The perception of multiple objects: a connectionist approach
The perception of multiple objects: a connectionist approach
On the relative complexity of active vs. passive visual search
International Journal of Computer Vision
Behaviorist intelligence and the scaling problem
Artificial Intelligence
Modeling visual attention via selective tuning
Artificial Intelligence - Special volume on computer vision
Task-dependent learning of attention
Neural Networks
SCAN: a scalable model of attentional selection
Neural Networks
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
The handbook of brain theory and neural networks
Active Perception
SAIM: A Model of Visual Attention and Neglect
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
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BMCV '02 Proceedings of the Second International Workshop on Biologically Motivated Computer Vision
Attending to Motion: Localizing and Classifying Motion Patterns in Image Sequences
BMCV '02 Proceedings of the Second International Workshop on Biologically Motivated Computer Vision
Gaze Selection for Visual Search
Gaze Selection for Visual Search
Computer Vision and Image Understanding - Special issue: Attention and performance in computer vision
Dynamic visual attention model in image sequences
Image and Vision Computing
A Bio-inspired Architecture of an Active Visual Search Model
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part II
Language Label Learning for Visual Concepts Discovered from Video Sequences
Attention in Cognitive Systems. Theories and Systems from an Interdisciplinary Viewpoint
Computational visual attention systems and their cognitive foundations: A survey
ACM Transactions on Applied Perception (TAP)
Visual search for an object in a 3D environment using a mobile robot
Computer Vision and Image Understanding
A visual saliency map based on random sub-window means
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
Second-Order (non-fourier) attention-based face detection
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
Selective tuning: feature binding through selective attention
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
METAL: A framework for mixture-of-experts task and attention learning
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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This paper presents arguments that explicit strategies for visual attentional selection are important for cognitive vision systems, and shows that a number of proposals currently exist for exactly how parts of this goal may be accomplished. A comprehensive survey of approaches to computational attention is given. A key characteristic of virtually all the models surveyed here is that they receive significant inspiration from the neurobiology and psychophysics of human and primate vision. This, although not necessarily a key component of mainstream computer vision, seems very appropriate for cognitive vision systems given a definition of the topic that always includes the goal of human-like visual performance. A particular model, the Selective Tuning model, is overviewed in some detail. The growing neurobiological and psychophysical evidence for its biological plausibility is cited highlighting the fact that it has more biological support than other models; it is further claimed that it may form an appropriate starting point for the difficult task of integrating attention into cognitive vision systems.