A vision system with automatic learning capability for industrial parts inspection

J. Lin, Wen-Hsiang Tsai , Jeunn Shenn Lee, Chai Hsiung Chen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

A vision system for automated parts inspection is proposed. The system is equipped with learning capabilities such that it automatically selects from a set of sample parts a minimum, but effective inspection region within the camera's field of view for parts discrimination. A binary template is formed within the inspection region which is then used for parts inspection by template matching. The inspection speed is enhanced by keeping the inspection region small and by making the matching task uncomplicated. A simple learning algorithm based on statistical pattern recognition theory is employed, which only requires the system to be taught by a training set of good and defective parts without specific defect identification or location. The system is applicable to most 2-D industrial parts inspection.

Original languageEnglish
Title of host publicationProceedings - 1984 IEEE International Conference on Robotics and Automation, ICRA 1984
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages417-425
Number of pages9
ISBN (Print)081860526X
DOIs
StatePublished - 1 Jan 1984
Event1st IEEE International Conference on Robotics and Automation, ICRA 1984 - Atlanta, United States
Duration: 13 Mar 198415 Mar 1984

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
ISSN (Print)1050-4729

Conference

Conference1st IEEE International Conference on Robotics and Automation, ICRA 1984
CountryUnited States
CityAtlanta
Period13/03/8415/03/84

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