Artificial Neural System for Object Classification and Orientation Estimation

Abstract

The usefulness of self-organizing neural systems for the problems of object recognition and orientation estimation are discussed. Self-organizing neural systems, like unsupervised cluster algorithms from classical pattern recognition, are most useful when no predetermined labels are available to attach to input patterns which must be categorized by the system. However, this makes it necessary to assign a 'natural' category to the input stimuli from the environment. Only with some type of supervisory feedback will this natural category be associated with the proper label for the input pattern. Without this teaching input, the internal, self-organizing principles of the system must be used to assign categories. These assigned categories may or may not coincide with the unique labels of an external supervisory system.

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Document Details

Document Type
Technical Report
Publication Date
Apr 01, 1991
Accession Number
ADA234936

Entities

People

  • Frank Kuhl
  • Jeffrey D. Johnson
  • Timothy A. Grogan

Organizations

  • United States Army Armament Research, Development and Engineering Center

Tags

Communities of Interest

  • Air Platforms

DTIC Thesaurus Topics

  • Aircrafts
  • Artificial Intelligence Software
  • Computational Complexity
  • Computer Vision
  • Computers
  • Differential Equations
  • Information Processing
  • Information Science
  • Neural Networks
  • Object Recognition
  • Pattern Recognition
  • Recognition
  • Self Organizing Systems
  • Simulations
  • Supervised Machine Learning
  • Training
  • Two Dimensional

Readers

  • Neural Network Machine Learning.
  • Regression Analysis.
  • Robotics and Automation.

Technology Areas

  • AI & ML
  • AI & ML - Information Retrieval
  • AI & ML - Neural Networks