of the Optical Society of America A 3(10), 1673–1683 (1986)
Maloney, L.T., Wandell, B.A.: Color constancy: a method for recovering surface spectral reflectance. J. Optical Society of America A 3(1), 29–33 (1986)
Tominaga, S., Haraguchi, H.: Estimation of fluorescent scene illuminant by a spectral camera system. In: Color Imaging X: Processing, Hardcopy, and Applications, San Jose, Calif, USA. Proceedings of SPIE, vol. 5667, pp. 128–135 (2005)
Tominaga, S.N., Tanaka, N.: Feature article: omnidirectional scene illuminant estimation using a mirrored ball. Journal of Imaging Science and Technology 50(3), 217–227 (2006)
Schultz, S., Doerschner, K., Maloney, L.T.: Color constancy and hue scaling. Journal of Vision 6(10), 1102–1116 (2006)
Tominaga, S.: Estimation of composite daylight-fluorescent light components based on multi-spectral scene images, In: Proceedings of the 14th IS&T/SID Color Imaging Conference, Scottsdale, Ariz, USA, pp.125–130 (2006)
van de Weijer, J., Gevers, T., Gijsenij, A.: Edge-based color constancy. IEEE Transactions on Image Processing 16(9), 2207–2214 (2007)
Zhou, W., Kambhamettu, C.: A unified framework for scene illuminant estimation. Image and Vision Computing 26(3), 415–429 (2008)
Klinker, G.J., Shafer, S.A., Kanade, T.: The Measurement of Highlights in Color Images. International Journal of Computer Vision 2(1), 7–26 (1992)
Tan, R.T., Nishino, K., Ikeuchi, K.: Separating Reflection Components Based on Chromaticity and Noise Analysis. IEEE Transaction on Pattern Analysis and Machine Intelligence 26(10), 1373–1379 (2004)
Xu, S.C., Ye, X., Wu, Y., Zhang, S.: Highlight detection and removal based on chromaticity. In: Kamel, M.S., Campilho, A.C. (eds.) ICIAR 2005. LNCS, vol. 3656, pp. 199–206. Springer, Heidelberg (2005)
Angelopoulou, E.: Specular Highlight Detection Based on the Fresnel Reflection Coefficient. In: IEEE 11th International Conference on Computer Vision, pp. 1–8 (2007)
Buchsbaum, G.: A spatial processor model for object colour perception. J. Franklin Institute 310(1), 1–26 (1980)
Tominaga, S., Kimachi, A.: Polarization imaging for material classification. Optical Engineering 47(12), 123201 (2008)
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Imai, Y., Kato, Y., Kadoi, H., Horiuchi, T., Tominaga, S. (2011). Estimation of Multiple Illuminants Based on Specular Highlight Detection. In: Schettini, R., Tominaga, S., Trémeau, A. (eds) Computational Color Imaging. CCIW 2011. Lecture Notes in Computer Science, vol 6626. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20404-3_7
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DOI: https://doi.org/10.1007/978-3-642-20404-3_7
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