磨粒图像目标提取的遗传FCM算法

GENETIC FCM ALGORITHM FOR WEAR PARTICLE IMAGE OBJECTIVE EXTRACTING

将遗传算法与模糊C均值聚类算法(FCM算法)结合,并运用于磨粒图像目标提取。遗传FCM算法的基本思路是:首先对模糊聚类中心进行编码,然后依据FCM算法的目标函数建立适应度函数,在适当的交叉率和变异率下,最终实现了图像的有效分割。并考虑在一维分割特征向量情况下,通过引入直方图统计特性,实现了遗传FCM算法的快速运算。分割实验表明本文方法在一定程度上改善了标准FCM算法的性能,能有效地运用于智能铁谱分析系统中的磨粒目标自动提取。

In this paper, the Fuzzy C-Mean algorithm (FCM) combined with genetic algorithm is applied for wear particle image objective extracting. By this method, the fuzzy cluster center is coded firstly, and the fitness function is established according to the object function in FCM algorithm, and finally, the image is segmented effectively under proper crossover rate and mutation rate. At the same time, under considering 1-D segmentation character vector and introducing the histogram, the genetic FCM algorithm speed is increased greatly. Segmentation examples show that the method in this paper improves standard FCM algorithm to some extent, and can be applied to extract wear particle objects from their images in intelligent ferrography analysis system effectively..