Low-dimensional representations of shaded surfaces under varying illumination

The aim of this paper is to find the best representation for the appearance of surfaces with Lambertian reflectance under varying illumination. Previous work using principal component analysis (PCA) found the best sub-space to represent all images of an object under a varying point light source. We extend this to images from any illumination distribution. Specifically we calculate the bases for all configurations of a point plus ambient light source and two point light sources, as well as from a database of captured real world illumination. We also reformulate the optimization criterion used in PCA. The resulting basis, we believe has higher representability and is better for analyzing images of shaded objects. The different bases are compared on a database of images to test the representability.

[1]  Paul E. Debevec,et al.  Rendering synthetic objects into real scenes: bridging traditional and image-based graphics with global illumination and high dynamic range photography , 1998, SIGGRAPH '08.

[2]  G. Chirikjian,et al.  Engineering Applications of Noncommutative Harmonic Analysis: With Emphasis on Rotation and Motion Groups , 2000 .

[3]  Russell A. Epstein,et al.  5/spl plusmn/2 eigenimages suffice: an empirical investigation of low-dimensional lighting models , 1995, Proceedings of the Workshop on Physics-Based Modeling in Computer Vision.

[4]  Steven W. Zucker,et al.  Finding folds: on the appearance and identification of occlusion , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[5]  David J. Kriegman,et al.  What Is the Set of Images of an Object Under All Possible Illumination Conditions? , 1998, International Journal of Computer Vision.

[6]  Ravi Ramamoorthi,et al.  Analytic PCA Construction for Theoretical Analysis of Lighting Variability in Images of a Lambertian Object , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Ronen Basri,et al.  Lambertian reflectance and linear subspaces , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[8]  Charles E. Heckler,et al.  Applied Multivariate Statistical Analysis , 2005, Technometrics.

[9]  P. Hanrahan,et al.  On the relationship between radiance and irradiance: determining the illumination from images of a convex Lambertian object. , 2001, Journal of the Optical Society of America. A, Optics, image science, and vision.

[10]  Peter W. Hallinan.  A low-dimensional representation of human faces for arbitrary lighting conditions , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.