A Simple Strategy for Calibrating the Geometry of Light Sources

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/spl plusmn/4/spl deg/ at /spl alpha/=.05 (6 percent relative) of its true direction and within 0.13/spl plusmn/.02 m at /spl 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]  Graham D. Finlayson,et al.  Color in Perspective , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Lawrence B. Wolff,et al.  3 D shape and light source location from depth and reflectance , 1992, Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[3]  Linda G. Shapiro,et al.  Computer and Robot Vision , 1991 .

[4]  Steven A. Shafer,et al.  Using color to separate reflection components , 1985 .

[5]  Glenn Healey,et al.  Markov Random Field Models for Unsupervised Segmentation of Textured Color Images , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  L. Maloney,et al.  Color constancy: a method for recovering surface spectral reflectance. , 1986, Journal of the Optical Society of America. A, Optics and image science.

[7]  J. Röning,et al.  Application of machine vision to assess involved surface in patients with psoriasis , 1997, The British journal of dermatology.

[8]  Narendra Ahuja,et al.  Estimation of the light source distribution and its use in integrated shape recovery from stereo and shading , 1993, 1993 (4th) International Conference on Computer Vision.

[9]  Azriel Rosenfeld,et al.  Improved Methods of Estimating Shape from Shading Using the Light Source Coordinate System , 1985, Artif. Intell..

[10]  Glenn Healey,et al.  Computing illumination-invariant descriptors of spatially filtered color image regions , 1997, IEEE Trans. Image Process..

[11]  Emanuele Trucco,et al.  Computer and Robot Vision , 1995 .

[12]  Michael J. Brooks,et al.  Shape and Source from Shading , 1985, IJCAI.

[13]  E H Land,et al.  Recent advances in retinex theory and some implications for cortical computations: color vision and the natural image. , 1983, Proceedings of the National Academy of Sciences of the United States of America.

[14]  D H Brainard,et al.  Bayesian color constancy. , 1997, Journal of the Optical Society of America. A, Optics, image science, and vision.

[15]  S. Tominaga.  Surface reflectance estimation by the dichromatic model , 1996 .

[16]  Kosuke Sato,et al.  Determining reflectance parameters using range and brightness images , 1990, [1990] Proceedings Third International Conference on Computer Vision.

[17]  B. Wandell,et al.  Standard surface-reflectance model and illuminant estimation , 1989 .

[18]  Robert A. McLaughlin,et al.  Randomized Hough Transform: Improved ellipse detection with comparison , 1998, Pattern Recognit. Lett..

[19]  Rama Chellappa,et al.  Estimation of illuminant direction, albedo, and shape from shading , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.