基于脉冲耦合神经网络的图像分割算法分析

Analysis of image segmentation algorithm based on Pulse Coupled Neural Network

为进一步提高脉冲耦合神经网络(PCNN)在图像分割中的性能,文章研究了基于PCNN模型的图像分割算法,并探讨其模型简化算法,旨在减少其中的参数量,从而提高图像分割处理效率和分割效果。借助Girl属性图像进行仿真验证,通过与OTSU算法及LevelSet算法的分割效果进行对比,文章论证了算法的有效性及可行性。实验结果表明,该算法具有显著的分割效果。

To further improve the performance of Pulse Coupled Neural Network(PCNN)in image segmentation,this article studies an image segmentation algorithm based on the PCNN model and explores its model simplification algorithm,aiming to reduce the number of parameters and improve the efficiency and effectiveness of image segmentation processing.Through simulation verification using Girl attribute images,the article demonstrates the effectiveness and feasibility of the algorithm by comparing its segmentation performance with OTSU algorithm and Level Set algorithm.The experimental results show that the algorithm has significant segmentation performance.