Three Dimensional Object Recognition Using an Unsupervised Neural Network: Understanding the Distinguishing Features

Abstract

A novel method for feature extraction has been applied to a problem of three-dimensional object recognition (Intrator and Gold, 1991). The method is related to recent statistical theory (Huber, 1985; Friedman, 1987) and is derived from a biologically motivated computational theory (Bienenstock et al., 1982). Results of an initial study replicating recent psychophysical experiments (Buelthoff and Edelman, 1991) demonstrated the utility of the proposed method for feature extraction. The authors describe further experiments designed to analyze the nature of the extracted features and their relevance to the theory and psychophysics of object recognition.

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Document Details

Document Type
Technical Report
Publication Date
Dec 23, 1992
Accession Number
ADA260048

Entities

People

  • Heinrich H. Buelthoff
  • Josh I. Gold
  • Nathan Intrator
  • Shimon Edelman

Organizations

  • Brown University

Tags

Communities of Interest

  • Human Systems

DTIC Thesaurus Topics

  • Artificial Intelligence Software
  • Automata Theory
  • Computer Science
  • Computer Vision
  • Dimensionality Reduction
  • Factor Analysis
  • Feature Extraction
  • Information Science
  • Machine Learning
  • Military Research
  • Network Science
  • Neural Networks
  • Object Recognition
  • Pattern Recognition
  • Recognition
  • Security
  • Three Dimensional

Readers

  • Computer Vision.
  • Neural Network Machine Learning.

Technology Areas

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