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Determination of number of clusters in K-Means Clustering and Application in color image segmenta
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基于内容的图像识别特征提取部分——k-均值聚类分割获取形状等信息-Content-based image recognition, feature extraction part of- k-means clustering segmentation information for shape
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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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matlab编写的纹理图像分割gussian滤波后k-means聚类并将不同区域用线条表示出来除m程序还包括测试图片-matlab prepared texture image segmentation gussian filtered k-means clustering will be expressed in different regions of the use of lines, inter m program also includes test pictures
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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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对电路板的图像进行分割，可以提取电路板中的目标物，以对电路板进行检测。文章使用K均值聚类算法完成对电路板图像的分割，针对传统的K均值聚类算法的不足，提出了使用直方图波形的有效波峰个数来确定K值的大小，并通过使用一种比传统的绝对误差的表示更简洁的表达式，达到了快速分割的目的。对一些电路板图像分割的实验结果表明，文章的方法能够根据目标物的数目有效的确定K值的大小，且比传统的K均值算法减少了运算量及计算时间。-On the circuit board image segmentation, to ex
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code for image clustering using k-means
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KMEANSK Performs K-means clustering given a list of feature vectors and k
The argument k indicates the number of clusters you want the data to be
divided into. data_vecs (N*R) is the set of R dimensional feature
vectors for N data points. E
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使用k均值法对一组数据进行聚类，画出结果图像-Use the k-means clustering, a set of data to draw the resulting image
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The attachment consists of a source code that performs malarial segmentation using k-means clustering.It also contains HUI conversion of an malarial image.Water shed algorithm applied on a cel image , and detection of water ridge lines for dental x r
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This paper presents a new approach to image segmentation using Pillar K-means algorithm. This
segmentation method includes a new mechanism for grouping the elements of high resolution images in order to
improve accuracy and reduce the computation
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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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此代码为matlab代码，分为两个部分。第一部分实现K均值聚类算法应用它来压缩图像。在第二部分中，你将使用主成份分析法pca来实现人脸图像的低维表示。
-This code for the matlab code, is divided into two parts. The first part of the implementation of the K means clustering algorithm to compress the image. In the second par
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一种k-means聚类算法的图像分割例程，很好用。里面包括主函数和子函数的m文件以及使用的图片。-One kind of k-means clustering image segmentation algorithm routines, very good use. Which includes the main functions and subroutines m documents and pictures used.
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表示出两帧图像间各个像素点的相对情况，基于K均值的PSO聚类算法,实现了图像的加水印，去噪，加噪声等功能。- Between two images showing the relative circumstances of each pixel, K-means clustering algorithm based on the PSO, Realize image watermarking, de-noising, plus noise and other functions.
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In this post, we are going to share with you, the MATLAB implementation of Color Quantization and Color Reduction of images, using intelligent clustering approaches: (a) k-Means Algorithm, (b) Fuzzy c-Means Clustering (FCM), and (c) Self-Organizing M
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包括最后计算压缩图像的峰值信噪比和压缩效果的源码,利用自然梯度算法，基于K均值的PSO聚类算法。- Including the final calculation of the compressed image peak signal to noise ratio and compression of the source, Use of natural gradient algorithm, K-means clustering algorithm based on the PSO.
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完整的图像处理课设，包含所有源代码，汽车图像，包含收发两个客户端程序，基于K均值的PSO聚类算法。- Complete class-based image processing, contains all of the source code, auto image, Transceiver contains two client programs, K-means clustering algorithm based on the PSO.
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基于K均值的PSO聚类算法,在matlab环境中自动识别连通区域的大小,在MATLAB中求图像纹理特征。- K-means clustering algorithm based on the PSO, Automatic identification in the matlab environment the size of the connected area, In the MATLAB image texture feature.
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Andrew Ng Cousera 机器学习K-means勇于图像压缩 以及主成分分析PCA用在人脸识别，源代码以及说明文档。(Andrew Ng Cousera machine learning , the K-means clustering algorithm and apply it to compress an image. In the second part, you will use principal component analysis to find a low-dime
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