Evaluation of visual attention models under 2D similarity transformations
Proceedings of the 2009 ACM symposium on Applied Computing
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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 “saliency map”, 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.