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Weight Adaptation and Oscillatory Correlation for Image Segmentation
ABSTRACT
We propose a method for image segmentation based on a neural oscillator
network. Unlike previous methods, weight adaptation is adopted during
segmentation to remove noise and preserve significant discontinuities in
an image. Moreover, a logarithmic grouping rule is proposed to facilitate
grouping of oscillators representing pixels with coherent properties.
We show that weight adaptation plays the roles of noise removal and
feature preservation. In particular, our weight adaptation scheme
is insensitive to termination times, and the resulting dynamic weights in a
wide range of iterations lead to the same segmentation results. A computer
algorithm derived from oscillatory dynamics is applied to synthetic and
real images, and simulation results show that the algorithm yields favorable
segmentation results in comparison with other recent algorithms. In addition,
the weight adaptation scheme can be directly transformed to a novel
feature-preserving smoothing procedure. We also demonstrate that our nonlinear
smoothing algorithm achieves good results for various kinds of images.
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