• DocumentCode
    1120550
  • Title

    INSPECTOR: A Computer Vision System that Learns to Inspect Parts

  • Author

    Perkins, W.A.

  • Author_Institution
    Computer Science Department, General Motors Research Laboratories, Warren, MI 49090; Lockheed Palo Alto Research Laboratory, Palo Alto, CA 94304.
  • Issue
    6
  • fYear
    1983
  • Firstpage
    584
  • Lastpage
    592
  • Abstract
    A computer vision inspection system has been developed that learns the difference between good and bad parts by being shown several identified good and bad parts. The model, formed during the training session, contains identifying points which are used for locating parts and inspection tests which apply only to pertinent regions of the part. Using the model, the system can distinguish between good parts and bad parts with an arbitrary number of defects. It can also learn to classify parts if it is shown the different parts during the training session. Examples of INSPECTOR inspecting industrial parts are shown.
  • Keywords
    Cameras; Computer vision; Gray-scale; Industrial training; Inspection; Laboratories; Pattern classification; Pattern recognition; TV; Testing; Automatic inspection; inspection; learning; part registration; pattern classification; visual learning;
  • fLanguage
    English
  • Journal_Title
    Pattern Analysis and Machine Intelligence, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0162-8828
  • Type

    jour

  • DOI
    10.1109/TPAMI.1983.4767447
  • Filename
    4767447