Image segmentation based on oscillatory correlation
Neural Computation
Edge Detection and Ridge Detection with Automatic Scale Selection
International Journal of Computer Vision
Dynamics of neural networks with a central element
Neural Networks
Object selection based on oscillatory correlation
Neural Networks
A model of contextual interactions and contour detection in primary visual cortex
Neural Networks - 2004 Special issue Vision and brain
Image Segmentation by Networks of Spiking Neurons
Neural Computation
An oscillatory neural model of multiple object tracking
Neural Computation
The iCub humanoid robot: an open platform for research in embodied cognition
PerMIS '08 Proceedings of the 8th Workshop on Performance Metrics for Intelligent Systems
PerMIS '08 Proceedings of the 8th Workshop on Performance Metrics for Intelligent Systems
Physiologically motivated image fusion for object detection using a pulse coupled neural network
IEEE Transactions on Neural Networks
Weight adaptation and oscillatory correlation for image segmentation
IEEE Transactions on Neural Networks
A network of dynamically coupled chaotic maps for scene segmentation
IEEE Transactions on Neural Networks
Pixel clustering by adaptive pixel moving and chaotic synchronization
IEEE Transactions on Neural Networks
The time dimension for scene analysis
IEEE Transactions on Neural Networks
Locally excitatory globally inhibitory oscillator networks
IEEE Transactions on Neural Networks
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A brain-inspired computational system is presented that allows sequential selection and processing of objects from a visual scene. The system is comprised of three modules. The selective attention module is designed as a network of spiking neurons of the Hodgkin-Huxley type with star-like connections between the central unit and peripheral elements. The attention focus is represented by those peripheral neurons that generate spikes synchronously with the central neuron while the activity of other peripheral neurons is suppressed. Such dynamics corresponds to the partial synchronization mode. It is shown that peripheral neurons with higher firing rates are preferentially drawn into partial synchronization. We show that local excitatory connections facilitate synchronization, while local inhibitory connections help distinguishing between two groups of peripheral neurons with similar intrinsic frequencies. The module automatically scans a visual scene and sequentially selects regions of interest for detailed processing and object segmentation. The contour extraction module implements standard image processing algorithms for contour extraction. The module computes raw contours of objects accompanied by noise and some spurious inclusions. At the next stage, the object segmentation module designed as a network of phase oscillators is used for precise determination of object boundaries and noise suppression. This module has a star-like architecture of connections. The segmented object is represented by a group of peripheral oscillators working in the regime of partial synchronization with the central oscillator. The functioning of each module is illustrated by an example of processing of the visual scene taken from a visual stream of a robot camera.