• DocumentCode
    1791257
  • Title

    Enhance quality control management for sensitive industrial products using 2D/3D image processing algorithms

  • Author

    Al-Yoonus, Murthad ; Jawad, Mohammed Saeed ; Abdullah, M.F.L. ; Al-Shargie, Fares

  • Author_Institution
    Fac. of Electr. & Electron. Eng., Univ. Tun Hussein Onn Malaysia, Batu Pahat, Malaysia
  • fYear
    2014
  • fDate
    27-28 Aug. 2014
  • Firstpage
    126
  • Lastpage
    131
  • Abstract
    Corners detection and sensitive parts features extractions methods are gaining more interests in quality control for automated industrial sensitive products manufacturing. As 2D edge defections detections algorithms have their own limitation as they are not supportive and accurate, 3D can improve significantly the accuracy of detecting the product defects. This paper, proof the concept of the accuracy of using 3D edge defections detection in comparison with 2D. Percentages of edge defection detections have been shown to aid the decision making of accepting or rejecting the final products shaping before delivery stage. Results showed that in many cases, 3D edge defections detections aid the decision making better than 2D edge detections algorithms.
  • Keywords
    edge detection; fault diagnosis; feature extraction; manufactured products; production engineering computing; quality control; 2D image processing; 3D image processing; corner detection; edge detection; feature extraction; product defects; quality control management; sensitive industrial products; Classification algorithms; Feature extraction; Image edge detection; Image enhancement; Standards; Three-dimensional displays; 2D edge detections; 3D edge detections; PCA; mismatching classifications; quality control; sensitive edges;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Electrical Power, Electronics, Communications, Controls and Informatics Seminar (EECCIS), 2014
  • Conference_Location
    Malang
  • Print_ISBN
    978-1-4799-6946-3
  • Type

    conf

  • DOI
    10.1109/EECCIS.2014.7003732
  • Filename
    7003732