Opto-Electronic Engineering, Volume. 47, Issue 11, 190725(2020)
Mura detection and positioning in picture based on BP neural network
[2] [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] [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] [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] [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] [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] [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] [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.
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Li Yineng, Zeng Qinghua, Zhang Yueyuan, Jiang Yong, Cui Yuchen. Mura detection and positioning in picture based on BP neural network[J]. Opto-Electronic Engineering, 2020, 47(11): 190725
Category: Article
Received: Dec. 11, 2019
Accepted: --
Published Online: Jan. 12, 2021
The Author Email: Qinghua Zeng (zengqh@nuaa.edu.cn)