Extracting and identifying form features: a Bayesian approach

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

This paper introduces a new uncertainty reasoning based method for identification and extraction of manufacturing features from solid model description of objects. A major difficulty faced by previously proposed methods for feature extraction has been the interaction between features. In interacting situations, the representation for various primitive features is non-unique making their recognition very difficult. We develop an approach based on generating, propagating, and combining geometric and topological evidences in a hierarchical belief network for identifying and extracting features. The methodology combines and propagates evidences to determine a set of correct virtual links to be augmented to the cavity graph representing a depression of the object so that the resulting supergraph can be partitioned to obtain the features of the object.

Original languageEnglish (US)
Title of host publicationProceedings - IEEE International Conference on Robotics and Automation
Editors Anon
PublisherPubl by IEEE
Pages1959-1964
Number of pages6
Editionpt 3
ISBN (Print)0818653329
Publication statusPublished - 1994
EventProceedings of the 1994 IEEE International Conference on Robotics and Automation. Part 3 (of 4) - San Diego, CA, USA
Duration: May 8 1994May 13 1994

Other

OtherProceedings of the 1994 IEEE International Conference on Robotics and Automation. Part 3 (of 4)
CitySan Diego, CA, USA
Period5/8/945/13/94

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ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering

Cite this

Marefat, M. M., & Ji, Q. (1994). Extracting and identifying form features: a Bayesian approach. In Anon (Ed.), Proceedings - IEEE International Conference on Robotics and Automation (pt 3 ed., pp. 1959-1964). Publ by IEEE.