Scene Analysis Using a Semantic Base for Region Growing

Abstract

The problem of breaking an image into meaningful regions is considered. A probabilistic semantic basis is effectively integrated with the segmentation process, providing various decision criteria. Learning facilities are provided for interactively generating the Bayesian probabilistic basis. A programming system which is based on these ideas and its successful application to two problem domains are described.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Jun 01, 1973
Accession Number
AD0767695

Entities

People

  • Yoram Yakimovsky

Organizations

  • Stanford University

Tags

Communities of Interest

  • Air Platforms
  • Autonomy

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Change Detection
  • Computer Science
  • Detection
  • Detectors
  • Image Processing
  • Image Segmentation
  • Information Science
  • Literature
  • Literature Surveys
  • Machine Learning
  • Pattern Recognition
  • Probabilistic Models
  • Probability
  • Random Variables
  • Three Dimensional
  • Two Dimensional

Fields of Study

  • Computer science

Readers

  • Neural Network Machine Learning.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference