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。
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.
当图像数量增长到一定数量后，基于浮点矢量形式表示的图像特征就不适合放置在内存中，欧氏距离的计算也将造成较大的时间开销。
For example, it costs more for storage and the distance computation is quite complex. With the number of images grows, it will be unsuitable for vector based image feature to stay in memory.
浮点矢量图像特征维数较高，且通常以欧氏距离作为矢量之间的相似度定义。
Float vector based image feature has high dimension and usually makes use of Euclidean distance as its similarity definition.
将待检索图像所有值域块的欧氏距离求平均，此平均欧氏距离较小的几幅图像即为检索出的图像。
Average the Euclid distances of all the range blocks in the retrieval image, then the images whose average Euclid distances are smaller are selected as the retrieval images.
首先建立环境的高斯背景模型，从步态视频序列中提取轮廓图像，计算质心以及轮廓线上的点到质心的欧氏距离。
It creates Gaussian Mixture Model for each scenario, and contour of gait is extracted from binary silhouette for Euclidean distance between the centroid and any pixel on it.
应用奇异值欧氏距离作为两幅图像相似程度的度量尺度从而实现轴承的故障诊断。
The Euclidean distance of singular values is used as the measurement of similarity between two gray charts, realizing fault diagnosis.
应用奇异值欧氏距离作为两幅图像相似程度的度量尺度从而实现轴承的故障诊断。
The Euclidean distance of singular values is used as the measurement of similarity between two gray charts, realizing fault diagnosis.
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