Image segmentation using fast linking SCM

Spiking cortical model (SCM) is applied to image segmentation. A natural image is processed to produce a series of spike images by SCM, and the segmented result is obtained by the integration of the series of spike images. An appropriate maximum iterative times is selected to achieve an optimal threshold of SCM. In each iteration, neurons that produced spikes correspond to pixels with an intensity of the input natural image approximately. SCM synchronizes the output spikes via the fast linking synaptic modulation, which makes objects in the image as homogeneous as possible. Experimental results show that the output image not only separates objects and background well, but also pixels in each object are homogeneous. The proposed method performs well over other methods and the quantitative metrics are consistent with the visual performance.