Signature Random Fields for Accommodating Illumination Variability

In this paper, we document an extension to traditional pattern-theoretic object templates to jointly accommodate variations in object pose and in the radiant appearance of the object surface. We first review classical object templates accommodating pose variation. We then develop an efficient subspace representation for the object radiance indexed on the surface of the three dimensional object template. We integrate the low-dimensional representation for the object radiance, or signature, into the pattern-theoretic template, and present the results of orientation estimation experiments. The experiments demonstrate both estimation performance fluctuations under varying illumination conditions and performance degradations associated with unknown scene illumination. We also present a Bayesian approach for estimation accommodating illumination variability.