Aydin Sümer, A. Çelik, Ayhan Küçükmanísa, A. Çelebi, O. Urhan
{"title":"Pixel Defect Detection in LCD TV Images using Adaptive Thresholding","authors":"Aydin Sümer, A. Çelik, Ayhan Küçükmanísa, A. Çelebi, O. Urhan","doi":"10.1109/SIU.2019.8806412","DOIUrl":null,"url":null,"abstract":"Nowadays, there is a trend towards higher physical size and resolution in LCD TV production. However, there are still undesired situations such as pixel defects in spite of developing manufacturing technologies. In this study, an adaptive thresholding based pixel defect detection method is proposed. The system, which is evaluated by the F1-score criterion, shows that it can be an alternative to human controlled approaches with its high detection performance. When compared with a machine learning based method in the literature, the proposed method stands out with its working time and detection performance.","PeriodicalId":326275,"journal":{"name":"2019 27th Signal Processing and Communications Applications Conference (SIU)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 27th Signal Processing and Communications Applications Conference (SIU)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SIU.2019.8806412","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
Nowadays, there is a trend towards higher physical size and resolution in LCD TV production. However, there are still undesired situations such as pixel defects in spite of developing manufacturing technologies. In this study, an adaptive thresholding based pixel defect detection method is proposed. The system, which is evaluated by the F1-score criterion, shows that it can be an alternative to human controlled approaches with its high detection performance. When compared with a machine learning based method in the literature, the proposed method stands out with its working time and detection performance.