Illumination planning for object recognition in structured environments

This paper addresses the problem of illumination planning for robust object recognition in structured environments. Given a set of objects, the goal is to determine the illumination for which the objects are most distinguishable in appearance from each other. For each object, a large number of images is automatically obtained by varying pose and illumination. Images of all objects, together, constitute the planning image set. The planning set is compressed using the Karhunen-Loeve transform to obtain a low-dimensional subspace. For any given illumination, objects are represented as parametrized manifolds in the subspace. The minimum distance between the manifolds of too objects represents the similarity between the objects in the correlation sense. The optimal illumination is therefore one that maximizes the shortest distance between object manifolds. Results produced by the illumination planner heave been used to enhance the performance of an object recognition system