Computer Science ›› 2018, Vol. 45 ›› Issue (6): 296-300.doi: 10.11896/j.issn.1002-137X.2018.06.052

• Graphics, Image & Pattern Recognition • Previous Articles     Next Articles

Machine Vision Based Inspection Method of Mura Defect for LCD

QIAN Ji-de1,5, CHEN Bin2,5, QIAN Ji-ye3, ZHAO Heng-jun4, CHEN Gang1,5   

  1. Chengdu Institute of Computer Application,Chinese Academy of Sciences,Chengdu 610041,China1;
    Guangzhou Institute of Electronic Technology,Chinese Academy of Sciences,Guangzhou 510070,China2;
    State Grid Chongqing Electric Power Co.Electric Power Research Institute,Chongqing 401123,China3;
    Chongqing University of Arts and Sciences,Chongqing 402160,China4;
    University of Chinese Academy Sciences,Beijing 100049,China5
  • Received:2017-04-28 Online:2018-06-15 Published:2018-07-24

Abstract: Analyzing the necessity of the defect detection and the disadvantage of the manual detection in the liquid crystal display(LCD),this paper studied a kind of online detection system for the Mura defect of LCD based on machine vision.There are some features of Mura such as the low contrast,the fuzzy edge,the irregular shapes,the uneven brightness and so on.The simulation computer vision system was built to imitate human detection.The single frame ima-ge background modeling and background subtraction method were proposed.The methods can effectively suppress the uneven brightness of the LCD,and enhance the features of Mura defect information.Then,based on the maximally stable extremal region(MSER),the Mura defect adaptive threshold segmentation method was proposed.The auto inspection machine vision system was set up by synthesizing the proposed methods.The experimental results show that the proposed detection algorithm can effectively suppress the uneven brightness of the LCD,and accurately segment the Mura defects with good robustness.The system has the advantages of less manual intervention,high accuracy and online automatic detection.

Key words: Background difference, Background modeling, Machine vision, MSER, Mura defect

CLC Number: 

  • TP274
[1]CHOI Y S,YUN J U,PARK S E.Flat panel display glass:Current status and future[J].Journal of Non-Crystalline Solids,2016,431:2-7.
[2]BI X,DING H.Machine Vision Inspection Method of Mura Defect for TFT-LCD[J].Chinese Journal of Mechanical Enginee-ring,2010,46(12):13-19.(in Chinese)
毕昕,丁汉.TFT-LCD Mura缺陷机器视觉检测方法[J].机械工程学报,2010,46(12):13-19.
[3]HE Z,SUN L.Surface defect detection method for glass substrate using improved Otsu segmentation[J].Applied Optics,2015,54(33):9823.
[4]TSAI D M,TSENG Y H,CHIU W Y.Surface defect detection in low-contrast images using basis image representation[C]//IAPR International Conference on Machine Vision Applications.2015:186-189.
[5]FAN S K S,CHUANG Y C.Automatic detection of Mura defect in TFT-LCD based on regression diagnostics[J].Pattern Recognition Letters,2010,31(15):2397-2404.
[6]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.2013:240-244.
[7]JIANG B C,WANG C C,LIU H C.Liquid crystal display surface uniformity defect inspection using analysis of variance and exponentially weighted moving average techniques[J].International Journal of Production Research,2005,43(1):67-80.
[8]FORSSEN P E,LOWE D G.Shape Descriptors for Maximally Stable Extremal Regions[C]//IEEE International Conference on Computer Vision.2007:1-8.
[9]YOSHINAGA S,SHIMADA A,NAGAHARA H,et al.Object detection based on spatiotemporal background models[J].Computer Vision and Image Understanding,2014,122(5):84-91.
[10]MOSHE Y,HELOR H,HELOR Y.Foreground detection using spatiotemporal projection kernels[C]//IEEE Conference on Computer Vision and Pattern Recognition.2012:3210-3217.
[11]Horprasert T,Harwood D,Davis L S.A statistical approach for real-time robust background subtraction and shadow detection[C]//IEEE International Conference on Computer Vision.1999:1-19.
[12]LIU Y J,ZHI M.A two-layer background modeling method based on codebook and running average[J].Computer Enginee-ring and Science,2016,38(6):1220-1224.(in Chinese)
刘妍江,智敏.基于码本和运行期均值法的双层背景建模方法[J].计算机工程与科学,2016,38(6):1220-1224.
[13]STAUFFER C,GRIMSON W E L.Adaptive Background Mixture Models for Real-Time Tracking[C]//IEEE International Conference on Computer Vision and Pattern Recognition.1999:246-252.
[14]WANG S F,YAN J H,WANG Z G.Improved moving object detection algorithm based on local united feature[J].Chinese Journal of Scientific Instrument,2015,36(10):2241-2248.(in Chinese)
王顺飞,闫钧华,王志刚.改进的基于局部联合特征的运动目标检测方法[J].仪器仪表学报,2015,36(10):2241-2248.
[15]MATAS J,CHUM O,URBAN M,et al.Robust wide-baseline stereo from maximally stable extremal regions[J].Image and Vision Computing,2004,22(10):761-767.
[16]DING W R,KANG C B,LI H G,et al.Building areas extraction basing on MSER in unmanned aerial vehicle image[J].Journal of Beijing University of Aeronautics and Astronautics,2015,41(3):383-390.(in Chinese)
丁文锐,康传波,李红光,等.基于MSER的无人机图像建筑区域提取[J].北京航空航天大学学报,2015,41(3):383-390.
[17]NISTÉR D,STEWÉNIUS H.Linear Time Maximally Stable Extremal Regions[C]//European Conference on Computer Vision.2008:183-196.
[18]REN S G,MA C,XU H L.Improved Skeleton Extraction Algorithm Based Active Contour Model Research[J].Computer Scien-ce,2013,40(7):289-292.(in Chinese)
任守纲,马超,徐焕良.基于改进主动轮廓模型的图像分割方法研究[J].计算机科学,2013,40(7):289-292.
[19]SHI Y G,TAN J S,LIU Z W.Renal Cortex Segmentation Using Graph Cuts and Level Set[J].Computer Science,2016,43(7):290-293.(in Chinese)
时永刚,谭继双,刘志文.基于图割和水平集的肾脏医学图像分割[J].计算机科学,2016,43(7):290-293.
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