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FCM算法用于灰度图象分割的研究
引用本文:丁震,胡钟山.FCM算法用于灰度图象分割的研究[J].电子学报,1997,25(5):39-43.
作者姓名:丁震  胡钟山
作者单位:南京理工大学信息分院
摘    要:模糊C均值(FCM)算法用于灰度图象分割是一种非监督模糊聚类后再标定的过程,适合灰度图象中存在着模糊和不稳定性的特点,但是这种算法存在着一些不足,如类数目无法自动确定,运算的开销太大等,因而限掉了这种方法的应用,针对这些问题,本文利用直方图分析的方法,自动确定算法的聚类数目和各类的类峰值,并针对FCM算法和灰度图象的特点,提出了一种适用于灰度图象分割的快速FCM算法(QFCM)使得运算了开销降低,

关 键 词:图象分割  模糊聚类  FCM算法  计算机视觉

FCM Algorithm for the Research of Intensity Image Segmentation
Abstract:It is a procedure of the label following an unsupervised fuzzy clustering that fuzzyc-means (FCM) algorithm is applied for intensity image segmentation,and it suits for the uncertainand ambiguous characteristic in intensity image. However,there are some deficiencies in the algorithm,for example,the number of clustering can not be determined automatically and the operational cost,for large data sets,is too high,which limit its application. In order to overcome the deficiencies,the number of clustering and the maximum of each cluster are automatically determinedthrough the histogram analysis ; Then, according to the character of FCM and intensity image, aquick fuzzy C-means (QFCM ) algorithm is presented for intensity images segmentation,with whichthe operational cost is reduced and the speed of clustering segmentation is greatly increased. Thefeasibility of the approach is proved by the mathematics and experiment result.
Keywords:Image segmentation  Fuzzy clustering  Fuzzy C-means algorithm  Histogram analysis  
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