棉纺织技术2017，Vol.45Issue(10)：5-8,4.
基于粒子群算法优化PCNN的织物疵点分割
Fabric Defect Segmentation Based on Particle Swarm Optimization Optimized PCNN
摘要
Abstract
Fabric defect segmentation based on particle swarm optimization optimized PCNN parameter was discussed.Three parameters of PCNN were used as the particles of particle swarm.The entropy of image after segmenting by PCNN was used as the fitness function of PSO.According to the fitness function of PSO,the optimal value of parameter in PCNN model was found.The segmentation contrast experiment has verified the feasibility and effectiveness of the method from subjective and objective perspectives.The method is compared with traditional PCNN and OTSU segmentation method.It is considered that the method has better segmentation effect and can improve the automation degree of model efficiently.关键词
粒子群算法/脉冲耦合神经网络/疵点分割/迭代/适应度Key words
Particle Swarm Optimization (PSO)/Pulse Coupled Neural Network (PCNN)/Fabric Defect Segmentation/Iteration/Fitness分类
信息技术与安全科学引用本文复制引用
钱炜,周武能..基于粒子群算法优化PCNN的织物疵点分割[J].棉纺织技术,2017,45(10):5-8,4.基金项目
国家自然科学基金(61573095) （61573095）
