An Effective Defect Inspection Method for LCD Using Active Contour Model

Visual defects in liquid crystal display images often appear as low contrast and blurry contour without distinct intensity difference from their surrounding region. Besides, the background is usually intensity inhomogeneity. All these properties make the machine vision inspection extremely hard. This paper presents an effective machine vision inspection method using a local active contour model to detect defects with different brightness levels as well as diverse sizes and shapes. A modified local binary fitting model which is robust to initial contour is developed to extract defect boundary. Meanwhile, a simple preprocessing scheme is given to compensate the drawback of the two-phase active contour model for detecting objects with wide brightness levels. Experimental results show that the presented method can detect various types of defects effectively and achieves high performance in terms of inspection accuracy (both precision and recall are higher than 0.99).

[1]  Kil-Houm Park,et al.  Inspection of defect on LCD panel using polynomial approximation , 2004, 2004 IEEE Region 10 Conference TENCON 2004..

[2]  Du-Ming Tsai,et al.  Independent component analysis based filter design for defect detection in low-contrast textured images , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[3]  Chunming Li,et al.  Implicit Active Contours Driven by Local Binary Fitting Energy , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[4]  Kil-Houm Park,et al.  The Defect Detection Using Human Visual System and Wavelet Transform in TFT-LCD Image , 2007, 2007 Frontiers in the Convergence of Bioscience and Information Technologies.

[5]  De Xu,et al.  Measurement and Defect Detection of the Weld Bead Based on Online Vision Inspection , 2010, IEEE Transactions on Instrumentation and Measurement.

[6]  Shang-Liang Chen,et al.  TFT-LCD Mura Defect Detection Using Wavelet and Cosine Transforms , 2008 .

[7]  Chunming Li,et al.  Minimization of Region-Scalable Fitting Energy for Image Segmentation , 2008, IEEE Transactions on Image Processing.

[8]  Han Ding,et al.  A New Mura Defect Inspection Way for TFT-LCD Using Level Set Method , 2009, IEEE Signal Processing Letters.

[9]  Mark Goadrich,et al.  The relationship between Precision-Recall and ROC curves , 2006, ICML.

[10]  Tony F. Chan,et al.  A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model , 2002, International Journal of Computer Vision.

[11]  Yi Shen,et al.  Segmentation for MRA Image: An Improved Level Set Approach , 2006, 2006 IEEE Instrumentation and Measurement Technology Conference Proceedings.

[12]  Baba C. Vemuri,et al.  Shape Modeling with Front Propagation: A Level Set Approach , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Shang-Liang Chen,et al.  TFT-LCD Mura defects automatic inspection system using linear regression diagnostic model , 2008 .

[14]  Jae Yeong Lee,et al.  Automatic Detection of Region-Mura Defect in TFT-LCD , 2004, IEICE Trans. Inf. Syst..

[15]  Qingyong Li,et al.  A Real-Time Visual Inspection System for Discrete Surface Defects of Rail Heads , 2012, IEEE Transactions on Instrumentation and Measurement.

[16]  H.-C. Liu,et al.  Liquid crystal display surface uniformity defect inspection using analysis of variance and exponentially weighted moving average techniques , 2005 .

[17]  Tony F. Chan,et al.  Active contours without edges , 2001, IEEE Trans. Image Process..

[18]  Guillermo Sapiro,et al.  Geodesic Active Contours , 1995, International Journal of Computer Vision.

[19]  Jian Zhang,et al.  A fuzzy neural network approach for quantitative evaluation of mura in TFT-LCD , 2005, 2005 International Conference on Neural Networks and Brain.

[20]  Du-Ming Tsai,et al.  An independent component analysis-based filter design for defect detection in low-contrast surface images , 2006, Pattern Recognit..

[21]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[22]  Rémi Ronfard,et al.  Region-based strategies for active contour models , 1994, International Journal of Computer Vision.

[23]  Kil-Houm Park,et al.  Morphological Blob-Mura Defect Detection Method for TFT-LCD Panel Inspection , 2004, KES.