Fast and robust image segmentation by small-world neural oscillator networks |
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Authors: | Chunguang Li Yuke Li |
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Institution: | (1) Department of Information Science and Electronic Engineering, Zhejiang University, 310027 Hangzhou, People’s Republic of China |
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Abstract: | Inspired by the temporal correlation theory of brain functions, researchers have presented a number of neural oscillator networks
to implement visual scene segmentation problems. Recently, it is shown that many biological neural networks are typical small-world
networks. In this paper, we propose and investigate two small-world models derived from the well-known LEGION (locally excitatory
and globally inhibitory oscillator network) model. To form a small-world network, we add a proper proportion of unidirectional
shortcuts (random long-range connections) to the original LEGION model. With local connections and shortcuts, the neural oscillators
can not only communicate with neighbors but also exchange phase information with remote partners. Model 1 introduces excitatory
shortcuts to enhance the synchronization within an oscillator group representing the same object. Model 2 goes further to
replace the global inhibitor with a sparse set of inhibitory shortcuts. Simulation results indicate that the proposed small-world
models could achieve synchronization faster than the original LEGION model and are more likely to bind disconnected image
regions belonging together. In addition, we argue that these two models are more biologically plausible. |
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Keywords: | Image segmentation Small-world network Neural oscillator Synchronization |
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