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On the use of error propagation for statistical validation of computer vision software
Xufei Liu1, Tapas Kanungo, Robert M Haralick
1Cisco Systems, 600 Lanidex Plaza, Parsippany, NJ 07054, USA. xuliu@cisco.com
IEEE Transactions on Pattern Analysis and Machine Intelligence
|October 22, 2005
Summary
This study presents a statistical validation method for complex computer vision software. The approach ensures accuracy in building parameter estimation by testing algorithms on controlled data, enhancing software reliability.
Area of Science:
- Computer Vision
- Photogrammetry
- Statistical Analysis
Background:
- Computer vision software is intricate, with extensive codebases prone to errors.
- Statistical predictability of algorithms on controlled data enables software validation.
- Existing methods for validating complex vision software require enhancement.
Purpose of the Study:
- To review general theories of statistical testing applicable to computer vision.
- To present an experimental methodology for validating building parameter estimation software.
- To demonstrate the statistical validation of 3D building vertex position estimation.
Main Methods:
- Review of statistical testing theories relevant to computer vision.
- Application of experimental methodology to building parameter estimation software.
- Validation using multi-image photogrammetric resection and 3D geometric constraints.
Main Results:
- Demonstrated statistical predictability of computer vision algorithms under controlled conditions.
- Successful validation of building parameter estimation software through the proposed methodology.
- Accurate estimation of 3D building vertex positions based on photogrammetric data.
Conclusions:
- Statistical validation is a viable approach for ensuring the reliability of computer vision software.
- The presented methodology effectively validates building parameter estimation algorithms.
- This work contributes to the development of more robust and accurate computer vision systems.
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