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17 April 2006 A biologically inspired neural oscillator network for geospatial analysis
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Abstract
A biologically plausible neurodynamical approach to scene segmentation based on oscillatory correlation theory is investigated. A network of relaxation oscillators, which is based on the Locally Excitatory Globally Inhibitory Oscillator Network (LEGION), is constructed and adapted to geospatial data with varying ranges and precision. This nonlinear dynamical network is capable of achieving segmentation of objects in a scene by the synchronization of oscillators that receive local excitatory inputs from a collection of local neighbors and desynchronization between oscillators corresponding to different objects. The original LEGION model is sensitive to several aspects of the data that are encountered in real imagery, and achieving good performance across these different data types requires the constant adjusting of parameters that control excitatory and inhibitory connections. In this effort, the connections in the oscillator network are modified to reduce this sensitivity with the goal to eliminate the need for parameter adjustment. We assess the ability of the proposed approach to perform natural and urban scene segmentation for geospatial analysis. Our approach is tested on simulated scene data as well as real imagery with varying gray shade ranges and scene complexity.
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Robert S. Rand and DeLiang Wang "A biologically inspired neural oscillator network for geospatial analysis", Proc. SPIE 6247, Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks IV, 62470N (17 April 2006); https://doi.org/10.1117/12.665040
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