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本实验基于K-Means聚类算法思想实现了字符分割，因为车牌规定是7位的，所以K取7。另外本实验对K-Means算法进行了改进，充分考虑了初始点的设置及迭代结束条件。实验结果证明这种改进的K-Means算法实现车牌字符分割是快速、有效的。-In this study, K-Means clustering algorithm based on the ideology of the character segmentation, because the license plate require
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利用相位一致性提取图像边缘，K-means聚类后区域生长进行图像分割，附参考论文。-Using phase coherence image edge extraction, K-means clustering image after region growing segmentation, attached reference paper.
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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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用于图像处理，K-MEANS聚类实现图像的分割和识别，是一种重要的图像处理方法-For image processing, K-MEANS Clustering achieve image segmentation and recognition, is an important image processing method
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K均值聚类算法 由于对纹理图像使用灰度共生矩阵分割效果不明显 因此该算法使用图像频域进行处理-K-means clustering algorithm because of the texture image segmentation using the gray co-occurrence matrix effect was not obvious, therefore use the algorithm for processing images in frequency domain
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K均值聚类算法 对图像颜色进行聚类 然后对图像分割-K-means clustering algorithm to cluster the color image segmentation and then
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Image segmentation k mean clustering
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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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利用K均值聚类实现彩色图像分割,效果不错-Color Image segmentation using K-means clustering
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简单的灰度图像的K均值聚类分割，Matlab实现-gray image segmentation using K-means clustering by matlab.
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KMeans图像聚类分割：用K-Means聚类方法来对图像进行分割。主要是对图像的颜色进行聚类。开发环境：VC6，需要安装OpenCV。-KMeans clustering in image segmentation: with K-Means clustering approach to image segmentation. Mainly the color of the image cluster. Development Environment: VC6, need to install
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该方法采用gabor提取纹理特征，最后采用k-means方法聚类进行图像分割-The method uses gabor texture features extraction, and finally using k-means clustering methods for image segmentation
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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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K-means算法是一种动态聚类方法，这种方法先选择若干样本作为聚类的中心，在按某种聚类准则（通常采用最小距离原则）使各种样本向各个中心积聚，从而得到初始的分类，然后，判断分类的合理性，如果不合理，就修改分类，如此反复的修改聚类的迭代运算，直到合理为止。-K-means algorithm is a dynamic clustering method, this method, select the number of samples as a cluster center in the clu
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KMEANS源代码：k-means 聚类算法 图像处理分割-KMEANS源代码：k-means clustering algorithm for image processing segmentation
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一种基于颜色的分割，使用聚类算法中的K均值算法。本例主要用到的函数是色彩空间转换函数makecform和applycform，对于K均值聚类使用kmeans函数。-Based on color segmentation, using clustering algorithm K-means algorithm. In this case the main function used is the color space conversion function makecform and appl
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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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谱聚类，基于k均值的聚类方法，能够分割高维空间的数据点，-Spectral clustering based on k-means clustering method, it is possible segmentation data point of the high-dimensional space,
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