The Implications of Boltzmann-Type Machines for SAR (Synthetic Aperture Radar) Data Processing: A Preliminary Survey,

Abstract

This document proposes that Markov random field models (MRFs) be used as a framework within which to construct models of synthetic aperture radar (SAR) images. Its author clarifies the relationship between this class of models and the Boltzmann machine (BM) of artificial intelligence. He then generalizes the BM training procedure and use it to train MRF models. Using this techniques he investigate the ability of a simple MRF texture model to learn a texture by maximizing a relative entropy objective function. It is found that the marriage of MRF models with the BM training procedure is fruitful. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 1985
Accession Number
ADA163749

Entities

People

  • S. P. Luttrell

Organizations

  • Royal Signals and Radar Establishment

Tags

Communities of Interest

  • Advanced Electronics
  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Boundaries
  • Data Compression
  • Data Processing
  • Equations
  • Image Processing
  • Information Processing
  • Information Theory
  • Machines
  • Markov Chains
  • Neural Networks
  • Probability
  • Probability Distributions
  • Simulations
  • Synthetic Aperture Radar
  • Training

Fields of Study

  • Computer science

Readers

  • Computer Vision.
  • Gender and Food Studies
  • Neural Network Machine Learning.

Technology Areas

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