中文说明：分割使用模糊的图像区域的c-means算法...的FCM算法通过分配像素每个类别利用模糊成员。让XZ（X1，X2，。，XN）表示具有N个像素的图像被划分成C簇，在那里习近平代表多光谱（功能）的数据。为每个聚类中心的初始猜测开始，FCM收敛为VI的解决方案代表着当地最小或成本函数的一个鞍点。收敛可以通过比较在隶属函数或变化在两个聚类中心被检测的图像的重要特征逐次迭代steps.One是相邻像素是高度相关的。换句话说，这些相邻像素具有类似的特征值。聚类是在每个迭代一个两遍过程。该第一遍是一样的，在标准的FCM以计算在频域中的隶属函数。在第二通，每个像素的成员信息被映射到空间域和空间函数计算从那个。在FCM迭代收益与被整合的空间功能的新成员。迭代停止时在两个连续的迭代两个集群中心之间的最大差值为大于阈值（Z0.02）少。汇合后，去模糊化被施加到每个像素分配给特定集群为其成员是最大的。...
English Description：
To segment the image region using fuzzy c-means algorithm...The FCM algorithm assigns pixels to each category byusing fuzzy memberships. Let XZ(x1, x2,.,xN) denotes an image with N pixels to be partitioned into c clusters, wherexi represents multispectral (features) data. Starting with an initial guess for each cluster center, the FCM converges to a solution for vi representing the localminimum or a saddle point of the cost function. Convergence can be detected by comparing the changes in the membership function or the cluster center at twosuccessive iteration steps.One of the important characteristics of an image is that neighboring pixels are highly correlated. In other words,these neighboring pixels possess similar feature values. The clustering is a two-pass process at each iteration. Thefirst pass is the same as that in standard FCM to calculate the membership function in the spectral domain. In the second