An improved FCM algorithm is proposed based on the improvement of hybrid color space.
提出一种基于混合颜色空间的改进的FCM算法。
The improved FCM algorithm was adopted to segment images, and automatic fuzzy redistribution algorithm to define the subject degree.
采用改进的FCM算法分割图像，用自动模糊重分布的算法确定隶属度。
The improved FCM algorithm applied with the region fitting term of CV model, working as the reliance of evolving the level-set curve.
再将改进后的FCM算法应用到CV模型的区域检测项，可较准确地使像素点归类，以此作为曲线的演化依据。
It is indicated by experiments that the improved FCM algorithm has better distribution feature of membership values and enhances the precision of image processing.
实验表明，改进的FCM算法具有良好的隶属信息分布特性并提高了图像处理的精度。
In this paper, an improved FCM algorithm (CFCM) is given, through which the lost information of in-complete-data images can be restored, while edge-detection of images is accomplished.
本文利用改进的模糊聚类算法，依据邻域信息实现了对丢失图像信息的恢复，并完成了对该图像的检测。
With the clustering feature analyzed, restrained function and pattern similarity are introduced. Then the algorithm of improved FCM is presented.
通过对模糊c均值算法聚类特性的分析，引入了约束函数及模式相似度的概念，提出了改进的FCM算法。
An improved color segmentation algorithm is presented based on weighting fuzzy c-means (FCM) clustering algorithm.
在加权模糊c -均值(FCM)聚类算法的基础上，对分色算法进行了改进。
After analyzing the problems with present FCM clustering algorithm, an improved version of FCM clustering algorithm is proposed in this paper.
分析了现有FCM聚类算法存在的问题，提出了一种改进的FCM聚类算法。
The FCNN is fuzzed by FCM algorithm and improved LMS algorithm is applied to tune the weight of FCNN.
采用模糊C—均值聚类算法对网络进行模糊化，利用改进的LMS算法对网络进行训练。
The FCNN is fuzzed by FCM algorithm and improved LMS algorithm is applied to tune the weight of FCNN.
采用模糊C—均值聚类算法对网络进行模糊化，利用改进的LMS算法对网络进行训练。
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