Modeling visual attention via selective tuning
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Real-time attention for robotic vision
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SCAN: a scalable model of attentional selection
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Lecture Notes in Computer Science
Selective visual attention enables learning and recognition of multiple objects in cluttered scenes
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Evaluation of selective attention under similarity transformations
Computer Vision and Image Understanding - Special issue: Attention and performance in computer vision
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Selective visual attention enables learning and recognition of multiple objects in cluttered scenes
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Computer Vision and Image Understanding - Special issue: Attention and performance in computer vision
Evaluation of selective attention under similarity transformations
Computer Vision and Image Understanding - Special issue: Attention and performance in computer vision
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PReMI '09 Proceedings of the 3rd International Conference on Pattern Recognition and Machine Intelligence
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ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
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A novel region-based image retrieval algorithm using selective visual attention model
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Automatic selection and detection of visual landmarks using multiple segmentations
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Non-local spatial redundancy reduction for bottom-up saliency estimation
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An edge detection with automatic scale selection approach to improve coherent visual attention model
Pattern Recognition Letters
Multiscale discriminant saliency for visual attention
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Salient object detection based on regions
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Visual attention mechanism for a social robot
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In this paper, a novel model of object-based visual attention extending Duncan's Integrated Competition Hypothesis [Phil. Trans. R. Soc. London B 353 (1998) 1307-1317] is presented. In contrast to the attention mechanisms used in most previous machine vision systems which drive attention based on the spatial location hypothesis, the mechanisms which direct visual attention in our system are object-driven as well as feature-driven. The competition to gain visual attention occurs not only within an object but also between objects. For this purpose, two new mechanisms in the proposed model are described and analyzed in detail. The first mechanism computes the visual salience of objects and groupings; the second one implements the hierarchical selectivity of attentional shifts. The results of the new approach on synthetic and natural images are reported.