Locally excitatory, globally inhibitory oscillator networks: theory and application to scene segmentation

DeLiang Wang; David Terman

doi:10.1117/12.200790

31 January 1995 Locally excitatory, globally inhibitory oscillator networks: theory and application to scene segmentation

DeLiang Wang, David Terman

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Proceedings Volume 2368, 23rd AIPR Workshop: Image and Information Systems: Applications and Opportunities; (1995) https://doi.org/10.1117/12.200790
Event: 23 Annual AIPR Workshop: Image and Information Systems: Applications and Opportunities, 1994, Washington, DC, United States

Abstract

A novel class of locally excitatory, globally inhibitory oscillator networks (LEGION) is proposed and investigated analytically and by computer simulation. The model of each oscillator corresponds to a standard relaxation oscillator with two time scales. The network exhibits a mechanism of selective gating, whereby an oscillator jumping up to its active phase rapidly recruits the oscillators stimulated by the same pattern, while preventing other oscillators from jumping up. We show analytically that with the selective gating mechanism the network rapidly achieves both synchronization within blocks of oscillators that are stimulated by connected regions and desynchronization between different blocks. Computer simulations demonstrate LEGION's promising ability for segmenting multiple input patterns in real time. This model lays a physical foundation for the oscillatory correlation theory of feature binding, and may provide an effective computational framework for scene segmentation and figure/ground segregation.

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DeLiang Wang and David Terman "Locally excitatory, globally inhibitory oscillator networks: theory and application to scene segmentation", Proc. SPIE 2368, 23rd AIPR Workshop: Image and Information Systems: Applications and Opportunities, (31 January 1995); https://doi.org/10.1117/12.200790

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KEYWORDS

Oscillators

Image segmentation

Computer simulations

Computer vision technology

Machine vision

Binary data

Neurons

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