一种基于改进型PCNN的织物疵点图像自适应分割方法

An Approach for Fabric Defect Image Segmentation Based on the Improved Conventional PCNN Model

针对传统脉冲耦合神经网络(Pulse Coupled Neural Network,PCNN)模型中网络参数多、不易自动选取的问题,本文在对PCNN模型进行改进的基础上,提出了一种基于改进型PCNN织物疵点图像自适应分割方法.采用了一种基于分割区域内均匀度差异最小作为最佳迭代次数判断标准,从而有效地满足了PCNN对织物疵点图像的自动分割要求.通过对不同疵点图像分割实验证明了算法对疵点分割的准确性和有效性.

An approach is proposed for fabric defect detection based on the improved conventional pulse coupled neural network（PCNN） model.For these too many parameters of conventional PCNN,it is difficult to get the adaptive parameters.The problem can be solved in the proposed way,in which optimal number of iteration to segment fabric defect image automatically is determined based on minimum difference of uniformity within region.Segmentations on various defect images are implemented with the proposed approach and the experimental results demonstrate its reliability and validity.