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A Dynamically Coupled Neural Oscillator Network for Image Segmentation
ABSTRACT
We propose a dynamically coupled neural oscillator network for image
segmentation. Instead of pair-wise coupling, an ensemble of oscillators
coupled in a local region is used for grouping. We introduce a set of
neighbourhoods to generate dynamical coupling structures associated with
a specific oscillator. Based on the proximity and similarity principles,
two grouping rules are proposed to explicitly consider the distinct cases of
whether an oscillator is inside a homogeneous image region or near a boundary
between different regions. The use of dynamical coupling makes our
segmentation network robust to noise on an image, and unlike image
processing algorithms no iterative operation is needed for noise removal.
For fast computation, a segmentation algorithm is abstracted from
the underlying oscillatory dynamics, and has been applied to synthetic and
real images. Simulation results demonstrate the effectiveness of our
oscillator network in image segmentation.
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