A neural cocktail-party processor
Biological Cybernetics
Synchronization of pulse-coupled biological oscillators
SIAM Journal on Applied Mathematics
On the relative complexity of active vs. passive visual search
International Journal of Computer Vision
Modeling visual attention via selective tuning
Artificial Intelligence - Special volume on computer vision
Image segmentation based on oscillatory correlation
Neural Computation
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Synchrony and desynchrony in integrate-and-fire oscillators
Neural Computation
Object selection based on oscillatory correlation
Neural Networks
Self-Organization of Pulse-Coupled Oscillators with Application to Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Selective visual attention enables learning and recognition of multiple objects in cluttered scenes
Computer Vision and Image Understanding - Special issue: Attention and performance in computer vision
A Behavioral Analysis of Computational Models of Visual Attention
International Journal of Computer Vision
Synchronization and desynchronization in a network of locally coupled Wilson-Cowan oscillators
IEEE Transactions on Neural Networks
The time dimension for scene analysis
IEEE Transactions on Neural Networks
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Biological systems have facility to capture salient object(s) in a given scene, but it is still a difficult task to be accomplished by artificial vision systems. In this paper a visual selection mechanism based on the integrate and fire neural network is proposed. The model not only can discriminate objects in a given visual scene, but also can deliver focus of attention to the salient object. Moreover, it processes a combination of relevant features of an input scene, such as intensity, color, orientation, and the contrast of them. In comparison to other visual selection approaches, this model presents several interesting features. It is able to capture attention of objects in complex forms, including those linearly non-separable. Moreover, computer simulations show that the model produces results similar to those observed in natural vision systems.