Modeling visual attention via selective tuning
Artificial Intelligence - Special volume on computer vision
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bottom-Up Visual Attention for Virtual Human Animation
CASA '03 Proceedings of the 16th International Conference on Computer Animation and Social Agents (CASA 2003)
Object-based Visual Attention: a Model for a Behaving Robot
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Using visual attention to extract regions of interest in the context of image retrieval
Proceedings of the 44th annual Southeast regional conference
A Bimodal Laser-Based Attention System
Computer Vision and Image Understanding - Special issue: Attention and performance in computer vision
MAPS: multiscale attention-based presegmentation of color images
Scale Space'03 Proceedings of the 4th international conference on Scale space methods in computer vision
Is bottom-up attention useful for object recognition?
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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Visual attention is a key visual process of selection of information, with a wide variety of applications in vision systems and image processing. In this paper we hold and test a novel model based on the Koch & Ullman architecture of saliency-based attention. We propose a novel feature from maximum local energy, already used in edge and feature extraction, but in ways which aren't suitable in attention applications. The envelope of a bank of log Gabor filters (resembling the receptive fields of complex cells from visual cortex) is taken as a local energy measure. In our approach to the feature integration problem, across the scales of each orientation, we compute initial conspicuity maps employing the T2 Hotelling statistic, as a multivariate measure of variance, and taking by this way conspicuous points with maximum local energy. Normalizing and integrating these maps, gathering the highest values of variance, we obtain a unique final saliency measure. With this model we achieve improved results accounting for orientation pop-out and equivalent performance in a visual search task within cluttered natural scenes, both in comparison with analogue experiments previously published with a state of art model.