Computing with Connections,

Abstract

There is a rapidly growing interest in problem-scale parallelism, both as a model of animal brains and as a paradigm for VLSI. Work at Rochester has concentrated on connectionist models and their application to vision. This paper lays out a framework for dealing with such problems. The framework is built around computational modules, the simplest of which are termed p-units. We develop their properties and show how they can be applied to a variety of problems. To show how the framework can be applied to computational problems in vision, two specific examples are developed in some detail. In the first, we describe how spatially distributed data can be associated with a complex concept. In the second, we discuss the shape from shading problem and show how a global parameter, such as light source position, interacts with the calculation of a spatially distributed parameter such as surface orientation. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Apr 01, 1981
Accession Number
ADA102221

Entities

People

  • D. H. Ballard
  • J. A. Feldman

Organizations

  • University of Rochester

Tags

Communities of Interest

  • Energy and Power Technologies
  • Engineered Resilient Systems
  • Sensors

DTIC Thesaurus Topics

  • Accuracy
  • Artificial Intelligence
  • Brain
  • Cognitive Science
  • Complex Systems
  • Computational Science
  • Computer Languages
  • Computer Science
  • Computer Vision
  • Computers
  • Differential Equations
  • Equations
  • Information Processing
  • Neural Networks
  • Neurons
  • Notation
  • Psychology

Fields of Study

  • Computer science

Readers

  • Computer Vision.
  • Neural Network Machine Learning.
  • Theoretical Analysis.