Integrating Selective Attention and Space-Variant Sensing in Machine Vision

Studies on visual perception have demonstrated that selective attention mechanisms and space-variant sensing are powerful tools for focusing available computing resources to the process of relevant data. In this paper an overall architecture for an active, anthropomorphic robot vision system which integrates retina-like sensing and attention mechanisms is proposed. Gaze direction is shifted both on the basis of sensory and semantic characteristics of the visual input, which are extracted separately by means of a parallel and serial analysis. An implementation of the system by means of optical flow and neural network techniques is described, and the results of its application are discussed.

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