Computer-Aided Vision System for MURA-Type Defect Inspection in Liquid Crystal Displays

  • Hong-Dar Lin
  • Singa Wang Chiu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4319)


This research proposes a new automated visual inspection method to detect MURA-type defects (color non-uniformity regions) on Liquid Crystal Displays (LCD). Owing to their space saving, energy efficiency, and low radiation, LCDs have been widely applied in many high-tech industries. However, MURA-type defects such as screen flaw points and small color variations often exist in LCDs. This research first uses multivariate Hotelling T 2 statistic to integrate different coordinates of color models and constructs a T 2 energy diagram to represent the degree of color variations for selecting suspected defect regions. Then, an Ant Colony based approach that integrates computer vision techniques precisely identifies the flaw point defects in the T 2 energy diagram. The Back Propagation Neural Network model determines the regions of small color variation defects based on the T 2 energy values. Results of experiments on real LCD panel samples demonstrate the effects and practicality of the proposed system.


Computer vision system MURA-type defects Hotelling T2 statistics Ant colony algorithm Back propagation network 


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  1. 1.
    Kido, T.: In Process Functional Inspection Technique for TFT-LCD Arrays. Journal of the SID 1, 429–435 (1993)Google Scholar
  2. 2.
    Chen, P.O., Chen, S.H., Su, F.C.: An Effective Method for Evaluating the Image-Sticking Effect of TFT-LCDs by Interpretative Modeling of Optical Measurement. Liquid Crystals 27, 965–975 (2000)CrossRefGoogle Scholar
  3. 3.
    Pratt, W.K., Hawthorne, J.A.: Machine Vision Methods for Automatic Defect Detection in Liquid Crystal Displays. Advanced Imaging 13, 52–54 (1998)Google Scholar
  4. 4.
    Pratt, W.K., Sawkar, S.S., O’Reilly, K.: Automatic Blemish Detection in Liquid Crystal Flat Panel Displays. In: SPIE Symposium on Electronic Imaging: Science and Technology (1998)Google Scholar
  5. 5.
    Lee, J.Y., Yoo, S.I.: Automatic Detection of Region-Mura Defect in TFT-LCD. IEICE Transactions on Information and Systems E87-D(10), 2371–2378 (2004)Google Scholar
  6. 6.
    Taniguchi, K., Ueta, K., Tatsumi, S.: A Mura Detection Method. Pattern Recognition 39, 1044–1052 (2006)zbMATHCrossRefGoogle Scholar
  7. 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. International Journal of Production Research 43(1), 67–80 (2005)zbMATHCrossRefGoogle Scholar
  8. 8.
    Lu, C.J., Tsai, D.M.: Defect Inspection of Patterned Thin Film Transistor-Liquid Crystal Display Panels Using a Fast Sub-image-based Singular Value Decomposition. International Journal of Production Research 42, 4331–4351 (2004)zbMATHCrossRefGoogle Scholar
  9. 9.
    Lu, C.J., Tsai, D.M.: Automatic Defect Inspection for LCDs Using Singular Value Decomposition. International Journal of Advanced Manufacturing Technology 25, 53–61 (2005)CrossRefGoogle Scholar
  10. 10.
    Lowry, C.A., Montgomery, D.C.: A Review of Multivariate Control Charts. IIE Transactions 27, 800–810 (1995)CrossRefGoogle Scholar
  11. 11.
    Mason, R.L., Chou, Y.M., Young, J.C.: Applying Hotelling’s T 2 Statistic to Batch Process. Journal of Quality Technology 33, 466–479 (2001)Google Scholar
  12. 12.
    Montgomery, D.C.: Introduction to Statistical Quality Control, 5th edn., pp. 491–504. John Wiley & Sons, Hoboken (2005)zbMATHGoogle Scholar
  13. 13.
    Dorigo, M., Maniezzo, V., Colorni, A.: Ant System: Optimization by a Colony of Cooperating Agents. IEEE Transactions on Systems, Man, and Cybernetics-Part B 26, 29–41 (1996)CrossRefGoogle Scholar
  14. 14.
    Dorigo, M., Bonabeau, E., Theraulaz, G.: Ant Algorithms and Stigmergy. Future Generation Computer System 16, 851–871 (2000)CrossRefGoogle Scholar
  15. 15.
    Kang, B.S., Park, S.C.: Integrated Machine Learning Approaches for Complementing Statistical Process Control Procedures. Decision Support Systems 29, 59–72 (2000)CrossRefGoogle Scholar
  16. 16.
    Smith, A.E.: X-bar and R Control Chart Interpretation Using Neural Computing. International Journal of Production Research 32, 309–320 (1994)zbMATHCrossRefGoogle Scholar
  17. 17.
    Hush, D.R., Horne, B.G.: Progress in Supervised Neural Networks. IEEE Signal Processing Magazine, 8–39 (January 1993)Google Scholar
  18. 18.
    Otsu, N.: A Threshold Selection Method from Gray Level Histograms. IEEE Transactions on Systems, Man and Cybernetics 9, 62–66 (1979)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Hong-Dar Lin
    • 1
  • Singa Wang Chiu
    • 2
  1. 1.Department of Industrial Engineering and Management 
  2. 2.Department of Business AdministrationChaoyang University of TechnologyTaichung CountyTaiwan

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