Modeling visual attention via selective tuning
Artificial Intelligence - Special volume on computer vision
Data- and Model-Driven Gaze Control for an Active-Vision System
IEEE Transactions on Pattern Analysis and Machine Intelligence
Biased Competition Mechanisms for Visual Attention in a Multimodular Neurodynamical System
Emergent Neural Computational Architectures Based on Neuroscience - Towards Neuroscience-Inspired Computing
Controlling the visual attention of intelligent vehicles
RA '07 Proceedings of the 13th IASTED International Conference on Robotics and Applications
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We present an application of dynamic neural fields for selection and tracking in the attentional control part of an active vision system. We propose a novel two-stage selection mechanism, in which the fields are used for the first selection stage. We discuss different variants, introducing 3D neural fields and systems of interconnected fields. The dynamics can be shown to achieve important goals in active vision like robust selection, multi-object tracking, and spatiotemporal integration.