A Self-Organizing Multiple-View Representation of Three-Dimensional Objects

Abstract

We explore representation of 3D objects in which several distinct 2D views are stored for each object. We demonstrate the ability of a two-layer network of threshold summation units to support such representations. Using unsupervised Hebbian relaxation, we trained the network to recognize ten objects from different viewpoints. The training process led to the emergence of compact representations of the specific input views. When tested on novel views of the same objects, the network exhibited a substantial generalization capability. In simulated psychophysical experiments, the network's behaviour was qualitatively similar to that of human subjects.

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 1989
Accession Number
ADA216711

Entities

People

  • Daphna Weinshall
  • Shimon Edelman

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Algorithms
  • Analysis Of Variance
  • Artificial Intelligence
  • Brain
  • Coefficients
  • Cognitive Science
  • Computer Vision
  • Information Processing
  • Information Science
  • Learning
  • Military Research
  • Motor Skills
  • Object Recognition
  • Regression Analysis
  • Standards
  • Three Dimensional
  • Unsupervised Machine Learning

Fields of Study

  • Computer science

Readers

  • Computer Vision.
  • Neural Network Machine Learning.
  • Vision Science/Vision Psychology/Cognitive Neuroscience.