Probabilistic eigenspace object recognition in the presence of occlusion

grantor: University of TorontoEigenspace approaches to object recognition have achieved impressive results in constrained environments. In general, however, these techniques tend to be brittle, as they are not naturally robust to changes in object scale, affine object transformations in an image, object occlusion, or background clutter. In this thesis, we present a novel approach to handling occlusions and background clutter when using an eigenspace approach to object recognition. The method presented involves an intuitive probabilistic formulation that exploits local information intrinsic to the eigenspace model. In addition, we explore the usefulness of enforcing local image constraints during fitting. A general, efficient, intrinsically parallel, search procedure is proposed that uses the framework. Experimental results indicate that the technique can reliably handle much higher degrees of occlusion, in comparison to previous techniques.M.Sc