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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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In this project ,we propose a color based segmentation method that uses the c means clustering technique to track tumor objects in magnetic resonance (MR) brain images. The key concept in this color based segmentation algorithm with k means means to
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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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