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.

在人类神经系统多种信息处理机制的启发下，提出了一种融合简化脉冲耦合神经网络（FSPCNN）的红外（IR）图像分割模型。在基于FSPCNN的方法中，根据史蒂文斯幂定律自适应地设置时间衰减因子，并基于横向抑制（LI）自适应生成突触权重，而无需人工干预。同时，根据快速链接机制，利用相邻迭代结果之间的相似性来实现最优分割结果的自动选择和控制迭代。实验结果表明，该方法能够较好地分割复杂背景下的目标，具有良好的鲁棒性和分割性能。