搜索资源列表
K-means
- k-means 算法 step1 初始化K个质心 step2 将所有的点分配给最近的质心 step3 更新质心 step4 若质心都没用变化,则停止,否则返回step2 -k-means algorithm is initialized step1 step2 K a center of mass of all the points assigned to the nearest centroid centroid step3 step4 update no us
PamCSharp
- k中心点聚类算法的具体实现,代码已优化,可以顺利运行。-K centroid clustering algorithm’s realization, the code has already been optimized, and can be smoothly operated.
Cpp1
- 距离与相异度,然后介绍一种常见的聚类算法——k均值和k中心点聚类-Distance and dissimilarity, and then introduce a clustering algorithm- k mean and k-medoids clustering
qt_open_surf
- 在上篇博客特征点检测学习_1(sift算法) 中简单介绍了经典的sift算法,sift算法比较稳定,检测到的特征点也比较多,其最大的确定是计算复杂度较高。后面有不少学者对其进行了改进,其中比较出名的就是本文要介绍的surf算法,surf的中文意思为快速鲁棒特征。本文不是专门介绍surf所有理论(最好的理论是作者的论文)的,只是对surf算法进行了下整理,方便以后查阅。 该代码的作者给出的主函数实现了6中功能,包括静态图片特征点的检测,视频中特征点的检测,图片之间的匹配,视频与图片之间的匹配
k-mediods
- 用于图像特征的K中心点聚类(k-mediods)的matlab实现。-k-mediods implements by matlab, used for cluster of image feature
UyghurTextDetection
- 利用Harris角点检测出维吾尔文视频图像中的角点,这些角点可能包含维吾尔文也可能不包含维吾尔文,将文字角点聚类成为文字区域,最后利用维吾尔文的基线特征来验证是否是真实的文字区域。-By Harris corner detection Uighur video image corner, these corners may contain the baseline characteristics of the Uighur Uighur may not contain Uighur text c
Ncut-Clustering
- 随机点的生成,基于图形的规范化分割使点聚类-product random points, graph-based for points segment and points clustering
data4cluster-with-the-data
- 本例子附带数据通过K中心点聚类算法,将案例聚成4类,并计算相似性矩阵,用logrank函数检验P值-The example of incidental data by K center point clustering algorithm, the case clustered into four categories, and calculate the similarity matrix logrank function test P value
matlab_k_clustering
- 基于matlab的k中心点聚类算法,适于matlab和聚类分析的入门学习-failed to translate
Kmeans
- K-均值聚类算法,是一种随机选取数个数据中心进行点聚类处理进而生成分类的数据挖掘算法,具有很好的学习功能。-K-means clustering algorithm is a randomly selected number of data center point clustering process thereby generating classification data mining algorithms, with good learning function.
kmeansCluster
- 用Java实现kmeans(画布上画点聚类)-Java implementation kmeans (on canvas painting point clustering)
kmedoids1
- matlab下的k中心点聚类算法,matlab内置仅有k均值聚类算法-k under the center clustering algorithm matlab, matlab built-k-means clustering algorithm only
HarrisDetector
- Harris角点检测是一种经典的角点检测方法,该代码中包含 1.原始的直接调用OpenCV实现角点检测; 2.封装自定义类来改进角点检测; 3.实现了增加容忍距离解决特征点聚类是角点分布均匀-Harris corner detection is a classical corner detection method, the code contained 1. The original of directly calling OpenCV corner detection to
Arithmetic
- Aprior算法,Fp树算法,K均值,k中心点聚类算法,都可实现-Aprior algorithm, Fp tree algorithm, K-means, k center clustering algorithm can be realized
SLIC_segment
- SLIC 超像素分割算法,在MATLAB平台上实现的算法,其核心为:首先均匀地指定聚类中心,然后根据features将周围的点聚类。- SLIC super pixel segmentation algorithm implemented on the MATLAB platform, the core idea is: first to specify the cluster center, and then according to the features clustering aro
_data.tar
- 实现三维点云条件欧式聚类,附带点云数据和CMakelists文件(Three dimensional cloud conditions to achieve the European clustering, with cloud data and CMakelists files)
dbscan
- 可以将数据分为几个类别,找出异常点并排除故障点。(You can classify the data, find out the outliers and troubleshoot them)
kmeans2
- 用于构建数据概要,用于分类、模式识别、假设生成和测试;用于异常检测,检测远离群簇的点。(Used to build data outlines for classification, pattern recognition, hypothesis generation and testing; for anomaly detection, detecting points away from the cluster.)
K_means
- 本代码可以根据类的初始中心位置和初始点总数,返回每个类的中心位置以及属于哪一类。(This code can return to the central position of each class and what class it belongs to, based on the initial center location and the total number of initial points of the class.)
FCMClust
- 实现对点云的分类,利用该算法可以自动化的实现点云的分类(point cloud classification)