Image segmentation based on a dynamically coupled neural oscillator network

Abstract

In this paper, a dynamically coupled neural oscillator network is proposed 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 neighborhoods 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. 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.