Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
Parameter estimation and hypothesis testing in linear models
Parameter estimation and hypothesis testing in linear models
Three-dimensional computer vision: a geometric viewpoint
Three-dimensional computer vision: a geometric viewpoint
Modeling and performance characterization of parameter estimation using perspective geometry
Modeling and performance characterization of parameter estimation using perspective geometry
Performance Assessment Through Bootstrap
IEEE Transactions on Pattern Analysis and Machine Intelligence
Performance characterisation in computer vision: statistics in testing and design
Imaging and vision systems
Statistical Optimization for Geometric Computation: Theory and Practice
Statistical Optimization for Geometric Computation: Theory and Practice
Fundamentals of Software Engineering
Fundamentals of Software Engineering
A methodology for quantitative performance evaluation of detection algorithms
IEEE Transactions on Image Processing
Performance measures for object detection evaluation
Pattern Recognition Letters
| Hi-index | 0.14 |
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.