Modeling visual attention via selective tuning
Artificial Intelligence - Special volume on computer vision
Dynamic relevance: vision-based focus of attention using artificial neural networks
Artificial Intelligence - Special issue on relevance
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object-based visual attention for computer vision
Artificial Intelligence
Robotics and Autonomous Systems
Incremental Knowledge Representation Based on Visual Selective Attention
Neural Information Processing
Implementation of visual attention system using bottom-up saliency map model
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Cooperative aspects of selective attention
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
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This paper presents a novel "hierarchical selectivity" mechanism for object-based visual attention. This mechanism integrates visual salience from bottom-up groupings and the top-down attentional setting. Under its guidance, covert visual attention can shift not only from one grouping to another but also from a grouping to its sub-groupings at a single resolution or multiple varying resolutions. Both object-based and space-based selection is integrated to give a visual attention mechanism that has multiple and hierarchical selectivity.