模糊C均值聚类图像分割的.net设计与实现

Design and Realization of Imagine Analysis by Using. net Technique and Fuzzy C Means Clustering

提出了一种新的基于模糊C均值(FCM)聚类的图形图像分析方法,并采用高级语言对其进行了设计与实现。阐述了FCM聚类的基本原理,建立了FCM聚类的类别识别模型,研究了FCM聚类分类的模型的缺陷并提出优化策略。在此基础上,采用.net与FCM聚类相结合的算法,展示了FCM聚类的算法优势,采用.net语言提高了FCM聚类分析的速度与聚类效果,多线程的应用更好地展示了FCM在图形图像分析中的优势。通过对不同情况下车牌图像的分割分析,提升了FCM对复杂图像的应用效果。

A new analytical method based on fuzzy C means（ FCM） clustering is proposed and realized. FCM clustering is an extension of K means clustering to fuzzy discriminant analysis. Firstly,the basic principle of FCM clustering is displayed,then the model on FCM for image is built. Secondly,the defects of FCM model are detailed,then the optimization strategy is put forward. Finally,the model is realized by C# language,the. net technique better displays its advantages and improves clustering analysis speed and effect. The result shows the advantages of FCM in image analysis and complex image application.