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基于遗传聚类的彩色图像分割,用caj 打开,很好的毕业论文-based on genetic clustering color image segmentation, PRODUCTS opened with a good thesis
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Multiclass Spectral Clustering Algorithm,和Dominant-set算法同属于较常用的聚类算法,用于图像分割等-Multiclass Spectral Clustering Algorith m, and Dominant-set algorithm is more commonly used with the clustering algorithm for image segmentation, etc.
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提出了一种新颖的处理图像视觉聚类问题的方法。与以往关注图像局部特征和局部连续性的方法不同,本文中的方法能够提取关于图像的全局印象。为此,我们将图像分割问题转化为图划分问题并提出了划分中的一种全局判别准则——Ncut (Normalized Cut)。Ncut不仅能够衡量不同聚类之间的相异程度,还能够衡量各聚类内部的相似程度。为求解Ncut 的最优化问题,提出了一种基于广义特征值问题的高效算法,并将此算法应用于静态图像分割,取得了良好的效果。-proposed a novel image proc
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这是一个用一种改进的谱聚类方法实现图像分割的车程序-with an improved spectral clustering method of image segmentation vehicle
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用matlab实现的c均值聚类算法,用于图像分割,分割效果很好-Matlab achieved with c-means clustering algorithm for image segmentation, segmentation well
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示例程序使用K均值聚类的方法实现了对位图图像的分割即轮廓提取,程序中附带一张位图,可直接打开查看效果、-Sample programs using the K-means clustering methods have been exported to a bitmap image segmentation or contour extraction, the program comes with a digital map, can be directly open the view resu
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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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这是个基于VC++的图像处理系统,主要实现实现系统能对图像文件(bmp、 jpg、 tiff、 gif等)进行打开的功能
2.实现系统能对图像文件进行保存,另存的功能
3.实现系统能对图像文件进行打印的功能
4.实现系统退出操作的功能
5.图像分割:阈值分割、区域生长、分裂合并 、区域增长法、特征空间聚类法、用分水岭变换分割法等各种方法,实现复杂区域的分割
希望对大家有用-This is based on VC++ image processing system, t
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This files are related to implementation of the article entitled "Integrating spatial fuzzy clustering with level set methods for automated medical image segmentation".
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对《Fuzzy c-means clustering with weighted image patch for image segmentation》这篇文章进行了实现,运行速度在一分钟之内,希望对研究模糊c均值及其拓展算法的人有所帮助。运行testWIPFCM.m即可,注意改掉文件路径-On " Fuzzy c-means clustering with weighted image patch for image segmentation" This article wa
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模糊聚类分析作为无监督机器学习的主要技术之一,是用模糊理论对重要数据分析和建模的方法,建立了样本类属的不确定性描述,能比较客观地反映现实世界,它已经有效地应用在大规模数据分析、数据挖掘、矢量量化、图像分割、模式识别等领域,具有重要的理论与实际应用价值,随着应用的深入发展,模糊聚类算法的研究不断丰富-Unsupervised fuzzy clustering analysis as the main machine learning techniques is the use of fuzzy t
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IMAGE SEGMENTATION BY FUZZY C-MEANS
CLUSTERING ALGORITHM WITH A NOVEL
PENALTY TERM
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用c#和emgucv实现了hough变换检测圆和最大类间方差和k均值聚类分割图像,k均值聚类的函数可填写分类数和迭代次数的阈值,而且是基于灰度的。(ps:函数都是自己实现的,但是读取图片信息用的是emgucv自带的函数)-With c# and emgucv achieved between hough transform circle detection and maximum variance and k-means clustering image segmentation, the fu
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image segmentation with k-means algorithm and mean shift clustering and filtering
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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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Fuzzy c-means clustering (FCM) with spatial constraints (FCM_S) is an
effective algorithm suitable for image segmentation. Its effectiveness contributes not
only to introduction of fuzziness for belongingness of each pixel but also to
exploitat
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integrated spatial fuzy clustering with level set methods for automated medical image segmentation
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FCM WITH 2D CLUSTERING AND LEVEL SET METHOD
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文章Fuzzy C-Means Clustering With Local Information and Kernel Metric for Image Segmentation的代码实现(the code of Fuzzy C-Means Clustering With Local Information and Kernel Metric for Image Segmentation)
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本文以k-means算法为背景,引入信息熵相关知识,从而实现全自动分割图像。然而在利用混合高斯模型对图像进行数据分析时,会发生一定的过拟合现象,导致我们得不到预期的聚类数目。本文设计合理的合并准则,令模型简化,有效地消除过拟合现象,使得最后得到的聚类数目与预期符合。,设计合理的准则改进了图像的全自动分割方法,使得分割结果更加优化(In this paper, k-means algorithm is used as the background, and information entropy
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