Task-dependent learning of attention
Neural Networks
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Development of a Biologically Inspired Real-Time Visual Attention System
BMVC '00 Proceedings of the First IEEE International Workshop on Biologically Motivated Computer Vision
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
Generating saliency maps using human based computation agents
CAEPIA'09 Proceedings of the Current topics in artificial intelligence, and 13th conference on Spanish association for artificial intelligence
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In this paper a new method of automatically detecting perceptually important regions in an image is described. The method uses bottom-up components of human visual attention, and includes the following three components : i) several feature maps known to influence human visual attention, which are computed in parallel directly from the original input image, ii) importance maps, each of which has the measure of "perceptual importance" of local regions of pixels in each corresponding feature map, and are computed based on lateral inhibition scheme, iii) single saliency map, integrated across multiple importance maps based on a simple iterative non-linear mechanism which uses statistical information and local competence of pixels in importance maps. The performance of the system was evaluated over some synthetic and complex real images. Experimental results indicate that our method correlates well with human perception of visually important regions.