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Kmeans.Cluster.using.Guide
- 图像集群（Image Clustering） (1)图像读入，显示图像所在路径； (2)采用imgcluster函数进行图像集群，选择集群个数后进行图像集群； (3)运行后，在原图像上显示集群灰度图； (4)若要显示各个集群情况，可打开【Show Clustering Image】新窗体，显示各集群类的基于原图的彩绘区域。其中非当前集群范围，则显示灰度为255的黑色。用户可点击按纽上下查看所有集群图。-image cluster (Image Clustering) (1) re
FCM
- FCM算法是一种基于划分的聚类算法，它的思想就是使得被划分到同一簇的对象之间相似度最大，而不同簇之间的相似度最小。有关FCM的介绍及程序实现。-FCM algorithm is a clustering algorithm based on the division of its thinking is that it is making is divided into clusters with the greatest similarity between the object, and d
isodata_cluster
- 本程序为C程序，主要用来实现对遥感图像的ISODATA聚类分类。-This programm clusters the generic binary image file into user defined cluster number based on hard c mean! The programm is used for 3 band images. If you want to cluster more bands, the programm has to be slightly mo
ctex
- In this project, we intend to segment natural images by combing colour and texture information. For this we will be using an unsupervised image segmentation framework (referred to as CTex) that is based on the adaptive inclusion of color and texture
kmeans
- 加强了的K-MEANS聚类函数，速度比以前快5 。适合多种聚类场合。一般聚类数为2-5之间为最佳。-The enhanced K-MEANS clustering function, speed 5 faster than before. Clustering for a variety of occasions. General the number of clusters for the best between 2-5.
kmeans
- 利用Kmeans分色，原型程式由書本上修改，敢為分色後再切割圖形並讀取圖形，但需預先給定聚類數。-Kmeans separation using the prototype program changes from the books, dare to cut graphics and color and then read the graphics, but the need to advance a given number of clusters.
MARK_Kmeans
- 使用k-means算法对一副RGB色彩空间的图像作简单的聚类。根据命令行提示输入聚类的大小K，程序自动计算每一个像素点的归属并着色该点为该类的色彩均值。工程运行于VS2008环境，需要OpenCV支持。Debug目下exe文件可以直接双击运行查看结果。-Using k-means algorithm on an RGB color space images to make a simple clustering. According to the command prompt enter the
Webinar_Files
- Working with large images presents a number of challenges such as managing memory use and leveraging high-performance resources ranging from multicore desktops to clusters and grids. In this webinar you ll discover new features in MATLAB and Ima
mean_shift
- Mean Shift算法介绍，清晰易懂，用于图像分割，图像平滑，聚类等，Mean Shift是热点-Mean Shhift introduction, easily understand, image segmentationt , preserving smoothing, clusters, it is a hot item for researching
OSTU_yuzhifenge
- 用matlab实现OSTU大津法算法，得到最大类间方差的阈值。对于图像处理的初学者可以更好的理解大津法的原理-The realization of OSTU with Matlab,which can get the maximum variance between clusters
SIFTflow
- SIFT keypoints of objects are first extracted from a set of reference images[4] and stored in a database. An object is recognized in a new image by individually comparing each feature from the new image to this database and finding candidate matching
meanshiftCLUST
- 利用meanshift方法进行分簇，并有实验结果-The meanshift method of sub-clusters, and experimental results
FCM_clust
- 本文在阐述聚类分析方法的基础上重点研究FCM聚类算法。FCM算法是一种基于划分的聚类算法，它的思想是使得被划分到同一簇的对象之间相似度最大，而不同簇之间的相似度最小。最后基于MATLAB实现了对图像信息的聚类。- This paper describes the basis of cluster analysis methods and focus on the FCM clustering algorithm. FCM algorithm is a clustering algorithm b
SIFT
- SIFT(Scale Invariant Feature Transform)即尺度不变特征变换，是 D. G.Lowe 在 1999 年提出的一种基于图像局部特征的描述算子，并于 2004年做了完善。SIFT算法是一种基于线性尺度空间，对图像缩放、旋转甚至仿射变换保持不变的局部特征描述算子，因此被广泛地应用于机器人定位、导航和地图生成中。-This paper presents a method for extracting distinctive invariant features fro
mode
- 模式识别，用于聚类识别。通过自主学习实现模式分类功能-Pattern recognition is used to identify clusters. Achieved through self-learning pattern classification
background-model3
- 针对背景差法易受外界环境因素影响的缺点, 提出了一种基于改进K-均值聚类的背景建模方法。通过比较任意样本与该像素位置处的子类中心之间的距离, 对各个像素的观察值进行聚类, 并在聚类过程中逐步确定其类别数。一段时间的学习之后, 样本数最多的子类就构成了背景模型。仿真结果表明, 该算法即使在运动目标存在的情况下也能准确的提取出实际的 背景, 而且显著地降低了系统的存储量。-Aimed at the disadvantage that background subtraction was liab
FCM
- FCM算法是一种基于划分的聚类算法，它的思想就是使得被划分到同一簇的对象之间相似度最大，而不同簇之间的相似度最小。-FCM algorithm is a clustering algorithm based division, which makes the idea is to be divided among a cluster of objects with the greatest similarity, but the similarity between different clus
kmeans
- 在图像分割中所涉及到的k均值聚类，这个方程fuction：[IDX P] = kmeans(V, k)，要求输入样本以及聚类数目，得到聚类值以及数目。-In the image segmentation involved in k-means clustering, this equation fuction: [IDX P] = kmeans (V, k), requires the input samples and the number of clusters to give value
KMeans
- k均值聚类，有聚类数目，聚类中心，实现有效聚类。-k-means clustering, there is the number of clusters, cluster centers achieve effective clustering.
FCM
- 该代码是文章A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters的实现，附图像(the code of A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters)