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this a code to segment the color texture using Gabor filter. It uses the initial segmentation using kmeans clustering.-this is a code to segment the color texture using Gabor filter. It uses the initial segmentation using kmeans clustering.
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四种聚类算法源代码及示例代码,本程序的最终目的是形成一套标准的用于聚类、可扩展的工具。包括的内容有1. 聚类算法:Kmeans和Kmedoid算法、FCMclust, GKclust, GGclust算法 2. 评估分类原型:程序可以在二维图像上绘制出聚类的结果 3. 验证:程序给每一个算法提供验证机制,每个聚类算法会统计Partition Coefficient (PC), Classification Entropy (CE), Partition Index (SC), Separatio
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Kmeans图像聚类 Kmeans图像聚类 Kmeans图像聚类-Kmeans image clustering Kmeans image clustering Kmeans image clustering Kmeans image clustering Kmeans image clustering
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这是K-means聚类算法的源代码,可实现任意多统计数据的归类处理。代码为原创,用途主要在数理统计方面,而不是常见的图像处理等方面。内附详细的使用说明。-This is the K-means clustering algorithm source code, enabling the classification of any number of statistical data processing. Code for my original, uses mainly in mathemat
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This function implements kmeans clustering on an input RGB (m x n x 3) image.
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Segments all the planes in an image.... using Kmeans Clustering algorithm
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2D-Kmeans clustering algorithm. It can be used for audio(source localization) and image clutstering(object clustering in an image)-2D-Kmeans clustering algorithm. It can be used for audio(source localization) and image clutstering(object clustering i
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image classification algorithm : k-means clustering implementation is provided herewith, which is an unsupervised clustering method
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matlab 基于K均值聚类算法的图像分割-K-means clustering algorithm for image segmentation
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MATLAB code for Adaptive kmeans Clustering for Color and Gray Image.
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用kmeans算法处理资源3号卫星图像,用欧式距离来计算聚类中心,在用前后两次聚类之间的误差来决定迭代次数(Kmeans algorithm is used to deal with the resource 3 satellite image, and the Euclidean distance is used to calculate the clustering center. The number of iterations is determined by the error bet
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THIS KMEANS CLUSTERING FOR IMAGE....
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K means clustering for image.
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ap clustering in image extraction
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该课题为基于kmeans的聚类分割,输入一张彩色图像,可以选择需要分割成多少类,就会以不同颜色区分不同的块,带有GUI界面,操作丰富。(This topic is based on Clustering Segmentation of kmeans. Input a color image, you can choose how many categories you need to segment, and then different blocks will be distinguished
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