• Title/Summary/Keyword: PCSR-G

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Self-Reference PCSR-G Method for Detecting Defect of Flat Panel Display (평판 디스플레이 결함 검출을 위한 자기 참조 PCSR-G 기법)

  • Kim, Jin-Hyung;Lee, Tae-Young;Ko, Yun-Ho
    • Journal of Korea Multimedia Society
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    • v.18 no.3
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    • pp.312-322
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    • 2015
  • In this paper a new defect detection method for flat panel display that does not require any separately prepared reference images and shows robustness against problems with regard to pixel tolerance and nonuniform illumination condition is proposed. In order to perform defect detection under any magnification value of camera, the proposed method automatically obtains the value of pattern interval through an image analysis. Using the information for pattern interval, an advanced PCSR-G method presented in this paper utilizes neighboring patterns as its reference images instead of utilizing any separately prepared reference images. Also this paper proposes a scheme to improve the performance of the conventional PCSR-G method by extracting and applying additional information for pixel tolerance and intensity distribution considering the value of pattern interval. Simulation results show that the performance of the proposed method utilizing pixel tolerance and intensity distribution is superior to that of the conventional method. Also, it is proved that the proposed method that is implemented using parallel technique based on GPGPU can be applied to real system.

A New Defect Inspection Method for TFT-LCD Panel using Pattern Comparison (패턴 비교를 통한 TFT-LCD 패널의 결함 검출 방법)

  • Lee, Kyong-Min;Jang, Moon-Soo;Park, Poo-Gyeon
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.57 no.2
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    • pp.307-313
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    • 2008
  • In this paper, we propose a novel defects inspection algorithm for TFT-LCD panels. We first compensate the distorted image caused by the camera distortion and the uneven illumination environment using the least squares method and the bezier surface. We find a starting point of each pattern for restricting each pattern. A clean image is compared to each pattern to find defects using modified PCSR-G algorithm. The simulation example shows that our algorithm not only inspects the defects well, but also is robust to the 1-pixel error.