Image segmentation by neural oscillator networks

We study image segmentation on the basis of locally excitatory globally inhibitory oscillator networks (LEGION), whereby the phases of oscillators encode the binding of pixels. We introduce a potential for each oscillator so that only those oscillators with strong connections from their neighborhoods can develop high potentials. Based on this concept, a solution to remove noisy regions in an image is proposed for LEGION, so that it suppresses the oscillators corresponding to noisy regions, without affecting those corresponding to major regions. The network is applied to segmenting real gray-level images and produces reasonable results. LEGION may provide a neurally plausible and effective framework for image segmentation and figure-ground segregation