Optical Image Segmentation Using Wavelet Correlation.

Abstract

This research introduces an optical method of segmenting potential targets using wavelet analysis. Implementation of an optical Harr wavelet is fulfilled using a magneto-optic spatial light modulator (MOSLM). Two methods of controlling wavelet dilation are explored: (1) spatial filtering of a ternary modulated MOSLM; (2) a single aperture positioned in front of a binary modulated MOSLM. Segmentation is performed through Vander Lugt correlation of a binarized image with a binarized optical wavelet. Three different image binarization methods are investigated for use in the correlation scheme: (1) average pixel value over an entire scene; (2) localized 4 x 4 pixel average followed by an average pixel value over the remaining scene; (3) localized 3 x 3 pixel average ANDed with an energy pixel value over the entire scene. Frequency-plane masks necessary for the correlation process are generated using thermal holography. Results show image segmentation for six possible experimental methods comprised of wavelet dilation and binarization techniques. The most successful correlation design used a single aperture to control wavelet dilation and binarization based on a localized 4 x 4 pixel average followed by an average pixel value over the remaining scene.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1991
Accession Number
ADA243650

Entities

People

  • Steven D. Pinski

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Air Platforms
  • Energy and Power Technologies
  • Weapons Technologies

DTIC Thesaurus Topics

  • C Programming Language
  • Change Detection
  • Computer Programming
  • Computer Programs
  • Computer Vision
  • Detectors
  • Filtration
  • Frequency
  • Holograms
  • Image Processing
  • Image Segmentation
  • Optical Images
  • Optical Modulators
  • Pattern Recognition
  • Spatial Filtering
  • Target Recognition
  • Two Dimensional

Fields of Study

  • Physics

Readers

  • Image Processing and Computer Vision.