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Brain MR Image segmentation using k-means clustering
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K均值聚类算法，此算法自动把数据组聚类分割，效果很好-K-means clustering algorithm, this algorithm automatically data set clustering segmentation with good results
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matlab中使用k-means聚类实现图像分割-using k-means clustering matlab image segmentation
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基于纹理的图像分割的Matlab源代代码,应用小波和K均值聚类-matlab
-Texture-based image segmentation Matlab source generation of code, the application of wavelet and K-means clustering-matlab
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k均值聚类二值化字符图像，通过把RGB图像分割成不同区域-k-means clustering binarized character image, by RGB image segmentation into different regions
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将彩色图像，转化到Lab空间，利用K-means类聚对图像进行分割-The color image is transformed into Lab space, the use of the class of K-means poly image segmentation
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K means+PSO for Image Segmentation
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Image Segmentation using K means
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利用k均值算法对图像色彩进行聚类分割，将图像中色彩相近的分割成一块。一种很好的图像分割算法。-K-means clustering segmentation algorithm for image color, the color in the image is divided into a similar. A good image segmentation algorithm.
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基于K-means聚类的图像分割算法，想法比较简单，实现很方便，运行速度很快，有例图。-Image segmentation based on K-means clustering algorithm, the idea is relatively simple, very convenient, fast run case diagram.
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K-Means Image Segmentation
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基于K-means聚类的超像素分割，可用于图像处理和模式识别-superpixel segmentation
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首先进行帧差处理，根据得到的帧差进行k均值算法，完成图像分割-First frame difference processing, k-means algorithm based on frame difference, to complete the image segmentation
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图像分割中K-means聚类算法的matlab实现-Image segmentation in the K-means clustering algorithm matlab implementation
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k均值图像分割源码，可对灰度图像进行三类聚类-k-means image segmentation source, three types of clustering of grayscale images
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k 均值算法用于图像分割,根据灰度图自适应地确定分类数并进行分割，需要先用k均值初始化-k’-means algorithm for image segmentation, based on grayscale adaptively determine the number of classification and segmentation, you need to use the k-means initialization
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使用k-means方法对图像进行分割,可以得到较为不错的效果-Using k-means method for image segmentation, good results can be obtained more
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使用K-means聚类方法实现图像分割，代码可用，操作简单-Using K-means clustering method of image segmentation, the code is available, easy to operate
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this package provides the segmentation using k means algorithm
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Image Segmentation is vital role in nowadays.
The K-means algorithm usually the initial centroids for the K-means clustering are determined randomly so that the determined centroids may reach the nearest local minima, not the global optimum.
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