结果模糊K- 均值聚类算法能很好地分割出磁共振颅脑图像中的灰质、 白质和脑脊液。
Results Fuzzy K means clustering algorithm can segment white matter, gray matter and CSF better from the MR head images.
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。
With the clustering feature analyzed, restrained function and pattern similarity are introduced. Then the algorithm of improved FCM is presented.
本文将经典的模糊c -均值聚类算法和模糊测度和模糊积分结合起来，并将这两种算法应用于医学病理图象的分割。
In this article we combine the fuzzy C-means algorithm with fuzzy measures and fuzzy integrals and apply the two algorithms to the medicinal pathological image segmentation.
该文在子镜头的关键帧提取方法基础上，利用模糊c -均值聚类算法，实现了一种基于子镜头聚类的情节代表帧选取方法。
An algorithm for selecting episode representation frames by using an approach of key frame extraction based on multiple characters and C-Mean fuzzy clustering is detailed in the paper.
改进后的模糊C-均值聚类算法具有更好的鲁棒性，且放松了隶属度条件，使得最终聚类结果对预先确定的聚类数目不敏感。
The improved fuzzy C-means clustering algorithm has better robustness and makes the cluster results insensitive to the predefined cluster number.
针对图像混合噪声提出了一种新型的模糊加权均值滤波算法。
A new efficient fuzzy weighted mean filter approach to the restoration of images corrupted by mixed noise was proposed.
结合模糊c均值算法(FCM)与模糊最小最大神经网络算法，提出一种基于超长方体集的模糊模式识别算法。
Based on fuzzy C-Means algorithm (FCM) and fuzzy Min-Max Neural Networks, an integrated algorithm for fuzzy pattern recognition using hypercube set was proposed.
模糊c均值算法(FCM)是经常使用的聚类算法之一。
The fuzzy c-means algorithm (FCM) is one of widely used clustering algorithms.
首先该文利用模糊C均值聚类和可能性C均值聚类的优点，设计出一种混合C均值聚类算法。
Firstly, the advantages of fuzzy C-means clustering and possibilistic C-means clustering are utilized in this paper. We design a new hybrid C-means clustering accordingly.
利用数据点的密度大小作为权值，借助数据本身的分布特性，提出了一种点密度加权模糊c -均值算法。
A dot density weighted fuzzy C-means algorithm is proposed by using density size of data dot regarded as weighted value and distributing characteristic of datas own.
提出一种基于球形的模糊c -均值算法的中文文本聚类方法。
A clustering algorithm for Chinese documents based on the spherical fuzzy c-means algorithm is presented.
针对模糊C均值聚类算法对初始值敏感、易陷入局部最优的缺陷，提出一种新的优化方法。
Considering fuzzy C-means clustering algorithms are sensitive to initialization and easy fall - en to local minimum, a novel optimization method is proposed.
论文采用了一种基于改进的模糊C均值算法来聚类图像。
This paper proposes a modified fuzzy C-means (MFCM) clustering algorithm to cluster all images before retrieval.
该文根据FCM算法和灰度图像的特点，提出了一种适用于灰度图像分割的抑制式模糊C -均值聚类算法(S - FCM)。
In the paper, a suppressed fuzzy c-means (S-FCM) algorithm, for intensity image segmentation, is proposed on the basis of the characters of FCM algorithm and intensity images.
分别采用模糊c -均值聚类方法和快速全局C -均值聚类两种算法实现化工建模所需训练数据的有效提取。
In order to getting the effective training data of chemical engineering modeling, two algorithms that fuzzy C-means and fast global fuzzy C-means clustering were used.
传统的模糊c -均值(FCM)聚类是一种基于梯度下降的优化算法，该方法对初始化较敏感，且易陷入局部极小。
The traditional fuzzy C-means (FCM) algorithm is an optimization algorithm based on gradient descending. it is sensitive to the initial condition and liable to be trapped in a local minimum.
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。
An improved color segmentation algorithm is presented based on weighting fuzzy c-means (FCM) clustering algorithm.
由于原始的模糊c -均值聚类算法没有考虑图像的空间信息，算法对图像中的噪音点十分敏感。
Without considering the spatial information of images, the original fuzzy C-means algorithm is very sensitive to image noise.
针对模糊c均值算法与粒子群算法的不足，提出了一种基于粒子群算法和模糊c—均值算法的混合聚类算法。
To avoid the shortcomings of FCM and Particle Swarm Optimization algorithm, new hybrid clustering algorithm based on PSO and FCM algorithm is proposed.
模糊c -均值聚类是模式识别中的重要算法之一，很早就被应用到图像分割中。
Fuzzy C-means clustering is one of the important learning algorithms in the field of pattern recognition, which has been applied early to image segmentation.
该文提出了一种将模糊C -均值聚类法的各种改进算法与矢量量化法相结合的说话人辨认的新方法。
Several new algorithms of fuzzy C-mean clustering with the combination of vector quantization are proposed for speaker identification.
本文讨论了模糊聚类中的模糊C均值算法和聚类有效性测度。
This paper discusses the fuzzy C-means algorithm (FCM), one of the fuzzy clustering methods and clustering validity measurements.
针对这个问题，很多稳健模糊C -均值聚类算法被提出。
Lots of robust fuzzy C-means algorithms have been proposed in the literature to solve this problem.
本文改进了传统FCM的目标函数，引入控制邻域作用紧密程度的参数，提出了一种能够更加合理地运用图像的空间信息，改进的模糊c -均值聚类算法。
Modifying the objective function of FCM and introducing a variable as the parameter to control the tight degree of neighborhood effect present a spatial model to FCM clustering algorithm.
使用模糊c均值算法时，如何选取模糊指标m一直是一个悬而未决的问题。
It is an open problem how to select an appropriate fuzziness index m when implementing the FCM.
本文从几何角度给出模糊c均值聚类算法中隶属度的解释，这种解释能更好的说明模糊c均值聚类算法的本质。
An explanation of membership degree in FCM algorithm from geometry view is given, which is helpful to understand the essence of FCM algorithm.
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。
The FCNN is fuzzed by FCM algorithm and improved LMS algorithm is applied to tune the weight of FCNN.
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