Publication 2001 Issue No. 9 - September Abstract - A Simple Strategy for Calibrating the Geometry of Light Sources
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A Simple Strategy for Calibrating the Geometry of Light Sources
September 2001 (vol. 23 no. 9)
pp. 1022-1027
 ASCII Text x M.W. Powell, S. Sarkar, D. Goldgof, "A Simple Strategy for Calibrating the Geometry of Light Sources," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 9, pp. 1022-1027, September, 2001.
 BibTex x @article{ 10.1109/34.955114,author = {M.W. Powell and S. Sarkar and D. Goldgof},title = {A Simple Strategy for Calibrating the Geometry of Light Sources},journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},volume = {23},number = {9},issn = {0162-8828},year = {2001},pages = {1022-1027},doi = {http://doi.ieeecomputersociety.org/10.1109/34.955114},publisher = {IEEE Computer Society},address = {Los Alamitos, CA, USA},}
 RefWorks Procite/RefMan/Endnote x TY - JOURJO - IEEE Transactions on Pattern Analysis and Machine IntelligenceTI - A Simple Strategy for Calibrating the Geometry of Light SourcesIS - 9SN - 0162-8828SP1022EP1027EPD - 1022-1027A1 - M.W. Powell, A1 - S. Sarkar, A1 - D. Goldgof, PY - 2001VL - 23JA - IEEE Transactions on Pattern Analysis and Machine IntelligenceER -

—We present a methodology for calibrating multiple light source locations in 3D from images. The procedure involves the use of a novel calibration object that consists of three spheres at known relative positions. The process uses intensity images to find the positions of the light sources. We conducted experiments to locate light sources in 51 different positions in a laboratory setting. Our data shows that the vector from a point in the scene to a light source can be measured to within $2.7 \pm .4 ^{\circ }$ at $\alpha =.05$ (6 percent relative) of its true direction and within $0.13 \pm .02$ m at $\alpha =.05$ (9 percent relative) of its true magnitude compared to empirically measured ground truth. Finally, we demonstrate how light source information is used for color correction.

[1] L. Savolainen, J. Kontinen, J. Roning, and A. Oikarinen, “Application of Machine Vision to Assess Involved Surface in Patients with Psoriasis,” British J. Dermatology, vol. 137, no. 3, pp. 395-400, Sept. 1997.
[2] G. Healey and D. Slater, “Computing Illumination-Invariant Descriptors of Spatially Filtered Color Image Regions,” IEEE Trans. Image Processing, vol. 6, no. 7 pp. 1,002-1,013, 1997.
[3] D.K. Panjwani and G. Healey, “Markov Random Field Models for Unsupervised Segmentation of Textured Color Images,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 17, pp. 939-954, 1995.
[4] D.H. Brainard and W.T. Freeman, “Bayesian Color Constancy,” J. Optical Soc. Am., vol. 14, no. 7, pp.1393-1411, 1997.
[5] M.M. D'Zmura and G. Iverson, “Probabilistic Color Constancy,” Geometric Representations of Perceptual Phenomena: Papers in Honor of Tarow Indow's 70th Birthday, M.M. D'Zmura, D. Hoffman, G. Iverson, and K. Romeny, eds. Laurence Relbaum Assoc., 1994.
[6] G.D. Finlayson, “Color in Perspective,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 18, no. 10, pp. 1,034-1,038, Oct. 1996.
[7] D.A. Forsyth, “A Novel Algorithm for Color Constancy,” Int'l J. Computer Vision, vol. 5, no. 1, pp. 5-36, 1990.
[8] E.H. Land, “Recent Advances in Retinex Theory and Some Implications for Cortical Computations: Color Vision and the Natural Image,” Proc. Nat'l Academy Science USA, vol. 80, pp. 5163-5169, 1983.
[9] L.T. Maloney and B.A. Wandell, “Color Constancy: A Method for Recovering Surface Spectral Reflectance,” J. Optical Soc. Am. vol. 3, no. 1, pp. 29-33, 1986.
[10] S.A. Shafer, “Using Color to Separate Reflection Components,” Color Research and Application, vol. 10, pp. 210-218, 1985.
[11] S. Tominaga, “Surface Reflectance Estimation by the Dichromatic Model,” Color Research and Application, vol. 21, no. 2, pp. 104-114, 1996.
[12] S. Tominaga and B.A. Wandell, “Standard Surface-Reflective Model and Illuminant Estimation,” J. Optical Soc. Am., vol. 6, no. 4, pp. 576-584, 1996.
[13] T.A. Mancini and L.B. Wolff, “3D Shape and Light Source Location from Depth and Reflectance,” IEEE Computer Vision Pattern Recognition, pp. 707-709, 1992.
[14] D.H. Hougen and N. Ahuja, Estimation of the Light Source Distribution and Its Use in Integrated Shape Recovery from Stereo and Shading Proc. IEEE Fourth Int'l Conf. Computer Vision, pp. 148-155, May 1993.
[15] M.J. Brooks and B.K. P Horn, “Shape and Source from Shading,” Proc Sixth Int'l Joint Conf. Artificial Intelligence, pp. 932-936, 1985.
[16] K. Ikeuchi and K. Sato, “Determining Reflectance Using Range and Brightness Images,” Proc. IEEE Int'l Conf. Computer Vision’90, pp. 12-20, 1990.
[17] C.H. Lee and A. Rosenfeld, “Improved Methods of Estimating Shape from Shading Using the Light Source Coordinate System,” Artifical Intelligence, vol. 26, no. 2, pp. 125-143, 1985.
[18] A.P. Pentland, “Linear Shape From Shading,” Int'l J. Computer Vision, vol. 4, no. 2, pp. 153-162, 1990.
[19] Q. Zheng and R. Chellappa, Estimation of Illuminant Direction, Albedo, and Shape from Shading IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 13, no. 7, pp. 680-702, July 1991.
[20] R.M. Haralick and L.G. Shapiro, Computer and Robot Vision. New York: Addison-Wesley, 1993.
[21] R.A. McLaughlin, “Randomized Hough Transform: Improved Ellipse Detection with Comparison,” PR Letters, vol. 19, pp. 299-305, 1998.

Citation:
M.W. Powell, S. Sarkar, D. Goldgof, "A Simple Strategy for Calibrating the Geometry of Light Sources," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 9, pp. 1022-1027, Sept. 2001, doi:10.1109/34.955114