Weight adaptation and oscillatory correlation for image segmentation

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 time 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.