An effective defect inspection system for polarized film images using image segmentation and template matching techniques

Computers and Industrial Engineering

Volumn 55, Issue 3, 2008, Pages 567-583

  Yoon, Young Geun ^a   Lee, Seok Lyong ^a   Chung, Chin Wan ^b   Kim, Sang Hee ^c  

^a Hankuk University of Foreign Studies   (South Korea)

^b Korea Advanced Institute of Science and Technology (KAIST)   (South Korea)

^c Agency for Defence Development   (South Korea)

Author keywords

Defect inspection system; Image segmentation; Polarized film; Template matching

Indexed keywords

DEFECTS; DIGITAL IMAGE STORAGE; FEATURE EXTRACTION; IMAGE ANALYSIS; IMAGE ENHANCEMENT; IMAGE PROCESSING; IMAGE SEGMENTATION; INSPECTION EQUIPMENT; LIGHT SOURCES; LIQUID CRYSTAL DISPLAYS; LIQUID CRYSTALS; TEMPLATE MATCHING; THICK FILMS; THIN FILM DEVICES; THIN FILM TRANSISTORS;

DEFECT IMAGING; DEFECT INSPECTION SYSTEM; DEFECT INSPECTION SYSTEMS; FILM DEFECTS; FILM IMAGES; POLARIZED FILM; RESPONSE TIME; ROTATION INVARIANCES; ROTATIONAL TRANSFORMATION; SEGMENTATION TECHNIQUES; TEMPLATE DATABASE; TEST IMAGES; TFT-LCD; THIN FILM TRANSISTOR LIQUID CRYSTAL DISPLAYS;

IMAGE MATCHING;

EID: 53249129414     PISSN: 03608352     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cie.2008.01.015     Document Type: Article

References (21)

1. Chang N.S., and Fu N.S. Query-by pictorial example. IEEE Transactions On Software Engineering (1980)
2. Chang Y.L., and Li X. Adaptive image region growing. IEEE Transactions on Image Processing 3 (1994) 868-872
3. Cheng H.D., et al. Color image segmentation: Advances and prospects. Pattern Recognition 34 (2001) 2259-2281
4. Choi, Y. (2003). Scene text extraction in natural images using hierarchical feature combining and verification. In The 2nd KAIST-Tsinghwa Joint Workshop on Pattern Recognition, pp. 76-102.
5. Chu C., and Aggarwal J.K. The integration of image segmentation maps using region and edge information. IEEE Transactions on Pattern Analysis and Machine Intelligence 15 (1993) 1241-1252
6. Fan J., and Yau D.K. Automatic image segmentation by integrating color-edge extraction and seeded region growing. IEEE Transactions on Image Processing 10 (2001) 1454-1466
7. Fan J., Zeng G., Body M., and Hacid M.S. Seeded region growing: An extensive and comparative study. Pattern recognition letters 26 8 (2005) 1139-1156
8. Haralick R.M., Shanmugam K., and Dinstein I. Textural features for image classification. IEEE Transactions on Systems, Man and Cybernetics. SMC 3 6 (1973) 610-620
9. Hijjatoleslami S.A., and Kittler J. Region growing: A new approach. IEEE Transactions on Image Processing 7 (1998) 1079-1084
10. Jain A.K., and Yu B. Automatic text location in images and video frames. Pattern Recognition 31 12 (1988) 2055-2076
11. Jeong K.W., Park J.S., and Gang C.G. A study on the defect inspection of the LCD polarized film using the vision system. Journal of Industrial Science and Technology Institute 17 1 (2003) 47-53
12. Jianfeng L. Adaptive region growing algorithm in medical images segmentation. Journal of Computer Aided Design and Computer Graphics 17 10 (2005) 2168-2173
13. Kim D.Y., Chung S.M., and Park J.W. Automatic navigation path generation based on two-phase adaptive region-growing algorithm for virtual angioscopy. Medical Engineering and Physics 28 4 (2006) 339-347
14. Kim J.S., Kim S.H., and Choi H.W. Text region extraction of natural scene images using gray-level information and split/merge method. Journal of Korea Information Science: Software and Applications 32 6 (2005) 502-511
15. Lu C.J., and 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 20 (2004) 4331-4351
16. Nakashima, K. (1994). Hybrid inspection system for LCD color filter panels. In Proceedings of the 10th International Conference on Instrumentation and measurement Technology, pp. 689-692.
17. Passat N., Ronse C., Baruthio J., Armspach J., Maillot C., and Jahn C. Region-growing segmentation of brain vessels: An atlas-based automatic approach. Journal of Magnetic Resonance Imaging : JMRI 21 6 (2005) 715-725
18. Pavlidis T., and Liow Y.T. Integrating region growing and edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 12 (1990) 225-233
19. Shih F.Y., and Cheng S. Automatic seeded region growing for color image segmentation. Image and Vision Computing 23 10 (2005) 877-886
20. Sokolov, S. M., Treskunov, A. S. (1992). Automatic vision system for final test of liquid crystal display. In: Proceedings of the 1992 IEEE international conference on robotics and automation, pp. 1578-1582.
21. USC-SIPI Image Database, http://sipi.usc.edu/services/database/.