On the Use of Error Propagation for Statistical Validation of Computer Vision Software

Content Provider	CiteSeerX
Author	Liu, Xufei Kanungo, Tapas Haralick, Robert M.
Abstract	Abstract—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. Index Terms—Statistical analysis, multivariate hypothesis testing, 3D parameter estimation, error propagation, software validation, software engineering. 1
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Subject Keyword	Computer Vision Software Error Propagation Statistical Validation Building Parameter Estimation Software Experimental Methodology Statistical Testing Multivariate Hypothesis Testing Parameter Estimation Geometric Information Index Term Statistical Analysis Relevant Kind Abstract Computer Vision Software Multi-image Photogrammetric Resection Calculation Many Ten Building Vertex Software Validation Software Engineering Vision Algorithm Associated Theoretical Derivation Algorithm Assumption Coding Mistake General Theory Input Data
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