A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Integrated Model of Top-Down and Bottom-Up Attention for Optimizing Detection Speed
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
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In this paper, we describe a hybrid human-machine system for searching and detecting Objects of Interest (OI) in imagery. Automated methods for OI detection based on models of human visual attention have received much interest, but are inherently bottom-up and driven by features. Humans fixate on regions of imagery based on a much stronger top-down component. While it may be possible to include some aspects of top-down cognition into these methods, it is difficult to fully capture all aspects of human cognition into an automated algorithm. Our hypothesis is that combination of automated methods with human fixations will provide a better solution than either alone. In this work, we describe a Brain-Enhanced Synergistic Attention (BESA) system that combines models of visual attention with real-time eye fixations from a human for accurate search and detections of OI. We describe two different BESA schemes and provide implementation details. Preliminary studies were conducted to determine the efficacy of the system and initial results are promising. Typical applications of this technology are in surveillance, reconnaissance and intelligence analysis.