Learning Shape Descriptions: Generating and Generalizing Models of Visual Objects.

Abstract

We present the results of an implemented system for learning structural prototypes from gray-scale images. We show how to divide an object into subparts and how to encode the properties of these subparts and how to encode the properties of these subparts and the relations between them. We discuss the importance of hierarchy and grouping in representing objects and show how a notion of visual similarity can be embedded in the description language. Finally we exhibit a learning algorithm that forms class models from the descriptions produced and used these models to recognize new members of the class. Keywords: Learning; Concept learning; Shape description; Machine vision; High-level vision; (SLS)Smoothed Local Symmetries.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1985
Accession Number
ADA162562

Entities

People

  • Jonathan H. Connell

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Air Platforms

DTIC Thesaurus Topics

  • Aircrafts
  • Algorithms
  • Artificial Intelligence
  • Aspect Ratio
  • Coding
  • Cognitive Science
  • Computer Vision
  • Computers
  • Gray Scale
  • Hierarchies
  • Language
  • Learning
  • New York
  • Pattern Recognition
  • Prototypes
  • Three Dimensional
  • Two Dimensional

Fields of Study

  • Computer science

Readers

  • Artificial Intelligence
  • Defense Financial Management and Audit.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Information Retrieval
  • AI & ML - Neural Networks