Corpus ID: 16930804

Statistical Validation of Computer Vision

@inproceedings{SoftwareXufei1996StatisticalVO,
  title={Statistical Validation of Computer Vision},
  author={SoftwareXufei and Liu and T. Kanungo and R. Haralick},
  year={1996}
}
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… Expand

References

SHOWING 1-10 OF 18 REFERENCES
Propagating Covariance in Computer Vision
  • R. Haralick
  • Mathematics, Computer Science
  • Theoretical Foundations of Computer Vision
  • 1998
This paper describes how to propagate approximately additive random perturbations through any kind of vision algorithm step in which the appropriate random perturbation model for the estimatedExpand
Parameter estimation and hypothesis testing in linear models
  • K. Koch
  • Mathematics, Computer Science
  • 1988
TLDR
This textbook on theoretical geodesy deals with the estimation of unknown parameters, the testing of hypothesis and the estimationof intervals in linear models and most of the necessary theorems of vector and matrix-algebra and the probability distributions for the test statistics are derived. Expand
Power functions and their use in selecting distance functions for document degradation model validation
TLDR
A statistical methodology is outlined to compare the various validation schemes that result by using different distance functions that is general enough to compare any two validation schemes. Expand
Numerical methods for unconstrained optimization and nonlinear equations
TLDR
Newton's Method for Nonlinear Equations and Unconstrained Minimization and methods for solving nonlinear least-squares problems with Special Structure. Expand
Practical Methods of Optimization
Preface Table of Notation Part 1: Unconstrained Optimization Introduction Structure of Methods Newton-like Methods Conjugate Direction Methods Restricted Step Methods Sums of Squares and NonlinearExpand
Practical optimization
TLDR
This ebook Practical Optimization by Philip E. Gill is presented in pdf format and the full version of this ebook in DjVu, ePub, doc, txt, PDF forms is presented. Expand
Modeling and performance characteri- zation of 3D parameter estimation using perspec- tive geometry
  • Ph.D. Dissertation,
  • 1995
Xufei Liu \Modeling and performance characterization of 3D parameter estimation using perspective geometry
  • Xufei Liu \Modeling and performance characterization of 3D parameter estimation using perspective geometry
  • 1995
\Multivariate Hypothesis Testing for Gaussian Data: Theory and Software
  • \Multivariate Hypothesis Testing for Gaussian Data: Theory and Software
  • 1995
Propagating covariance in com- puter vision.
  • In Proc. of IAPR Int. Conf. on Pattern Recognition,
  • 1994
...
1
2
...