A neural cocktail-party processor
Biological Cybernetics
On the relative complexity of active vs. passive visual search
International Journal of 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
Object selection based on oscillatory correlation
Neural Networks
A Behavioral Analysis of Computational Models of Visual Attention
International Journal of Computer Vision
Chaotic phase synchronization for visual selection
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
A network of dynamically coupled chaotic maps for scene segmentation
IEEE Transactions on Neural Networks
The time dimension for scene analysis
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Objects auto-selection from stereo-images realised by self-correcting neural network
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part I
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Object selection refers to the mechanism of extracting objects of interest while ignoring other objects and background in a given visual scene. It is a fundamental issue for many computer vision and image analysis techniques and it is still a challenging task to artificial visual systems. Chaotic phase synchronization takes place in cases involving almost identical dynamical systems and it means that the phase difference between the systems is kept bounded over the time, while their amplitudes remain chaotic and may be uncorrelated. Instead of complete synchronization, phase synchronization is believed to be a mechanism for neural integration in brain. In this paper, an object selection model is proposed. Oscillators in the network representing the salient object in a given scene are phase synchronized, while no phase synchronization occurs for background objects. In this way, the salient object can be extracted. In this model, a shift mechanism is also introduced to change attention from one object to another. Computer simulations show that the model produces some results similar to those observed in natural vision systems.