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基于PCNN模型和对称Tsallis交叉熵的织物疵点图像分割

Segmentation of Fabric Defect Images Based on PCNN Model and Symmetric Tsallis Cross Entropy
作者:Hong WanYiquan WuZhaoqing CaoZhilong Ye 加工时间:2014-09-23 信息来源:科技报告(Other) 索取原文[5 页]
关键词:织物疵点检测;图像分割;脉冲耦合神经网络;电子信息
摘 要:Segmentation of defect images is an important step in the automatic fabric defect detection.In order to extract fabric defects effectively,a segmentation method of fabric defect images based on pulse coupled neural network (PCNN) model and symmetric Tsallis cross entropy is proposed.The image is segmented by PCNN according to the gray strength difference between fabric defect area and non-defect area.To guarantee that the grayscale inside the object and background is uniform after segmentation,symmetric Tsallis cross entropy is used as the image segmentation criterion to select the optimal threshold and iteration number.A large number of experimental results show that,compared with the related segmentation methods such as Otsu method,PCNN method,the method based on PCNN and cross entropy,the segmentation effect of the proposed method is the best.The texture of non-detect area is removed more completely,and the defect area is segmented more accurately.
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