• DocumentCode
    1684878
  • Title

    Fast subpixel digital image correlation using artificial neural networks

  • Author

    Pitter, Mark C. ; See, Chung W. ; Somekh, Michael G.

  • Author_Institution
    Dept. of Electr. & Electron. Eng., Nottingham Univ., UK
  • Volume
    2
  • fYear
    2001
  • Firstpage
    901
  • Abstract
    Digital image correlation has been used to measure microscopic deformation in thermally stressed microelectronics devices. Displacement precisions of better than 0.03 pixels have been achieved by combining nonintegral pixel shifting of subimages and artificial neural networks (ANNs). The ANNs are trained to estimate the subpixel element of the object displacement from the digital correlation. Although similar accuracies can be obtained by curve-fitting to the correlation peaks and differentiating, the neural approach has the advantage that it allows fast subpixel. displacement analysis over a range of object textures without knowledge of the analytical form of the correlation peaks
  • Keywords
    displacement measurement; image processing; integrated circuit testing; learning (artificial intelligence); multilayer perceptrons; optical correlation; parameter estimation; thermal stresses; artificial neural networks; correlation peaks; microelectronics devices; microscopic deformation measurement; multilayer perceptrons; nonintegral pixel shifting; object textures; subpixel digital image correlation; thermal stress; Artificial neural networks; Curve fitting; Digital images; Electric variables measurement; Electron microscopy; Manufacturing; Microelectronics; Packaging; Printed circuits; Stress measurement;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Image Processing, 2001. Proceedings. 2001 International Conference on
  • Conference_Location
    Thessaloniki
  • Print_ISBN
    0-7803-6725-1
  • Type

    conf

  • DOI
    10.1109/ICIP.2001.958640
  • Filename
    958640