关键词: 光缆表面, 瑕疵分割, 模糊C均值聚类, 样本邻域像素, 欧式距离

Abstract: With the industrial production and technological progress, people on the product quality requirements are getting higher and higher. In order to improve the effect of Fuzzy C-Means clustering algorithm on noise-sensitive fragmentation, a new Fuzzy C-Means（FCM） clustering defective image segmentation method is proposed. The method considers the neighborhood pixel information of the sample on the one hand, the FCM membership function contains the neighborhood information of the pixel, and the other uses a new distance metric instead of the traditional Euclidean distance. The above two methods are used to increase the robustness of the algorithm. In addition, through the histogram method to the cluster center by the initial value, so that the segmentation effect is stable. Finally, the pretreatment of the cable image is added by using the improved FCM algorithm and the traditional FCM algorithm and FCMM algorithm to add the salt and pepper noise and the Gaussian white noise respectively to the cable image acquired by the CCD camera. The comparison of the experimental results and the correctness of the segmentation show that the improved FCM algorithm can better overcome the noise and extract the flaws from the image accurately. The defect profile is clearer and the effect of the flaw detection on the surface of the cable is improved.

Key words: fiber-optic cable surface, segmentation of defects, Fuzzy C-Means clustering, sample neighborhood pixel, Euclidean distance