Correlation Filter Synthesis Using Neural Networks.

Abstract

Excellent results were obtained using neural networks to synthesize filters for optical correlators, including filters for both cluttered backgrounds and target rotation angles not used in training. The most significant results employed new stretch and hammer neural networks which constitute an important and enduring advance because they train with guaranteed upper bounds on computational effort and generalize with guaranteed lower bounds on smoothness and stability. These results indicate good prospects for training neural networks to synthesize filters for a wide range of target distortions, and this approach has clear advantages compared to searching stored filters. Neural networks, Optical pattern recognition, Optimizing algorithms, Target recognition.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1993
Accession Number
ADA278124

Entities

People

  • David L. Flannery
  • Steven C. Gustafson

Organizations

  • University of Dayton

Tags

Communities of Interest

  • Air Platforms
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Computational Science
  • Computers
  • Correlators
  • Detectors
  • Digital Images
  • Image Processing
  • Information Processing
  • Modulators
  • Network Science
  • Neural Networks
  • Optical Correlators
  • Optics
  • Optomechanics
  • Signal Processing
  • Target Recognition
  • Two Dimensional

Fields of Study

  • Computer science

Readers

  • Approximation Theory.
  • Neural Network Machine Learning.

Technology Areas

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