Position, Scale, and Rotation Invariant Optical Pattern Recognition for Target Extraction and Identification

Abstract

This thesis investigates the feasibility of optically implementing a real-time, pattern recognition system using correlation techniques in a position, scale, and rotation invariant (PSRI) feature space. Input target templates were optically Fourier transformed using an improved high-resolution, high-pass filter positioned in the focal plane of the transforming lens. A logarithmic-polar coordinate transform of the magnitude-squared of the Fourier transform (/FT/2) was performed with an improved method of focusing the magnified /FT/2 onto a computer generated hologram (CGH), which was shown to scale linearly along the horizontal axis and logarithmically along the vertical axis. Optical, matched-filter correlations on the magnitude-squared Fourier transform logarithmic-polar (FLRT) feature space were performed using thermoplastic, phase-relief holography and Vander Lugt filtering. Correlation results prove that scale and rotation changes of the input can be predicted accurately based on linear shifts of the correlation peak. Also, the FLRT feature space is shown to provide excellent discrimination for multiple-input scenes. The need for a cyclic correlation is verified, and digital simulations prove the validity of the

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1988
Accession Number
ADA202600

Entities

People

  • J. T. Walrond
  • Timothy G. Childress

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Air Platforms
  • Energy and Power Technologies
  • Weapons Technologies

DTIC Thesaurus Topics

  • Air Force
  • Cameras
  • Computer Programs
  • Computers
  • Correlation Techniques
  • Detectors
  • Electrical Engineering
  • Focal Planes
  • High Pass Filters
  • High Resolution
  • Holograms
  • Image Processing
  • Optical Images
  • Pattern Recognition
  • Photographs
  • Recognition
  • Three Dimensional

Fields of Study

  • Physics

Readers

  • Aerospace Engineering
  • Computer Vision.
  • Image Processing and Computer Vision.

Technology Areas

  • AI & ML
  • Space
  • Space - Space Objects