Automatic inspection of salt-and-pepper defects in OLED panels using image processing and control chart techniques
Author
Abstract
Suggested Citation
DOI: 10.1007/s10845-017-1304-8
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
References listed on IDEAS
- Te-Hsiu Sun & Fang-Cheng Tien & Fang-Chih Tien & Ren-Jieh Kuo, 2016. "Automated thermal fuse inspection using machine vision and artificial neural networks," Journal of Intelligent Manufacturing, Springer, vol. 27(3), pages 639-651, June.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Aslı Çelik & Ayhan Küçükmanisa & Aydın Sümer & Aysun Taşyapı Çelebi & Oğuzhan Urhan, 2022. "A real-time defective pixel detection system for LCDs using deep learning based object detectors," Journal of Intelligent Manufacturing, Springer, vol. 33(4), pages 985-994, April.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Xinyu Suo & Jian Liu & Licheng Dong & Chen Shengfeng & Lu Enhui & Chen Ning, 2022. "A machine vision-based defect detection system for nuclear-fuel rod groove," Journal of Intelligent Manufacturing, Springer, vol. 33(6), pages 1649-1663, August.
- Chi Ma & Hongquan Gui & Jialan Liu, 2023. "Self learning-empowered thermal error control method of precision machine tools based on digital twin," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 695-717, February.
- Ohyung Kwon & Hyung Giun Kim & Min Ji Ham & Wonrae Kim & Gun-Hee Kim & Jae-Hyung Cho & Nam Il Kim & Kangil Kim, 2020. "A deep neural network for classification of melt-pool images in metal additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 31(2), pages 375-386, February.
- Mohamed Ben Gharsallah & Ezzedine Ben Braiek, 2021. "Computer aided manufacturing method for surface silicon steel inspection based on an efficient anisotropic diffusion algorithm," Journal of Intelligent Manufacturing, Springer, vol. 32(4), pages 1025-1041, April.
- Keyur D. Joshi & Vedang Chauhan & Brian Surgenor, 2020. "A flexible machine vision system for small part inspection based on a hybrid SVM/ANN approach," Journal of Intelligent Manufacturing, Springer, vol. 31(1), pages 103-125, January.
- Saksham Jain & Gautam Seth & Arpit Paruthi & Umang Soni & Girish Kumar, 2022. "Synthetic data augmentation for surface defect detection and classification using deep learning," Journal of Intelligent Manufacturing, Springer, vol. 33(4), pages 1007-1020, April.
More about this item
Keywords
Automated visual inspection; Flat panel display; Salt-and-pepper defect; Image processing technique; Statistical control chart;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:joinma:v:30:y:2019:i:3:d:10.1007_s10845-017-1304-8. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.