Image segmentation based on oscillatory correlation
Neural Computation
A neural model of contour integration in the primary visual cortex
Neural Computation
Pattern segmentation in associative memory
Neural Computation
Optical Memory and Neural Networks
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Recurrent oscillatory network with tunable oscillator dynamics and nonlocal dynamical interaction has been designed. Two versions of the network model have been suggested: 3D oscillatory network of columnar architecture that reflects some image processing features inherent in the brain visual cortex, and 2D version of the model, obtained from the 3D network by proper reduction. The developed image segmentation algorithm is based on cluster synchronization of the reduced network that is controlled by means of interaction adaptation method. Our approach provides successive separation of synchronized clusters and final decomposition of the network into a set of mutually desynchronized clusters corresponding to image fragments with different levels of brightness. The algorithm demonstrates the ability of automatic gray-level image segmentation with accurate edge detection. It also demonstrates noise reduction ability.