关于误差传播在计算机视觉软件统计验证中的应用。
On the use of error propagation for statistical validation of computer vision software.
作者信息
Liu Xufei, Kanungo Tapas, Haralick Robert M
机构信息
Cisco Systems, 600 Lanidex Plaza, Parsippany, NJ 07054, USA.
出版信息
IEEE Trans Pattern Anal Mach Intell. 2005 Oct;27(10):1603-14. doi: 10.1109/TPAMI.2005.203.
Computer vision software is complex, involving many tens of thousands of lines of code. Coding mistakes are not uncommon. When the vision algorithms are run on controlled data which meet all the algorithm assumptions, the results are often statistically predictable. This renders it possible to statistically validate the computer vision software and its associated theoretical derivations. In this paper, we review the general theory for some relevant kinds of statistical testing and then illustrate this experimental methodology to validate our building parameter estimation software. This software estimates the 3D positions of buildings vertices based on the input data obtained from multi-image photogrammetric resection calculations and 3D geometric information relating some of the points, lines and planes of the buildings to each other.
计算机视觉软件非常复杂，涉及数万行代码。编码错误并不罕见。当视觉算法在符合所有算法假设的受控数据上运行时，结果通常在统计上是可预测的。这使得对计算机视觉软件及其相关理论推导进行统计验证成为可能。在本文中，我们回顾了一些相关统计测试的一般理论，然后说明这种实验方法来验证我们的建筑物参数估计软件。该软件基于从多图像摄影测量后方交会计算获得的输入数据以及建筑物的一些点、线和面之间的三维几何信息来估计建筑物顶点的三维位置。
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