Theoretical analysis of illumination in PCA-based vision systems
In this paper, we address the problem: How to account for arbitrary illumination effects for a pose of an object in PCA-based vision systems. This is a key problem since after solving this problem, the approach can be applied directly to an arbitrary number of poses of an arbitrary number of objects. We solve this problem by first generating an analytic closed-form formula of the covariance matrix for a special lighting condition. Then after analyzing all possible illumination effects, an equation called the illumination equation is derived to account for arbitrary illumination effects. Experiments on simulated conditions and real world conditions confirm the advantages of our new methods. A direct application of current research is that for any pose, our method can be used to compress the image of the object in any possible illumination. This is demonstrated in the real world experiment in the paper. Furthermore, this paper gives a new framework on how to address illumination effects in computer vision in general. (C) 1999 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
illumination effects interreflection light intensity change light sources mosaic image method principal component analysis radiosity method