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An application of rough set theory to defect detection of automotive glass

Author

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  • Lee, Seungkoo
  • Vachtsevanos, George

Abstract

A technique based on rough set theory is investigated for identifying defects on a backlight (a rear window of a vehicle with a defrost circuit). Since replacement of defective backlights result in a significant financial loss, automobile manufacturers are trying to remove defective backlights during the manufacturing process. Therefore, an automated inspection system based on infrared (IR) imaging techniques has been developed to detect backlight defects such as missing lines or hotspots, where the most challenging task is identifying hotspots from their artifacts.

Suggested Citation

  • Lee, Seungkoo & Vachtsevanos, George, 2002. "An application of rough set theory to defect detection of automotive glass," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 60(3), pages 225-231.
    • Handle: RePEc:eee:matcom:v:60:y:2002:i:3:p:225-231
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    Cited by:

    1. Corrado Zoppi & Sabrina Lai, 2006. "Geographic Information Systems and Decision Processes for Urban Planning: A Case Study of Rough Set Analysis on the Residential Areas of the City of Cagliari, Italy," ERSA conference papers ersa06p367, European Regional Science Association.
    2. (Bill) Tseng, Tzu-Liang & Huang, Chun-Che, 2007. "Rough set-based approach to feature selection in customer relationship management," Omega, Elsevier, vol. 35(4), pages 365-383, August.

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