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.
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