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Based on the concepts of dynamic field theory (DFT), we present an architecture that autonomously generates scene representations by controlling gaze and attention, creating visual objects in the foreground, tracking objects, reading them into working memory, and taking into account their visibility. At the core of this architecture are three-dimensional dynamic neural fields (DNFs) that link feature...
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...
We present an architecture based on the Dynamic Field Theory for the problem of scene representation. At the core of this architecture are three-dimensional neural fields linking feature to spatial information. These three-dimensional fields are coupled to lower-dimensional fields that provide both a close link to the sensory surface and a close link to motor behavior. We highlight the updating mechanism...
We present a robotic vision system for object recognition, pose estimation and fast object learning. Our approach uses the Dynamic Neural Field Theory to combine bottom-up recognition of matching patterns and top-down estimation of pose parameters in a recurrent loop. Because Dynamic Neural Fields provide the system with stabilized percepts that still track changes in the incoming sensory stream,...
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...
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