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3下载：
本程序首先把图像由RGB空间转到HSI空间，然后利用彩色图像分割策略以及meanshift算法对图像进行分割最后加入边界合成。其中‘keyprogram.m’文件为主程序，‘meanshift.m’文件为调用函数，实现数据的聚类分割。-The program first the image from the RGB space to HSI space and then using color image segmentation strategy and meanshift image seg
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基于matlab的彩色图像分割，利用k聚类算法-Matlab-based color image segmentation, clustering algorithm using k
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基于Mean Shift的图像分割过程就是首先利用Mean Shift算法对图像中的像素进行聚类，即把收敛到同一点的起始点归为一类，然后把这一类的标号赋给这些起始点，同时把包含像素点太少的类去掉。然后，采用阈值化分割的方法对图像进行二值化处理。-Mean Shift Based on the process of image segmentation is the first to use the image Mean Shift algorithm for clustering of pixe
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基于Mean Shift的图像分割过程就是首先利用Mean Shift算法对图像中的像素进行聚类，即把收敛到同一点的起始点归为一类，然后把这一类的标号赋给这些起始点，同时把包含像素点太少的类去掉。然后，采用阈值化分割的方法对图像进行二值化处理 -Mean Shift Based on the process of image segmentation is the first to use the image Mean Shift algorithm for clustering of pixe
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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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采用模糊聚类算法和加核模糊聚类算法进行医学图像的分割。采用matlab编程，界面处理较好。
-Using fuzzy clustering algorithm and processing of nuclear fuzzy clustering algorithm for medical image segmentation. Using matlab programming, interface, better handling.
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用Matlab实现基于核函数的C均值聚类图像分割，实验好，好用-Using Matlab implementation of kernel-based C-means clustering image segmentation, experimental is good, easy to use
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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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image segmentation using cmeans clustering
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基于核的FCM，可以用于聚类或者图像分割，但运行速度不是很快 自己不知道如何优化-Kernel-based FCM, can be used for clustering or image segmentation, but the speed is not fast they do not know how to optimize
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图像特征提取的总结，用MATLAB模糊聚类算法进行图像分割，阀值分割及特征提取的资料和作业。-Summary of the image feature extraction, fuzzy clustering algorithm using MATLAB for image segmentation, threshold segmentation and feature extraction of data and operations.
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彩色图像分割，灰常不错。This function implements kmeans clustering on an input RGB (m x n x 3)
image. The user inputs at least two inputs: IMGIN and NCLUSTERS, and this
function will step through an interactive color segmentation using kmeans clustering. It
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本程序包括：论文SVM 用于基于块划分特征提取的图像分类，和相应的matlab实现其中图像划分以及特征提取、聚类均利用matlab6.5完成。
-The procedures include: paper by SVM for feature extraction based on block classification, and the corresponding realization of one image into matlab, and feature extraction,
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信号维数的估计，对HARQ系统的吞吐量分析，实现串口的数据采集，用于图像处理的独立分量分析，用MATLAB实现动态聚类或迭代自组织数据分析，仿真图是速度、距离、幅度三维图像，研究生时的现代信号处理的作业，表示出两帧图像间各个像素点的相对情况。- Signal dimension estimates, HARQ throughput analysis of the system, Achieve serial data acquisition, Independent component anal
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用于图像处理的独立分量分析，用MATLAB实现动态聚类或迭代自组织数据分析，使用拉亚普诺夫指数的公式。- Independent component analysis for image processing, Using MATLAB dynamic clustering or iterative self-organizing data analysis, Raya Punuo Fu index using the formula.
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利用matlab GUI实现的串口编程例子，毕设内容，高光谱图像基本处理，用MATLAB实现动态聚类或迭代自组织数据分析。- Use serial programming examples matlab GUI implementation, Complete set content, basic hyperspectral image processing, Using MATLAB dynamic clustering or iterative self-organizing data an
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从先验概率中采样，计算权重，意信号卷积的运算,并且绘制图象，用MATLAB实现动态聚类或迭代自组织数据分析。- Sampling a priori probability, calculate the weight, Convolution operation is intended to signal and image rendering, Using MATLAB dynamic clustering or iterative self-organizing data analysis.
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在MATLAB中求图像纹理特征，可实现对二维数据的聚类，用MATLAB实现动态聚类或迭代自组织数据分析。- In the MATLAB image texture feature, Can realize the two-dimensional data clustering, Using MATLAB dynamic clustering or iterative self-organizing data analysis.
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用于图像处理的独立分量分析，用MATLAB实现动态聚类或迭代自组织数据分析，模拟数据分析处理的过程。- Independent component analysis for image processing, Using MATLAB dynamic clustering or iterative self-organizing data analysis, Analog data analysis processing.
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Using MATLAB dynamic clustering or iterative self-organizing data analysis, The true extent of the value of the intermediary measure, measure the true extent of the agency based on the value of image segmentation It draws on principal component analy
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