• DocumentCode
    920503
  • Title

    Automatic fault recognition by image correlation neural network techniques

  • Author

    Szu, Harold H.

  • Author_Institution
    NSWC Dahlgren Div., Silver Springs, MD, USA
  • Volume
    40
  • Issue
    2
  • fYear
    1993
  • fDate
    4/1/1993 12:00:00 AM
  • Firstpage
    197
  • Lastpage
    208
  • Abstract
    The image correlation technique, a useful method for online inspection for quality production control, is discussed. A Cauchy machine determines the imperfection by the degree of orthogonality between the automated extracted feature from the send-through image and the class feature of early good samples. The performance measure used for such an automatic feature extraction is based on a certain minimax cost function useful for image classification. Such an inspection theory based on image sequences is simulated by incorporating space-filling Peano curves, fast simulated Cauchy annealing, and minimax classification performance measures. An artificial neural network (ANN) is discussed as a possible implementation
  • Keywords
    automatic optical inspection; correlation methods; factory automation; fault location; feature extraction; neural nets; quality control; simulated annealing; Cauchy machine; artificial neural network; automated extracted feature; automatic fault recognition; fast simulated Cauchy annealing; image classification; image correlation neural network techniques; minimax cost function; online inspection; quality production control; send-through image; space-filling Peano curves; Artificial neural networks; Cost function; Feature extraction; Image classification; Image recognition; Image sequences; Inspection; Minimax techniques; Neural networks; Production control;
  • fLanguage
    English
  • Journal_Title
    Industrial Electronics, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0278-0046
  • Type

    jour

  • DOI
    10.1109/41.222641
  • Filename
    222641