Spatial Filters for Optical Correlation

Abstract

A set of images comprising an aximuthal training set of M-48, tank images was used as the basis of simulation studies to assess the performance of improved metric sort ternary phase-amplitude filters using gray scale inputs. Results compared favorably with those obtained with other filter formulations. Binary versions of these images were used as the basis for a study of image binarization effects on optical correlation performance. The study indicated excellent correlation performance with binarized inputs and references. A set of T-72 tank images, including an azimuthal training set and input scenes with clutter, was the basis for a simulation study comparing correlation performance of smart TPAFs with gray scale and binary input scenes. The correlation results with binary images were at least equal in quality to those using gray scale inputs. 128xl28 correlation filters were generated on a zero-filled 256x256- pixel array. These filters were correlated with l28xl28-pixel input scenes (imbedded in a zero-filled 256x256 array for digital simulations). Methods for implementing these filters in a correlator with 128xl28-pixel SLM's at both input and filter planes were investigated by both simulation and experimentation. The two methods provided acceptable results.... Laser radar, Near-infrared, Optical pattern recognition, Phase-only filters.

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Document Details

Document Type
Technical Report
Publication Date
May 01, 1993
Accession Number
ADA267199

Entities

People

  • David L. Flannery

Organizations

  • University of Dayton

Tags

Communities of Interest

  • Air Platforms
  • Energy and Power Technologies
  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Air Force
  • Algorithms
  • Amplitude
  • Command And Control
  • Correlators
  • Distortion
  • False Targets
  • Filters
  • Governments
  • Gray Scale
  • Images
  • Information Processing
  • Intensity
  • Noise
  • Pattern Recognition
  • Power Spectra
  • White Noise

Fields of Study

  • Physics

Readers

  • Computational Modeling and Simulation
  • Image Processing and Computer Vision.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Neural Networks
  • Directed Energy