A neuro-dynamic object recognition architecture enhanced by foveal vision and a gaze control mechanism

We present an extension of a neuro-dynamic object recognition system that combines bottom-up recognition of matching patterns and top-down estimation of pose parameters in a recurrent loop. It is extended by an active foveal vision system. Adding the active vision component is easily integrated within the architecture and improves the recognition rate on previous experiments on the COIL-100 database and for scenes where segmentation of objects is not trivial. Furthermore the active component allows to substantially increase the spatial area where objects can be tracked. When objects move faster than visual servoing can track, catch-up saccades are autonomously generated.