Visit: an efficient computational model of human visual attention
Visit: an efficient computational model of human visual attention
Toward a computational model of visual attention
Early vision and beyond
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Attentional scene segmentation: integrating depth and motion
Computer Vision and Image Understanding
A Real Time Implementation of the Saliency-Based Model of Visual Attention on a SIMD Architecture
Proceedings of the 24th DAGM Symposium on Pattern Recognition
A space variant mapping architecture for reliable car segmentation
ARC'07 Proceedings of the 3rd international conference on Reconfigurable computing: architectures, tools and applications
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Visual attention is the ability to rapidly detect the interesting parts of a given scene on which higher level computer vision tasks can focus. This paper reports a computational model of dynamic visual attention which combines static and dynamic features to detect salient locations in natural image sequences. Therefore, the model computes a map of interest - saliency map - related to static features and a saliency map derived from dynamic scene features and then combines them into a final saliency map, which topographically encodes stimulus saliency. The information provided by the model of attention is then used by a tracking method to attentively track the interesting features in the scene. The experimental results, reported in this work refer to real color image sequences. They clearly validate the reported model of dynamic visual attention and show its usefulness in guiding the tracking task.