STNLCD外观缺陷检测方法研究
摘要
超扭曲向列液晶显示器件(STN-LCD)是各类仪器仪表和电子设备的主要显示方式.由于制备过程较复杂,生产过程中难免会出现各类缺陷。由于人眼很难分辨直径只有零点几毫米的缺陷,而且长时间的工作极易引起视觉疲劳,因此需要一种STN-LCD缺陷的自动检测技术,以实现高效的自动化.
本文研究了图像配准区域标记二值化等的原理与技术,提出一种基于图像配准的STN-LCD缺陷检测方法,并开发了相应的软件系统。该方法首先对标准模板图像做不均匀光照消除、二值化以及区域信息提取;然后通过控制点检测和模板匹配实现待检测图像和模板图像之间的配准;并利用各图形区域的灰度平均值等统计信息,检测缺段、针孔等各类缺陷。为提高图像配准精度,进一步提出了有效控制点筛选方案以及混合插值方法。实验结果表明,该方法设计思路合理可行,可代替人眼实现对STN-LCD各类外观缺陷的快速、自动检测,满足实际应用需求。
Super-twisted nematic liquid crystal display (STN-LCD) is a main display type of various electronic devices. Due to the complicated manufacturing process, various types of defects may exist on LCD panel, which is usually inspected manually. However, it is quite difficult and exhausting for human eye to locate defects less than 1 mm, a system for automatic defect detecting is desired in practical application.
This thesis proposed a new method for the detection of LCD defects based on the techniques including image registration, region labeling, and image thresholding.In the proposed method, the standard template LCD image is first restored from the possible non-uniform illumination, and then the statistical information of each region is collected by image thresholding and region labeling. The template and test image are registered by control-point detection and template registration. The defects including stroke-loss and pinhole of the LCD can then be detected by using the statistical information of each display region. To improve the accuracy of image registration, special schemes for control-point selection and hybrid image interpolation are proposed. Experimental results show that the proposed method is robust and accurate in detecting all forms of STN-LCD defects. The proposed method is applicable for industrial applications, and offers advantages over the traditional manual inspection manner.
本文研究了图像配准区域标记二值化等的原理与技术,提出一种基于图像配准的STN-LCD缺陷检测方法,并开发了相应的软件系统。该方法首先对标准模板图像做不均匀光照消除、二值化以及区域信息提取;然后通过控制点检测和模板匹配实现待检测图像和模板图像之间的配准;并利用各图形区域的灰度平均值等统计信息,检测缺段、针孔等各类缺陷。为提高图像配准精度,进一步提出了有效控制点筛选方案以及混合插值方法。实验结果表明,该方法设计思路合理可行,可代替人眼实现对STN-LCD各类外观缺陷的快速、自动检测,满足实际应用需求。
Super-twisted nematic liquid crystal display (STN-LCD) is a main display type of various electronic devices. Due to the complicated manufacturing process, various types of defects may exist on LCD panel, which is usually inspected manually. However, it is quite difficult and exhausting for human eye to locate defects less than 1 mm, a system for automatic defect detecting is desired in practical application.
This thesis proposed a new method for the detection of LCD defects based on the techniques including image registration, region labeling, and image thresholding.In the proposed method, the standard template LCD image is first restored from the possible non-uniform illumination, and then the statistical information of each region is collected by image thresholding and region labeling. The template and test image are registered by control-point detection and template registration. The defects including stroke-loss and pinhole of the LCD can then be detected by using the statistical information of each display region. To improve the accuracy of image registration, special schemes for control-point selection and hybrid image interpolation are proposed. Experimental results show that the proposed method is robust and accurate in detecting all forms of STN-LCD defects. The proposed method is applicable for industrial applications, and offers advantages over the traditional manual inspection manner.
引文
[1]应根裕,胡文波,邱勇等平板显示技术第一版[M]北京:人民邮电出版社103-104 135-165 132-135 149-150
[2]田民波 电子显示第一版[M]北京:清华大学出版社29-30
[3]张昱,张健,基于多项式曲面拟合的TFT-LCD斑痕缺陷自动检测技术[J],光电工程,2006,33(10):108-114;
[4]张昱,张健模糊专家系统在TFT-LCD缺陷检测中的应用,光电子·激光,2006,17(6):719-723
[5]Young.S.Foster.N LCD Error Detection System,the USA,United States Patent,Patent Number:4870357,Sep.26 1989
[6]苏彩红,詹宁宙,谢海鸿,基于LabVIEW的墙地砖表面缺陷自动检测与等级分类无损检测2007.29(4):189-192
[7]刘蕴辉,刘铁,王权良,罗四维基于图像处理的铁轨表面缺陷检测算法,计算机工程,2007.33(11):236-238
[8]杨顺辽,苏明,钟立,李泽军基于图像处理的印制电路板缺陷自动检测,计算机测量与控制,2007.15(5):591-592
[9]吕植勇,严新平磨粒图象背景光照不均匀分布图象预处理,润滑与密封2005(5):34-37
[10]欧阳庆,不均匀光照下车牌图像二值化研究,武汉大学学报(工学版),2006.39(4)143-146
[11]王宇,龙兴武,基于小波的不均匀光照的校正,光学技术,2005.31(5):776-778
[12]Tappen.M.F.Freeman.W.T.Adelson E.H.Recovering intrinsic images from a single image,IEEE Transactions on Pattern Analysis and Machine Intelligence,2005,27(9):1459-1472
[13]Weiss.Y.Deriving intrinsic images from image sequences,Proceedings of the Eighth IEEE International Conference on Computer Vision,Vancouver,Canada,2001,2:68-75
[14]田自君,刘艺 基于LOG算子边缘检测的图像二值化处理中国测试技术2007 33(6)109-110
[15]庄军,李弼程,陈刚 一种有效的文木图像二值化方法[J].微计算机信息,2005,21(8):56-57
[16]吴冰,秦志远自动确定图像二值化最佳阈值的新方法[J].测绘学院学报,2001,18(4):283-286
[17]Otsu.N.A threshold selection method from gray-level histogram[J].IEEE Transactions on System,Man and Cybernetics,1979,9:62-66
[18]Bernsen,Dynamic Thresholding of Gray level Image,ICPR'86:Proc.Int.Conf.on Pattern Recognition,Berlin,Germany,1986,pp:1251-1255
[19]高红波,王卫星 一种二值图像连通区域标记的新算法计算机应用200727(11)2776-2785
[20]Rosenfeld.A.Pfaltz.J.L.Sequential operations in digital picture processing[J].Journal of the ACM,1966,13(4):471-494.
[21]杨淑莹.基于VC++图像处理[M].北京:清华大学出版社,2005.
[22]Haralick.R.M.Shapiro.L.G Computer and robot version[M].Reading,MA:Addison-Wesley,1992.
[23]张树生.一种基于线的标号传播二值图像连通快速检测方法[J].计算机研究与发展,1994,31(10):.51-54.
[24]张桂林.基于跑长码的连通区域标记算法[J].华中理工大学学报,1994,22(5):11-14.
[25]徐利华,陈早生.二值图像中的游程编码区域标记[J].光电工程,2004,31(6):63-65.
[26]陈柏生.一种二值图像连通区域标记的新方法[J].计算机工程与应用,2006,42(25):50-51.
[27]章德伟,蒲晓蓉,章毅.基于Max—tree的连通区域标记新算法[J].计算机应用研究,2006,23(8):168-170.
[28]Samet.H.Connected component labeling using quadtrees[J].Journal of the ACM,1981,28(3):487-501
[29]Bhatyacharya.P.Connected component labeling for binary images on reconfigurable mesh architectures[J].Journal of Systems Architecture,1996,42(4):309-313.
[30]Smith.S,Brady.M.A new approach to low level image processing.International Journal of Computer Vision 1997 23(1):45-78
[31]Brown.M.Szeliski.R.Winder.S.Multi-image matching using multi-scale oriented patches.Computer Vision and Pattern Recognition,2005 1:510-517
[32]张小洪 李博 杨丹 一种新的Harris多尺度角点检测电子与信息学报2007 29(7)1735-1738
[33]李俊平,周振环,提取图像边界角点的新方法,微电子学与计算机,2006,23(11):188-190
[34]Harris.C.Stephens.M.J.A combined corner and edge detector[C].Proceedings of the Fourth Alvey Vision Conference,Manchester:University of Manchester,1988:147-151.
[35]Smith.S.M.Brady.J.M.SUSAN—A New Approach to Low Level Image Processing[R].UK:Oxford University,1995.
[36]陈志方,张艳宁,杨将林,孙瑾秋一种改进的SUSAN算法微电子学与计算机2007 24(11)142-144
[37]章毓晋 图像工程(中册)第二版北京:清华大学出版社354-355 370
[38]Hill.L.Vlachos.T.On the Estimation of Global Motion Using Phase Correlation for Broadcast Applications,IEEE ICIPA 1 999,pp721-725,Manchester.
[39]Castro.E.D.Morandi.C.Registration of Translated and Rotated Images Using Finite Fourier Transforms,IEEEPAMI,vol.9,No.5,1987.
[40]Xie.H.Hicks.N.Keller.G.R.Huang.H Kreinovick.V.Automatic Image Registration Based on A FFT Algorithm and ⅡDL/ENV1,ICORG-2000ICRS and GIS/GPS Proceedings.Vol.1,PP.397-402,Dec.1-4,2000
[41]Onishi.H.Suzuki.H.Detection of Rotation and Parallel Translation Using Hough and Fourier Transforms,IEEE Inter.Conf.Image Processing,1996,pp827-830.
[42]李中科 吴乐南 基于霍夫变换和相位相关的图像配准方法信号处理2004 20(2)166-169
[43]Zitova.B.Flusser.J.Image registration methods:a survey[J].Image and Vision Computing,2003,21:977-1000
[44]Reddy B.S.Chatterji.B.N.An FFT-based technique for translation,rotation,and scale-invariant image registration[J].IEEE Transaction on image processing,1996,5(8):1266-1271
[45]章毓晋 图像工程(上册)第二版北京:清华大学出版社 73-75
[2]田民波 电子显示第一版[M]北京:清华大学出版社29-30
[3]张昱,张健,基于多项式曲面拟合的TFT-LCD斑痕缺陷自动检测技术[J],光电工程,2006,33(10):108-114;
[4]张昱,张健模糊专家系统在TFT-LCD缺陷检测中的应用,光电子·激光,2006,17(6):719-723
[5]Young.S.Foster.N LCD Error Detection System,the USA,United States Patent,Patent Number:4870357,Sep.26 1989
[6]苏彩红,詹宁宙,谢海鸿,基于LabVIEW的墙地砖表面缺陷自动检测与等级分类无损检测2007.29(4):189-192
[7]刘蕴辉,刘铁,王权良,罗四维基于图像处理的铁轨表面缺陷检测算法,计算机工程,2007.33(11):236-238
[8]杨顺辽,苏明,钟立,李泽军基于图像处理的印制电路板缺陷自动检测,计算机测量与控制,2007.15(5):591-592
[9]吕植勇,严新平磨粒图象背景光照不均匀分布图象预处理,润滑与密封2005(5):34-37
[10]欧阳庆,不均匀光照下车牌图像二值化研究,武汉大学学报(工学版),2006.39(4)143-146
[11]王宇,龙兴武,基于小波的不均匀光照的校正,光学技术,2005.31(5):776-778
[12]Tappen.M.F.Freeman.W.T.Adelson E.H.Recovering intrinsic images from a single image,IEEE Transactions on Pattern Analysis and Machine Intelligence,2005,27(9):1459-1472
[13]Weiss.Y.Deriving intrinsic images from image sequences,Proceedings of the Eighth IEEE International Conference on Computer Vision,Vancouver,Canada,2001,2:68-75
[14]田自君,刘艺 基于LOG算子边缘检测的图像二值化处理中国测试技术2007 33(6)109-110
[15]庄军,李弼程,陈刚 一种有效的文木图像二值化方法[J].微计算机信息,2005,21(8):56-57
[16]吴冰,秦志远自动确定图像二值化最佳阈值的新方法[J].测绘学院学报,2001,18(4):283-286
[17]Otsu.N.A threshold selection method from gray-level histogram[J].IEEE Transactions on System,Man and Cybernetics,1979,9:62-66
[18]Bernsen,Dynamic Thresholding of Gray level Image,ICPR'86:Proc.Int.Conf.on Pattern Recognition,Berlin,Germany,1986,pp:1251-1255
[19]高红波,王卫星 一种二值图像连通区域标记的新算法计算机应用200727(11)2776-2785
[20]Rosenfeld.A.Pfaltz.J.L.Sequential operations in digital picture processing[J].Journal of the ACM,1966,13(4):471-494.
[21]杨淑莹.基于VC++图像处理[M].北京:清华大学出版社,2005.
[22]Haralick.R.M.Shapiro.L.G Computer and robot version[M].Reading,MA:Addison-Wesley,1992.
[23]张树生.一种基于线的标号传播二值图像连通快速检测方法[J].计算机研究与发展,1994,31(10):.51-54.
[24]张桂林.基于跑长码的连通区域标记算法[J].华中理工大学学报,1994,22(5):11-14.
[25]徐利华,陈早生.二值图像中的游程编码区域标记[J].光电工程,2004,31(6):63-65.
[26]陈柏生.一种二值图像连通区域标记的新方法[J].计算机工程与应用,2006,42(25):50-51.
[27]章德伟,蒲晓蓉,章毅.基于Max—tree的连通区域标记新算法[J].计算机应用研究,2006,23(8):168-170.
[28]Samet.H.Connected component labeling using quadtrees[J].Journal of the ACM,1981,28(3):487-501
[29]Bhatyacharya.P.Connected component labeling for binary images on reconfigurable mesh architectures[J].Journal of Systems Architecture,1996,42(4):309-313.
[30]Smith.S,Brady.M.A new approach to low level image processing.International Journal of Computer Vision 1997 23(1):45-78
[31]Brown.M.Szeliski.R.Winder.S.Multi-image matching using multi-scale oriented patches.Computer Vision and Pattern Recognition,2005 1:510-517
[32]张小洪 李博 杨丹 一种新的Harris多尺度角点检测电子与信息学报2007 29(7)1735-1738
[33]李俊平,周振环,提取图像边界角点的新方法,微电子学与计算机,2006,23(11):188-190
[34]Harris.C.Stephens.M.J.A combined corner and edge detector[C].Proceedings of the Fourth Alvey Vision Conference,Manchester:University of Manchester,1988:147-151.
[35]Smith.S.M.Brady.J.M.SUSAN—A New Approach to Low Level Image Processing[R].UK:Oxford University,1995.
[36]陈志方,张艳宁,杨将林,孙瑾秋一种改进的SUSAN算法微电子学与计算机2007 24(11)142-144
[37]章毓晋 图像工程(中册)第二版北京:清华大学出版社354-355 370
[38]Hill.L.Vlachos.T.On the Estimation of Global Motion Using Phase Correlation for Broadcast Applications,IEEE ICIPA 1 999,pp721-725,Manchester.
[39]Castro.E.D.Morandi.C.Registration of Translated and Rotated Images Using Finite Fourier Transforms,IEEEPAMI,vol.9,No.5,1987.
[40]Xie.H.Hicks.N.Keller.G.R.Huang.H Kreinovick.V.Automatic Image Registration Based on A FFT Algorithm and ⅡDL/ENV1,ICORG-2000ICRS and GIS/GPS Proceedings.Vol.1,PP.397-402,Dec.1-4,2000
[41]Onishi.H.Suzuki.H.Detection of Rotation and Parallel Translation Using Hough and Fourier Transforms,IEEE Inter.Conf.Image Processing,1996,pp827-830.
[42]李中科 吴乐南 基于霍夫变换和相位相关的图像配准方法信号处理2004 20(2)166-169
[43]Zitova.B.Flusser.J.Image registration methods:a survey[J].Image and Vision Computing,2003,21:977-1000
[44]Reddy B.S.Chatterji.B.N.An FFT-based technique for translation,rotation,and scale-invariant image registration[J].IEEE Transaction on image processing,1996,5(8):1266-1271
[45]章毓晋 图像工程(上册)第二版北京:清华大学出版社 73-75