搜索资源列表
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k均值图像分割，经典的图像分割方法，算法简单，效果好。-k-means image segmentation
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K-meansK均值聚类在无监督的情况下选择图像特征的算法-K-meansK means clustering in the case of unsupervised image feature selection algorithm
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快速K均值聚类图像分割算法源代码，能很好的实现图像的分割处理-Fast K-means clustering algorithm for image segmentation source code, can achieve very good to deal with image segmentation
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用matlab实现的图像k均值分割，很好用的，以经过验证-Using matlab to achieve k-means image segmentation, well used to a proven
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K均值算法实现图像的分割，分割效果可以达到预期的效果-K-means algorithm to achieve image segmentation, segmentation results can be achieved the desired results
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K均值 很好用的K均值代码 -K-means K-means used in a very good code
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Image segmentation k mean clustering
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image segmentation K-means for image of color space RGB-image segmentation K-means for image of color space RGB
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k-均值聚类法用于各种图像的聚类、分割问题，希望可以对您有利-k-means clustering method for a variety of image clustering, segmentation
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将K均值算法用于图像分割,输入的是彩色图像,转换为灰度图像进行分割,输出结果为灰度图像.利用灰度做为特征对每个像素进行聚类,由于光照等原因,有时应该属于一个物体的像素,其灰度值也会有很大的差别,可能导致对该像素的聚类发生错误.在分割结果中,该物体表面会出现一些不同于其它像素的噪声点,因此,算法的最后,对结果进行一次中值滤波,以消除噪声,达到平滑图像的作用-The K means algorithm for image segmentation, the input is a color imag
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k-mean Algorithm for segmentation of digital image
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matlab图像分割实例，K均值用于图像分割，割调用库函数实现，带注释，简单易懂-matlab image segmentation examples, K means for image segmentation, cutting calls library functions, with comments, easy to understand
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k-means image segmentation algorithm
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台湾大学几位学者写的基于K-均值算法的快速图像分割，用到基于HSV颜色空间的直方图-Fast Image Segmentation Based on K-Means
Clustering with Histograms in HSV Color Space
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基于纹理度量的图像分割，适用于遥感图像，主要利用到K-mean算法。-Image segmentation based on texture measurement for remote sensing image, the main advantage of K-mean algorithm.
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对彩色图像进行K均值的图像分割，可用于模式识别-K on the mean color image segmentation, can be used for pattern recognition
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K均值图像检测，GUI界面，可对彩色图像分割-K-means image detection, the GUI interface, color image segmentation
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利用k-means聚类对图像进行分割,在MFC中实现。-k-means image segmentation by c++
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Clustering is a way to separate groups of objects. K-means clustering treats each object as having a location in space. It finds partitions such that objects within each cluster are as close to each other as possible, and as far from objects in other
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k-means 聚类和分割的图像处理方法(k-means and segement image process)
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