Model-Based Vision System by Object-Oriented Programming

Abstract

This paper presents an approach to using object-oriented programming for the generation of a object recognition program that recognizes a complex 3-D object within a jumbled pile. We generate a recognition program from an interpretation tree that classifies an object into an appropriate attitude group, which has a similar appearance. Each node of an interpretation tree represents a feature matching. We convert each feature extracting or matching operation into an individual processing entity, called an object. Two kinds of objects have been prepared: data objects and event objects. A data object is used for representing geometric objects (such as edges and regions) and extracting features from geometric objects. An event object is used for feature matching and attitude determination. A library of prototypical objects is prepared and an executable program is constructed by properly selecting and instantiating modules from it. The object-oriented programming paradigm provides modularity and extensibility. This method has been applied to the generation of a recognition program for a toy wagon. The generated program has been tested with real scenes and has recognized the wagon in a pile. Keywords: Robotics; Libraries.

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Document Details

Document Type
Technical Report
Publication Date
Feb 01, 1988
Accession Number
ADA195819

Entities

People

  • Huey Chang
  • Katsushi Ikeuchi
  • Takeo Kanade

Organizations

  • Carnegie Mellon University

Tags

Communities of Interest

  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Abstracts
  • Classification
  • Computer Programming
  • Conversion
  • Demographic Cohorts
  • Feature Extraction
  • Four Dimensional
  • Models
  • Object Oriented Programming
  • Orientation (Direction)
  • Prototypes
  • Recognition
  • Reliability
  • Three Dimensional
  • Two Dimensional
  • Verification

Fields of Study

  • Computer science

Readers

  • Computational Linguistics
  • Database Systems and Applications
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • Autonomy