Segmentation of Fabric Defect Images Based on PCNN Model and Symmetric Tsallis Cross Entropy

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.

关键词： Fabric Defect Detection Image Segmentation Pulse Coupled Neural Network Symmetric Tsallis Cross Entropy

作者: Hong Wan Yiquan Wu Zhaoqing Cao Zhilong Ye

作者单位: College of Electronic and Information Engineering,Nanjing University of Aeronautics and Astronautics College of Electronic and Information Engineering,Nanjing University of Aeronautics and Astronautics

会议类型: 国际会议

会议名称: 2013 2nd international Conference on Opto-Electronics Engineering and Materials Eesearch(2013第二届光电工程与材料研究国际会议)(OEMR2013)

会议地点: 郑州

会议语种:英文

页码: 603-607

在线出版日期: 2013-10-19（万方平台首次上网日期，不代表论文的发表时间）