A Qualia Framework for Ladar 3D Object Classification

LADAR provides 3D shape information that has yet to be fully exploited for object recognition or classification. This is partly due to the operating conditions, but mostly due to a representational gap in computational intelligence. This paper briefly explores some of the hurdles of object classification using LADAR data and proposes a theoretical framework, based on the biological inspiration of qualia, we believe will allow us to address these operating conditions and, most importantly, this representational gap. Our framework works on concepts instead of parts, and iterates a top-down and bottom-up solution that updates the hypothesis with the accrual of evidence. This creates a system that we believe will generalize concepts, learn from experience, and even recognize the need for the addition of new classes based on its current world view.