An Adaptive Infrared Image Segmentation Method Based on Fusion SPCNN

Abstract Inspired by multiple information processing mechanisms of the human nervous system, a fusion simplified pulse coupled neural network (FSPCNN) model for infrared (IR) image segmentation is proposed in this paper. In the method based on FSPCNN, the time decay factor is set adaptively based on Stevens’ power law, and the synaptic weight is generated adaptively based on Lateral Inhibition (LI), without manual intervention. Meanwhile, according to Fast linking mechanism, the similarity between adjacent iteration results is used to implement the automatic selection of optimal segmentation result and control iteration. Experimental results indicate that the proposed method can satisfactorily segment targets from complex backgrounds, and it has favorable robustness and segmentation performance.