前言：目的探讨颅脑mri图像模糊聚类分割算法中最佳模糊聚类数。
Objective: To discuss the best fuzzy clustering number of MRI brain images segmentation.
最后用K均值算法对谱聚类集成的结果进行再次聚类，得到最终的集成聚类分割结果。
At last, the segmentation result is clustered again using K-means cluster to get the ultimate segmentation result.
与三维网格模型的K均值聚类分割、点云模型的谱系聚类分割的实验结果比较证实了这一点。
This is supported by the comparison with the results of hierarchical clustering segmentation of point cloud model and K-Means clustering segmentation of mesh model.
因此，本文采用RPCL算法，对这些组合的聚类中心颜色进行学习来确定实际的颜色类数目以及聚类中心，并用学习后的聚类中心对原图像进行聚类分割。
Therefore, RPCL is utilized to converge some of initial centers to actual centers of original color image and image is segmented by these learned cluster centers.
本文首先分析了基于内容的视频检索的关键技术。总结了镜头分割、视频流特征分析和镜头聚类方面的相关研究和算法。
At first, we summarize the key techniques used in the content-based video retrieval, such as shots division, video character analysis, shots clustering, etc.
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.
模糊聚类是模糊理论的一个重要的分支，在图像分割中得到广泛应用。
Fuzzy clustering is an important branch of fuzzy set theory, and is widely applied in image segmentation.
数据聚类或分割就是其中的一种重要的数据发掘应用。
Clustering or segmentation of data is an important data mining application.
本文介绍了实现视频分割和场景聚类的算法。
This paper introduced algorithms for video segmentation and scene clustering.
为了改进当前社会化标注系统在标签浏览和检索方面的弱点，提出一种基于加权网络分割的社会性标签聚类算法。
This paper proposes a clustering algorithm of social tags based on weighed network division for the purpose of improving browsing and retrieval in existing social annotation system.
采用自适应门限进行阈值分割，得到二值化的图像；利用聚类的方法去掉噪声点。
Binaryzation image is obtained through adaptive threshold segmentation and noise is removed by sorting method.
这种无监督的聚类方法能够自动搜索最佳的网络输出节点数而获取图像中的目标数，从而完成对图像的自动分割。
This kind of unsupervised clustering method can search for the optimal number of output nodes automatically to get the number of textures in the 'image, and finish the automatic segmentation.
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.
本文给出了模糊聚类算法在图像分割中的应用结果。
In this paper, the application of suppressed fuzzy clustering algorithm in image segmentation is introduced.
针对偏置环境下图像分割问题，提出了一种基于偏置场估计的模糊聚类算法。
A novel FCM segmentation algorithm is proposed based on bias field estimation with respect to the segmentation issue of defocused images with illumination patterns under bias field.
为了对低信噪比的超声图像进行有效分割，提出一种谱聚类集成的超声图像分割算法。
A novel ultrasound image segmentation algorithm, which is based on the spectral cluster ensemble, is proposed to segment ultrasound images with low SNR.
针对模糊核聚类对红外图像分割存在的不足，提出了一种改进的模糊核聚类红外图像分割算法。
Due to the problems of infrared image segmentation using fuzzy kernel clustering, an improved method for infrared image segmentation was proposed.
本文处理的对象是灰度图像，分割的核心是对像素进行聚类，属于优化问题。
This thesis deals with gray image. The kernel of segmentation is pixel clustering. It belongs to optimization problem.
算法用于图像分割是一种非监督模糊聚类后再标定的过程。
It is a procedure of the label following an unsupervised fuzzy clustering that fuzzy c-means (FCM) algorithm is applied to image segmentation.
框架由四部分组成：图像输入阶段、图像特征处理阶段、聚类择优阶段和最后的分割结果。
There are four part of it: image input, dealing with image feature, clustering and choosing the best and output the result.
作为一种重要的分类器，模糊聚类技术在磁共振图像的分割中已经得到了成功的应用，并成为了一种有效的磁共振图像的分割工具。
As an important classifier, fuzzy clustering technique has been widely used in segmentation of MRI image and became an effective segmentation tool of MR image.
算法基于标签节点的核心度和相似性对标签共现网络进行分割，并在聚类后自动生成该类的特征标签来代表该类簇。
The algorithm divides tag co -occurrence network based on tag node's centrality and similarity, and automatically generates a cluster feature tag after clustering to represent that cluster.
目的介绍一种动态模糊聚类算法并利用该算法对磁共振图像进行分割研究。
Objective to introduce a dynamic fuzzy clustering algorithm and use it to do the study of segmentation of the brain in MRI.
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.
为了提高单层组织自动识别的精度，运用颜色聚类等方法分割织物样图，并提出了一种经纱分割算法，实现了经纬纱线的准确分割。
Color clustering is used to segment the fabric image, and a new algorithm of warp yarn segmentation is proposed to perform the identification more precisely.
根据视觉的颜色聚类特性，提出一种图像分割算法。
Proposes an image segmentation algorithm based on perceptual color clustering.
算法首先对图像进行量化处理，而后在量化后的色彩空间中集成先验的分割信息进行色彩聚类。
The algorithm first has the image quantized and then clusters in the quantized color space with prior segmentation information.
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.
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.
应用推荐