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利用K均值进行图像分割，无需利用“苹果为红色”这个先验知识，而只需利用图像本身的特征——前景与背景区别较明显-The use of K-means image segmentation, without the use of " red apple" This a priori knowledge, and simply use the characteristics of the image itself- the more obvious differences betwe
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K均值聚类算法，可用于图像分割，算法简单有效-K-means clustering algorithm can be used to image segmentation and it is simple and effective
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K-means图像分割，简单方便的经典图像分割方法~-K-means image segmentation
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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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matlab 基于K均值聚类算法的图像分割-K-means clustering algorithm for image segmentation
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在opencv环境下使用fuzzy c means和kmeans算法实现图片像素RGB值聚类，希望对大家有帮助-using fuzzy c means and k-means algorithm to realize the RGB image segmentation in opencv.
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This program illustrates the Fuzzy c-means segmentation of an image.
This program converts an input image into two segments using Fuzzy k-means
algorithm. The output is stored as fuzzysegmented.jpg in the current directory.
This program ca
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image segmentation using k-means partitional clustering algorithm, matlab code
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The K-means algorithm has been widely used in the segmentation of color images for many applications, such as color blood corpuscle accounting, natural image segmentation, face detection of fruit accounting, detection of oil overflow, and biology mic
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基于K-means聚类算法对彩色图象进行分割，聚类的个数可由自己定义.-Based on K- means clustering algorithm for color image segmentation,and the number of clustering can be defined by yourself.
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image segmentation with k-means
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rgb转化为lab空间，k-means聚类，基本的图像处理如腐蚀膨胀等，分水岭分割方法分割标记-rgb converted to lab space, k-means clustering, basic image processing such as corrosion expansion, etc., watershed segmentation segmentation mark
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基于人类视觉将图像分割成若干个有意义的区域是目标检测和模式识别的基础。图像分割属于图像处理中一种重要的图像分析技术。图像分割的基本方法是对灰度图像分割，处理图像的亮度分量，简单快速。本论文介绍了传统的图像分割与K-均值聚类算法分割，然后利用OpenCV函数将其实现，并介绍了OpenCV中图像分割相关的基本函数。-Based on the human visual image is segmented into several meaningful regions is the basis for
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在图像分割中所涉及到的k均值聚类，这个方程fuction：[IDX P] = kmeans(V, k)，要求输入样本以及聚类数目，得到聚类值以及数目。-In the image segmentation involved in k-means clustering, this equation fuction: [IDX P] = kmeans (V, k), requires the input samples and the number of clusters to give value
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根据图像的颜色对图像进行分割，里面有多个方法，一种是RGB色彩分割，一种是K均值聚类，还有对视频处理分割，有多个测试图-According to the color image on the image segmentation, there are multiple ways, one is RGB color segmentation, one is the K-means clustering, and video processing division, a plurality of t
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实现图像的Kmeans聚类，完美实现图像分割-The K Means clustering of image, the perfect image segmentation
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西电 电子工程学院 智能科学与技术
DSP智能平台实验 C语言程序代码 K-means 图像分割-West Electronics Engineering, Science and Technology, Intelligent Intelligent DSP experimental platform C language code K-means image segmentation
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运用k-means算法对图像进行分割处理。-Using k-means algorithm for image segmentation process.
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Adaptive K means algorithm for medical image segmentation , It is very useful for ultrasound images-Adaptive K means algorithm for medical image segmentation , It is very useful for ultrasound images
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EM 算法是求参数极大似然估计的一种方法，它可以从非完整数据集中对参数进行估计，是一种非常简单实用的学习算法。这种方法可以广泛地应用于处理缺损数据、截尾数据以及带有噪声等所谓的不完全数据，可以具体来说，我们可以利用EM算法来填充样本中的缺失数据、发现隐藏变量的值、估计HMM中的参数、估计有限混合分布中的参数以及可以进行无监督聚类等等。-Expectation Maximization image segmentation
Input:
ima: gr
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