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Crosstalk Defect Detection Method Based on Salient Color Channel Frequency Domain Filtering.
Xie, Wenqiang; Chen, Huaixin; Wang, Zhixi; Liu, Xing; Liu, Biyuan; Shuai, Lingyu.
Afiliación
  • Xie W; Department of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China.
  • Chen H; Department of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China.
  • Wang Z; Department of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China.
  • Liu X; Novel Product R & D Department, Truly Opto-Electronics Co., Ltd., Shanwei 516600, China.
  • Liu B; Department of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China.
  • Shuai L; Department of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China.
Sensors (Basel) ; 22(14)2022 Jul 20.
Article en En | MEDLINE | ID: mdl-35891104
ABSTRACT
Display crosstalk defect detection is an important link in the display quality inspection process. We propose a crosstalk defect detection method based on salient color channel frequency domain filtering. Firstly, the salient color channel in RGBY is selected by the maximum relative entropy criterion, and the color quaternion matrix of the displayed image is formed with the Lab color space. Secondly, the image color quaternion matrix is converted into the logarithmic spectrum in the frequency domain through the hyper-complex Fourier transform. Finally, Gaussian threshold band-pass filtering and hyper-complex inverse Fourier transform are used to separate the low-contrast defects and background of the display image. The experimental results show that the accuracy of the proposed algorithm reaches 96% for a variety of crosstalk defect detection. Compared with the current advanced defect detection algorithms, the effectiveness of the proposed method for low-contrast crosstalk defect detection is confirmed.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Diagnostic_studies Idioma: En Revista: Sensors (Basel) Año: 2022 Tipo del documento: Article País de afiliación: China

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Diagnostic_studies Idioma: En Revista: Sensors (Basel) Año: 2022 Tipo del documento: Article País de afiliación: China