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image Euclidean distance 图像欧式距离
First, the counterparts of the points on image in camera coordinate system (CCS) are found by utilizing two properties, namely collinearity and Euclidean distance invariability. 首先,利用共线性和欧氏距离不变性这两个特性可以求得图像中的点在相机坐标系(CCS)中的坐标。
With an Euclidean distance based classifier,each nonoverlapping window of the texture image is then assigned to its corresponding category. 利用基于欧几里得距离的分类器,每个纹理图像不相重叠的图像窗被确定到属于它的那一类。
We choose the Euclidean distance as a similarity coefficient. 将相似性系数E引人菌株相似性分析，大大提高了耐药谱分型的辨别力和重复性。
This paper gives an algorithm for complete Euclidean distance transformation on the binary image on the basis of the contour scanning. 本文采用基于围线扫描的思想,提出了一个在二值图像中进行完全欧氏距离变换的算法。
A new method using Fourier radius descriptor to compute the area of image and using Euclidean distance to determine the similarity of boundary is educed too. 并给出了利用半径描述子计算图像区域面积以及利用欧几里德距离判定边界相似度的方法。
This paper eliminates the image non-uniform brightness using mathematical morphological open operation and obtains the image threshold value according to the Euclidean distance. 利用数学形态学的开运算消除图像光照不均匀影响,根据欧几里德距离确定图像阈值。
Finally, weighted Euclidean distance classifier is utilizedin recognition. 最后利用加权欧氏距离分类器进行识别。
The similarity is decided by the Euclidean distance of the gray histograms. 利用各区域灰度直方图的欧氏距离判定相似区域。
Finally, the Euclidean distance between two images normalized moment vectors gives the measure of similarity, and constructs a shape-based image retrieval system. 最后,对特征向量进行高斯归一化,用欧氏距离度量图像间的相似度,构建了一个基于形状特征的图像检索系统。
The relative Weighted Euclidean Distance(WED) classifier is proposed as an improvement of the classifier. 通过对WED(Weighted Euclidean Distance)分类器的改进研究,提出了相对WED分类器的新概念,进一步提高了系统的识别能力。
This method include 3D Euclidean distance transform, computer the Hessian matrix in every voxel,and visibility test. 主要步骤包括:三维欧几里德距离变换,求Hessian矩阵,可视化检测几个步骤。
The technique supports Euclidean distance measure and L-shift Euclidean distance measure. 这种方法支持欧几理德距离标准和 L -平移欧几理德距离标准 .
All the process of clustering based on the Euclidean distance among data vectors. 聚类过程都是根据数据之间的Euclidean(欧几里得)距离。
During the recognition phase, Euclidean Distance representing VQ distortion in this project is calculated. 在认识阶段期间，通过对欧几里德距离代表VQ的计算来减少失真。
Euclidean distance transform is one of most useful distance transform algorithms. It defines the distance of the line between two points in the space. 其中，欧几里德距离转定义了空间两点间的直线距离，它是一种最常见的距离转换方式，在相关领域中，尤其在图像处理中，它的应用十分广泛。
Experimental results show that retrieval effectiveness is the highest for E+Gibbs and the lowest for the Euclidean distance. 实验结果证明E+吉布斯的检索效果最好而欧氏距离的检索效果最差。
Finally the algorithm discovers all time -series patterns by computing Euclidean distance between any two subsequences in each box. 最后通过计算每个盒子中任意两个子序列间的欧几里德距离来发现所有的模式。
Finally, with the step-by-step scheme, we implemented the fast hardware algorithm for Euclidean distance transform based on two-dimension images. 最后，根据硬件算法的实现原理，本文采用stp-by-step的设计方案，选择相关硬件，实现了一种基于二维图像的欧几里德距离转换算法电路。
The global features and local features will be recognized and sorted by weighting Euclidean distance and the minimum distance, respectively. 通过加权欧几里德距离和最小距离分别对全局特征和局部特征进行分类识别。