Preattentive processing in vision
Computer Vision, Graphics, and Image Processing
Deliberation scheduling for problem solving in time-constrained environments
Artificial Intelligence
Networks of spiking neurons: the third generation of neural network models
Transactions of the Society for Computer Simulation International - Special issue: simulation methodology in transportation systems
The handbook of brain theory and neural networks
A neural network implementation of a saliency map model
Neural Networks
Distributed real time neural networks in interactive complex systems
CSTST '08 Proceedings of the 5th international conference on Soft computing as transdisciplinary science and technology
Enhancing robustness of a saliency-based attention system for driver assistance
ICVS'08 Proceedings of the 6th international conference on Computer vision systems
Covert attention with a spiking neural network
ICVS'08 Proceedings of the 6th international conference on Computer vision systems
Perception as a dynamical sensori-motor attraction basin
ECAL'05 Proceedings of the 8th European conference on Advances in Artificial Life
A distributed model of spatial visual attention
Biomimetic Neural Learning for Intelligent Robots
Attentional Landmarks and Active Gaze Control for Visual SLAM
IEEE Transactions on Robotics
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Human vision rely on attention to select only a few regions to process and thus reduce the complexity and the processing time of visual task. Artificial vision systems can benefit from a bio-inspired attentional process relying on neural models. In such applications, what is the most efficient neural model: spiked-based or frequency-based? We propose an evaluation of both neural model, in term of complexity and quality of results (on artificial and natural images).