Feature Extraction for Pose Estimation. A Comparison Between Synthetic and Real IR Imagery

Abstract

This research addressed the problem of pose estimation of three- dimensional objects given their two-dimensional IR imagery and corresponding synthetic (computer-generated) IR imagery. Features and techniques were investigated to find those which may be extendable from computer models to real- world IR imagery. GTSIG and SCNGEN were used to create the synthetic imagery. Silhouette and outline shape moments were explored as optimum features for the comparison. Employing back-propagation with momentum as the training paradigm, a two-hidden-layer neural network was able to determine the base-plane orientation of the synthetic imagery to within 7.5 degrees with better than 90% accuracy. (No conclusive results were obtained from comparison with real-world IR imagery. ) Additionally, the use of object hot spots relative to object height-to-width ratio is briefly discussed as an alternative feature/technique. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1991
Accession Number
ADA243699

Entities

People

  • Donald J. Willis

Organizations

  • Air Force Institute of Technology

Tags

DTIC Thesaurus Topics

  • Change Detection
  • Computer Vision
  • Databases
  • Depression Angles
  • Detection
  • Detectors
  • Electrical Engineering
  • Geometry
  • Information Processing
  • Laser Radar
  • Object Recognition
  • Recognition
  • Standards
  • Statistics
  • Target Recognition
  • Three Dimensional
  • Two Dimensional

Readers

  • Computer Vision.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks