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本文针对SLAM数据关联中使用最为广泛的最近邻方法作了改进，利用特征估计位置与载体预测位置之间的欧氏距离计算代替了全部特征与每个量测之间的马氏距离计算，避免了大量的矩阵乘法计算。该算法简单易行，降低了算法的计算复杂度，有利于SLAM算法的实时执行，且关联效果与全局最近邻法相同-In this paper, SLAM data association in the most widely used methods of improving the nearest neighbor, using t
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用的是局部颜色特征，再说细点是用里面的区域颜色直方图的方法。把图像归一化到256X256，把图像分成4X4块，计算16个区域的颜色直方图、、、
最后计算相似度是用欧氏距离.-Using local color feature, repeat fine-point is inside the regional color histogram method. The normalized image to 256X256, the image is divided into 4X4 blocks
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实现模式识别，实现最小欧式距离分类器的实现与形成-Achieve pattern recognition, achieve the minimum Euclidean distance classifier and the formation of implementation
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k-means(欧氏距离)聚类算法是最基本的聚类算法，是理解和应用聚类算法的基础，通过k-means(欧氏距离)聚类算法我们才可以初步了解数据挖掘的原理。-k-means (Euclidean distance) clustering algorithm is the most basic clustering algorithm, is understanding and the basis for the application of clustering algorithm, throu
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这是data mining的维度魔咒实作之一
简单介绍在资料库中当资料的dim愈高愈多，在计算Euclidean Distance时会降低,如图所示
资料皆为乱数产生-This is the dimension of data mining as one of curse is a brief introduction in the database when the information the higher the more dim, when the Euclidean Dist
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This a MFC program to test direct linear discriminant analysis (direct LDA) for face recognition. Histogram equalization is used as a preprocessing and direct LDA is used to reduce a dimension in feature space from train images. Minimum Euclidean dis
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CVAP includes 4 External validity indices, 14 Internal validity indices and 5 clustering algorithms (K-means, PAM, hierarchical clustering, SOM and etc.). It supports other clustering algorithms via loading a solution file with class labels, or by ad
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我的毕业设计，用matlab编的关键帧提取的代码，参考了光流法的代码。是基于帧差的欧式距离，均值，方差，差异系数下的关键帧提取。代码调试通过，运行结果理想，与大家分享下。-I graduated from design, matlab key frame extraction for the code, with reference to code optical flow method. Frame difference is based on the Euclidean distance,
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代码用于图形图像的像素点欧几里得距离计算转换等工作-Code for the graphic image of the Euclidean distance-based pixel conversion, etc.
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function in R software to calculate 36 clusterings of a data matrix (clusterings are in columns), using hierarchical average, cosine, centroid and single linkages, pam and neural gas and applying the Euclidean distance to the data.
Clusterings can
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通过欧几里德距离对三维空间内的数据点进行聚类：
从文件读入三列表示坐标的数据进行聚类。按项目总数和组数平均划分。首先计算两两之间的距离，选出最近的两个点，再按距离由小到大排序，找出距这两个点最近的若干个点。
除去这些点后对其余的点重复进行上述操作。-By Euclidean distance on three-dimensional space, clustering of data points: from the file read into the three coordinate
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摘要：拐点是数字图像中的一个重要信息载体 提出一种新的拐点检测算法 该算法并非寻找连续空间中曲率的离散近似计算方法，而是源于离散曲线的外观特征，推导出离散曲线上拐点处k个点对间欧氏距离平方和局部最小这一重要性质。基于该性质，本算法首先利用Freeman链码的性质.过滤掉物体边界上明显不可能成为拐点的象素，然后在剩余的边界点中通过寻找该局部最小值定位出拐点。给出了本算法与四种著名拐点检测算法的对比实验。
-Abstract: The inflection point is a digital
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classify the objects in data matrix based on the attributes
Criteria: minimize Euclidean distance between centroids and object points
For more explanation of the algorithm-classify the objects in data matrix based on the attributes
Cr
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simple grid interpolation of scatter data by using euclidean distance. Colouring z with different RGB and save in ASCII, comma delimeter-simple grid interpolation from scatter data.
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一、 本程序采用16QAM调制方式，对一串2进制信源进行调制，用升余弦滚降函数进行基带调制，再调到高频信道；在信道上加入高斯白噪声，运用匹配滤波器解调，画出解调星座图，运用最小欧氏距离译码判决，计算误比特率。-First, this program uses 16QAM modulation mode, for a string of two binary source is modulated with a raised cosine roll-off function for base-ba
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用带形状校正的腐蚀膨胀实现Euclidean距离变换-Implementation of Euclidean Distance Transforms Using Erosion and Dilation with Form Correction
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face recognition program using eigenface approach and euclidean distance method.-face recognition program using eigenface approach and euclidean distance method.
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一种车牌号码图像二值化的新方法。利用数学形态学的开运算消除图像光照不均匀影响，根据欧几里得距离确定图像阈值。-Of a license plate number of the new image binarization method. Using mathematical morphology opening operation to eliminate uneven lighting image effects, according to Euclidean distance to dete
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这是一个用于实现欧几里得距离变换的C++源代码-C++ code used to do Euclidean distance transform
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模式识别C聚类算法 主要采用四种距离 这里的例子是欧氏距离-Pattern Recognition C clustering algorithm mainly uses four kinds of examples here from the Euclidean distance
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