基于改进的FCM算法图像分割研究

Image Segmentation Research Based on Improved FCM Algorithm

将自适应均值滤波引入到FCM算法中.首先利用自适应加权窗,计算邻域像素的加权系数,重新计算原图像的自适应加权均值图像,然后利用新的目标函数、聚类中心函数及隶属度函数完成图像分割.因为充分考虑了像素的空间信息和邻域像素对中心像素的影响程度的不同,使得正常数据和异常数据有所区分,从而降低噪声数据对中心像素的影响程度.最后将改进的算法应用到道岔缺口的提取中.实验结果表明,改进的FCM算法在噪声的干扰下,能够分割出较为完整的道岔缺口图像,克服了FCM算法对噪声敏感的问题,提高了FCM算法的鲁棒性.

An adaptive median filter is applied in the FCM algorithm.Firstly,adaptive weighted window is used to calculate the weighted coefficient of neighborhood pixels,adaptive weighted average image of the original image is recalculated,and the new objective function,the clustering center function and membership function are used to complete the image segmentation.Due to pixels of space information and the different influence of the neighborhood pixels to center pixel is fully considered,the distinguishments between normal data and abnormal data are given out,thus the influence of the noise data to the center pixel is reduced.Finally,the improved algorithm is applied to extraction of rail gap.The experimental results show that the improved FCM algorithm can relatively complete rail gap image segmented with noise interference,overcome the disadvantage of the FCM algorithm sensitive to noise,and improve the robustness of FCM algorithm.