Error Propagation and Statistical Validation of Computer Vision Software

Abstract

Computer vision software is complex, involving many tens of thousands of lines of code. Coding mistakes are not uncommon. When a vision algorithm is run on controlled data which meet all the algorithm assumptions, the results are often statistically predictable. This renders it possible to statistically validate the algorithm and its associated theoretical derivations. In this paper we review the general theory of some relevant kinds of statistical tests and then illustrate the experimental methodology of statistical algorithm validation to validate a program that estimates parameters of buildings in aerial photographs. This program estimates the 3D positions of building vertices based on input data obtained from multi-image photogrammetric resection calculations and 3D geometric information relating some of the points, lines and planes of the building to each other.

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Document Details

Document Type
Technical Report
Publication Date
Feb 01, 2001
Accession Number
ADA458742

Entities

People

  • Robert M. Haralick
  • Tapas Kanungo
  • Xufei Liu

Organizations

  • University of Maryland

Tags

Communities of Interest

  • Air Platforms

DTIC Thesaurus Topics

  • Abstracts
  • Aerial Photographs
  • Algorithms
  • Computer Vision
  • Computers
  • Computing-Related Activities
  • Distribution Functions
  • Images
  • Information Operations
  • Language
  • Photographs
  • Photography
  • Statistical Algorithms
  • Statistical Tests
  • Universities
  • Validation

Fields of Study

  • Computer science
  • Engineering

Readers

  • Computational Modeling and Simulation
  • Computer Vision.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference