摘要
脉冲耦合神经网络PCNN(Pulse Coupled Neural Network)在图像处理中得到了广泛的应用,但是其多个参数的设置给实际应用造成了很大的困难。尤其是在图像分割中,不同类型的图像要求不同的分割参数,不同的参数对图像分割结果影响很大。而粒子群算法(PSO)具有对参数自动寻优的优势,因此,本文提出了一种基于粒子群算法和PCNN的图像自动分割研究方法。分割试验仿真结果验证了该方法的正确性和可信性,即不仅可以实现正确的图像分割,而且参数可以自动设置省去了人工试验的麻烦,同时图像分割速度也有所提高。
Pulse coupled neural network（PCNN） finds many applications in image processing.Because the parameters greatly affect the performance of PCNN,finding the optimal parameters becomes an onerous task.Especially in image segmentation,the parameters vary with the image that needs to process.An automated PCNN method was proposed that based on PCNN and Particle Swarm Optimization algorithm（PSO） and it was used to segment the image automatically and successfully.The correctness and dependability of the automated PCNN method are verified by experiment results,that is to say,the quality of the segmentation based on the automated PCNN method is much better and parameters-setting automatically is the main feature of the method.
出处
《机电产品开发与创新》
2011年第1期43-44,26,共3页
Development & Innovation of Machinery & Electrical Products
关键词
脉冲耦合神经网络
粒子群算法
图像分割
pulse coupled neural network（PCNN）
PSO algorithm
image segmentation
