Abstract:
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 i...Show MoreMetadata
Abstract:
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.
Date of Conference: 18-22 October 2010
Date Added to IEEE Xplore: 03 December 2010
ISBN Information: