首先用阈值分割法去除红毛丹背景，然后用模糊C均值聚类方法来分割果肉区域。
The rambutan flesh was segmented using the FCM (fuzzy C-mean) clustering method after removing the background of the image.
首先，运用K-均值聚类方法提取出细胞核，并且采用多域值分割演算法去除细胞图像中的背景区域。
Firstly, nucleus regions of leukocytes in images are automatically segmented by K-mean clustering method. Then single leukocyte region is detected by utilizing thresholding algorithm segmentation.
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.
此算法首先结合RAC和K -均值聚类方法对未知模型的特征点进行预匹配，得到的匹配结果称为聚类点集。
Firstly, RAC and K-means clustering method are combined in this algorithm by the way of searching pre-matches feature points, which are called the cluster point set, of the unknown model.
这里提出了一种高效的基于模糊c均值(FCM)聚类的彩色图像分割方法，它利用塔形数据结构对彩色图像进行多层分割。
An efficient segmentation method based upon fuzzy c-means (FCM) clustering principles is proposed. The approach utilizes a pyramid data structure for the hierarchical ana - lysis of color images.
研究红外图像中弱小目标的检测问题，提出了一种基于能量累积与均值漂移聚类的红外小目标检测方法。
A new small target detection method for infrared image based on energy accumulation and mean shift clustering is presented.
通过理论分析，属性均值聚类是比模糊均值聚类更稳健的聚类方法。
Attribute means clustering is more robust than fuzzy means clustering by theoretical analysis and numerical example.
提出了基于模糊C均值聚类和图像匹配，检测喷雾锥角和喷雾不均匀度的方法，并应用于发动机喷嘴性能检测。
A method based on fuzzy C-mean clustering and image matching algorithms are proposed to detect atomization Angle and uniformity, applied to performance test-bed of engine nozzle.
为了改善文本聚类的准确度，提出用基于主题概念子空间的模糊c -均值聚类(TCS2FCM)方法来分类文本。
To improve the accuracy of text clustering, fuzzy c-means clustering based on topic concept sub-space (TCS2FCM) is introduced for classifying texts.
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.
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.
提出一种隐马尔可夫模型和K -均值聚类混合模型的声目标识别方法。
A recognition method based on HMM and K-means cluster is proposed through extracting LPC characteristic from acoustic target.
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.
基于划分的聚类算法主要有K均值和K中心点算法，其他的方法都是这两种算法的变种。
The clustering method based on partitioning is mainly included K-Means and K-Medoids; the other methods are the mutation of these two methods.
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.
经典的C -均值聚类算法（CMA）是将图像分割成C类的常用方法，但依赖于初始聚类中心的选择。
The classical C-means clustering algorithm (CMA) is a well-known clustering method to partition an image into homogeneous regions.
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.
最后通过非监督的K均值方法完成动作电位聚类。
After that, unsupervised K-means clustering was calculated to complete spike sorting.
基于多特征联合分布直方图理论和模糊c -均值聚类算法，我们提出了新的视频流模糊检索方法。
We bring out our video retrieval method based on multi-feature data association histogram and C-Mean fuzzy clustering algorithm.
传统的K-均值方法用于聚类具有收敛速度快、算法实现简单等特点，但容易陷入局部最优，并对初始解敏感。
Although the traditional K - means algorithm has good convergence rate and can be realized easily, it can easily be trapped in a local optimum, and it is sensitive in initial setting.
传统的K-均值方法用于聚类具有收敛速度快、算法实现简单等特点，但容易陷入局部最优，并对初始解敏感。
Although the traditional K - means algorithm has good convergence rate and can be realized easily, it can easily be trapped in a local optimum, and it is sensitive in initial setting.
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