Visual Selective Attention Model for Robot Vision

摘要

This paper describes a model of visual selective attention, called NLOOK, proposed to be used in computational and robotic vision systems. This model first decomposes the visual input in a set of topographic feature maps which encode intensity, orientation, color and movement. All feature maps feed into a master ldquosaliency maprdquo, which topographically codifies for local conspicuity over the entire visual scene, and a winner-take-all neural network with an inhibition of return mechanism that selects the most salient points of the map in decreasing order. The obtained results demonstrate that the proposed model is suitable for robotic vision systems.