Mura detection and positioning in picture based on BP neural network

Li Yineng; Zeng Qinghua; Zhang Yueyuan; Jiang Yong; Cui Yuchen

doi:10.12086/oee.2020.190725

[2] Yang Y B, Li N, Zhang Y. Automatic TFT-LCD Mura detection based on image reconstruction and processing[C]//IEEE Third International Conference on Consumer Electronics, Berlin, 2014: 240–244.

[3] Fan S K S, Chuang Y C. Automatic detection of Mura defect in TFT-LCD based on regression diagnostics[J]. Pattern Recogni-tion Letters, 2010, 31(15): 2397–2404.

[6] Ma ZQ, Gong J. An automatic detectionmethod of Mura defects for liquid crystal display[C]// 2019 Chinese Control Conference (CCC), Guangzhou, China, 2019: 7722–7727.

[7] Kong LF,Shen J,Hu ZL, et al. Detection of water-stains defects in TFT-LCD based on machine vision[C]//2018 11th International Congress on Image and Signal Processing, BioMedical Engi-neering and Informatics (CISP-BMEI), Beijing, China, 2018: 1–5.

[8] Wang X, Dong R, Li B. TFT-LCD Mura defect detection based on ICA and multi-channels fusion[C]// 2016 3rd International Con-ference on Information Science and Control Engineering (ICISCE), Beijing, China, 2016: 687–691.

[14] Cen YG,Zhao RZ,Cen LH, et al. Defect inspection for TFT-LCD images based on the low-rank matrix reconstruction[J]. Neurocomputing, 2015, 149: 1206–1215.

[15] Han X H, Xiong X Y, Duan F. A new method for image segmen-tation based on BP neural network and gravitational search al-gorithm enhanced by cat chaotic mapping[J]. Applied Intelli-gence, 2015, 43(4): 855–873.