A Hybrid Neural Model for Target Recognition

Abstract

The invariance principle is one of the important design consideration in target recognition. Some theoretical aspects of this principle were investigated. A new set of affine invariant features were developed. Geometrical examples are given, and features generated using this feature extraction technique are demonstrated. A novel artificial neural network model was developed to analyze these features and perform classification of the targets. This network acts as a dynamic model to establish classes of targets in a nonlinear fashion. Recognition is based on the combination of a unique set of features and the newly developed neural network model. This target recognition approach demonstrates that recognition can be obtained despite target orientation, size, or aspect angle.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 1991
Accession Number
ADA245053

Entities

People

  • A. Farsaie
  • J. J. Fuller
  • L. E. Elkins

Organizations

  • Naval Surface Warfare Center

Tags

Communities of Interest

  • Energy and Power Technologies
  • Materials and Manufacturing Processes
  • Weapons Technologies

DTIC Thesaurus Topics

  • Abstracts
  • Accuracy
  • Algorithms
  • Aspect Angle
  • Computers
  • Extraction
  • Feature Extraction
  • Neural Networks
  • Orientation (Direction)
  • Pattern Recognition
  • Recognition
  • Rotation
  • Target Classification
  • Target Recognition
  • Translations
  • Two Dimensional
  • Weapons

Readers

  • Computational Fluid Dynamics (CFD)
  • Mathematical Modeling and Probability Theory.
  • Sensor Fusion and Tracking Systems.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Neural Networks