A neuro-dynamic architecture for one shot learning of objects that uses both bottom-up recognition and top-down prediction

Learning to recognize objects from a small number of example views is a difficult problem of robot vision, of particular importance to assistance robots who are taught by human users. Here we present an approach that combines bottom-up recognition of matching patterns and top-down estimation of pose parameters in a recurrent loop that improves on previous efforts to reconcile invariance of recognition under view changes with discrimination among different objects. We demonstrate and evaluate the approach both in a service robotics implementation as well as on the COIL database. The robotic implementation highlights features of our approach that enable real-time pose tracking as well as recognition from views where figure ground segmentation is difficult.