Attention and performance XIV (silver jubilee volume)
Modeling visual attention via selective tuning
Artificial Intelligence - Special volume on computer vision
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Providing the basis for human-robot-interaction: a multi-modal attention system for a mobile robot
Proceedings of the 5th international conference on Multimodal interfaces
Computer Vision and Image Understanding - Special issue: Attention and performance in computer vision
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
Modeling attention: from computational neuroscience to computer vision
WAPCV'04 Proceedings of the Second international conference on Attention and Performance in Computational Vision
Biologically motivated visual selective attention for face localization
WAPCV'04 Proceedings of the Second international conference on Attention and Performance in Computational Vision
A computational model of auditory selective attention
IEEE Transactions on Neural Networks
A probabilistic model of overt visual attention for cognitive robots
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An affective interactive audio interface for Lovotics
Computers in Entertainment (CIE) - Theoretical and Practical Computer Applications in Entertainment
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During the lifetime of a mobile robot, the number and complexity of the stimuli it receives may be quite high. Therefore, the construction of a detection system considering the whole sensorial space is usually not a viable proposition when aiming for real time operation. It becomes necessary to build some kind of sensorial hierarchy map to put some order into how detectors are applied. This is what is usually called an attentional system, and it provides a framework for applying detectors in a more efficient manner. In this paper, an architecture for developing attentional functions for robots that must operate in real time in dynamic environments is presented. This architecture is based on the concept of attentor and it allows for the real time adaptation to the environment and tasks to be performed in a natural manner. One of the main requirements imposed on the design of the architecture was the capability of handling different sensorial modalities and attentional streams in a transparent manner while, at the same time, being able to progressively create more complex attentional structures. The architecture is particularized for its implementation in a real robot.