搜索资源列表
K_mean
- 很好用的K均值算法，聚类效果较好，输入bmp灰度图像-Good use of K-means algorithm, clustering better, grayscale image input bmp
litekmeans
- 基于图像分割的k均值，聚类法的区域生长方法。缺点是需要提前确定分割类别-kmeans of image
KMean
- C++下利用K均值方法实现遥感影像非监督分类-remote sensing image
KMATH
- MATLAB实现K均值聚类，自己编的，可用，不用系统函数。-MATLAB realization of K-means clustering, own, use, instead of the system functions.
2012302590069
- 图像K均值聚类，使用DIB类实现，整体比较简单-Image K-means clustering, using DIB class implementation, the overall relatively simple
Classificatin
- 遥感影像分类，K均值和ISODATA算法实现，分类效果较好-Remote sensing image classification, K-means algorithm and ISODATA classification is better
KMeans
- 利用K均值对特征进行聚类，是聚类方法中最常用且容易理解的方法-k-means clustering
wet1
- 基于K均值算法对数据地图进行处理的一个代码-A code is based on K-means algorithm for processing the data map
Kmean
- 在matlab下的k-均值聚类进行图像分类分割处理-In matlab under the k-means clustering for image classification be dealt with separately
FCM(Matlab)
- FCM K均值聚类算法的matlab实现 ，-FCM matlab
KMkeen
- 基于人类视觉将图像分割成若干个有意义的区域是目标检测和模式识别的基础。图像分割属于图像处理中一种重要的图像分析技术。图像分割的基本方法是对灰度图像分割，处理图像的亮度分量，简单快速。本论文介绍了传统的图像分割与K-均值聚类算法分割，然后利用OpenCV函数将其实现，并介绍了OpenCV中图像分割相关的基本函数。-Based on the human visual image is segmented into several meaningful regions is the basis for
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
fengeTU
- 根据图像的颜色对图像进行分割，里面有多个方法，一种是RGB色彩分割，一种是K均值聚类，还有对视频处理分割，有多个测试图-According to the color image on the image segmentation, there are multiple ways, one is RGB color segmentation, one is the K-means clustering, and video processing division, a plurality of t
K_means
- K均值聚类算法 属于模式识别的一种基本算法 在此没有与opencv关联-K-means clustering algorithm A basic pattern recognition algorithm is not associated with this opencv
kmeans
- 对遥感图像进行分类，使用k均值算法,对Indian_pines数据进行分类-Remote sensing image classification, using the k-means algorithm
AP
- AP聚类算法是基于数据点间的 信息传递 的一种聚类算法。与k-均值算法或k中心点算法不同，AP算法不需要在运行算法之前确定聚类的个数。AP算法寻找的 examplars 即聚类中心点是数据集合中实际存在的点，作为每类的代表-AP clustering algorithm is based on the information transfer between data points of a clustering algorithm. K- and k-means algorithm
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.
naobanqiu
- 从脑部MRI图像中提取出左、右脑半球.对腐蚀后的二值图像的各连通区域进行标记，最后找到最大的连通区域.K均值聚类算法，根据要求有k 5或4；-Extracted the left and right hemispheres of the brain MRI image
MRIxiaonaonaogan
- 从脑部MRI图像中提取小脑和脑干，分别采用k均值聚类算法，阈值分割，形态学操作，分水岭算法，区域合并-Brainstem and cerebellum extracts the brain MRI images were used to k-means clustering algorithm, thresholding, morphological operations, watershed algorithm, region merging, etc.
MRIzuoyounaobanqiu
- 从脑部MRI图像中提取左右脑半球，分别采用k均值聚类算法，阈值分割，形态学操作，分水岭算法，区域合并-Extracted the brain MRI image left and right brain hemispheres, respectively k-means clustering algorithm, thresholding, morphological operations, watershed algorithm, region merging, etc.