Modeling visual attention via selective tuning
Artificial Intelligence - Special volume on computer vision
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sparse coding in the primate cortex
The handbook of brain theory and neural networks
Form-From-Motion: MEG Evidence for Time Course and Processing Sequence
Journal of Cognitive Neuroscience
Computer Vision and Image Understanding - Special issue: Attention and performance in computer vision
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
TarzaNN: a general purpose neural network simulator for visual attention modeling
WAPCV'04 Proceedings of the Second international conference on Attention and Performance in Computational Vision
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We present a biologically plausible computational model for solving the visual feature binding problem. The binding problem appears to be due to the distributed nature of visual processing in the primate brain, and the gradual loss of spatial information along the processing hierarchy. The model relies on the reentrant connections so ubiquitous in the primate brain to recover spatial information, and thus allows features represented in different parts of the brain to be integrated in a unitary conscious percept. We demonstrate the ability of the Selective Tuning model of visual attention [1] to recover spatial information, and based on this we propose a general solution to the feature binding problem. The solution is used to simulate the results of a recent neurophysiology study on the binding of motion and color. The example demonstrates how the method is able to handle the difficult case of transparency.