Neural Networks for Model-Based Recognition

Abstract

This annual progress report describes first-year progress in theoretical and applied fronts for neutral-net object recognition via graph matching. On the theory front, a learning scheme is applied to our previously hand-designed graphs, and a Bayesian approach to weighted graph matching is described. On an applied front, our networks are applied to recognition of machined parts. Continuing progress on the application of continuation optimization methods to our networks is reported.

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Document Details

Document Type
Technical Report
Publication Date
Jun 12, 1991
Accession Number
ADA241900

Entities

People

  • Gene R. Gindi

Organizations

  • Yale University

Tags

Communities of Interest

  • Air Platforms
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Artificial Intelligence Software
  • Computer Programming
  • Computer Programs
  • Computer Vision
  • Computers
  • Databases
  • Equations Of Motion
  • Image Recognition
  • Information Processing
  • Neural Networks
  • Neurons
  • Object Recognition
  • Ontologies
  • Recognition
  • Three Dimensional

Fields of Study

  • Computer science

Readers

  • Neural Network Machine Learning.
  • Technical Research and Report Writing.

Technology Areas

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