Constraint Networks: Modeling and Inferring Object Locations by Constraints,

Abstract

Relationships between objects in the real world are constrained by many physical and functional considerations. This paper presents a formalism called Constraint Networks which allows such constraints to be represented and used to make inferences about object locations in images. Constraint Networks are used in a system which accepts information about geometric relationships between structures in images and then uses these constraints to guide search for these structures. The system has been used successfully to infer rib positions in a chest X-ray and to locate aeration tanks and new construction sites in aerial images. (Author)

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Aug 01, 1978
Accession Number
ADA071140

Entities

People

  • Daniel M. Russell

Organizations

  • University of Rochester

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Aeration
  • Aerial Photographs
  • Algorithms
  • Artificial Intelligence
  • Coding
  • Color Displays
  • Computations
  • Computer Science
  • Computer Vision
  • Computers
  • Construction
  • Data Sets
  • Grain Size
  • Images
  • Machine Learning
  • Oil Tanks
  • Test And Evaluation

Fields of Study

  • Computer science

Readers

  • Computational Linguistics
  • Medical Imaging.
  • Neural Network Machine Learning.

Technology Areas

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