Browse > Article

A Film-Defect Inspection System Using Image Segmentation and Template Matching Techniques  

Yoon, Young-Geun (한국외국어대학교 산업정보시스템공학부)
Lee, Seok-Lyong (한국외국어대학교 산업정보시스템공학부)
Park, Ho-Hyun (중앙대학교 전기전자공학부)
Chung, Chin-Wan (한국과학기술원 전산학과)
Kim, Sang-Hee (국방과학연구소 기술1-2팀)
Publication Information
Journal of KIISE:Databases / v.34, no.2, 2007 , pp. 99-108 More about this Journal
Abstract
In this paper, we design and implement the Film Defect Inspection System (FDIS) that detects film defects and determines their types which can be used for producing polarized films of TFT-LCD. The proposed system is designed to detect film defects from polarized film images using image segmentation techniques and to determine defect types through the image analysis of detected defects. To determine defect types, we extract features such as shape and texture of defects, and compare those features with corresponding features of referential images stored in a template database. Experimental results using FDIS show that the proposed system detects all defects of test images effectively (Precision 1.0, Recall 1.0) and efficiently (within 0.64 second in average), and achieves the considerably high correctness in determining defect types (Precision 0.96 and Recall 0.95 in average). In addition, our system shows the high robustness for rotated transformation of images, achieving Precision 0.95 and Recall 0.89 in average.
Keywords
Film Defect Inspection System; Image Segmentation; Template Database;
Citations & Related Records
연도 인용수 순위
  • Reference
1 R. M. Haralick, K. Shanmugam and I. Dinstein, 'Textural Features for Image Classification,' IEEE Transactions on Systems, Man and Cybernetics. SMC, Vol. 3, No. 6(1973), pp. 610-620   DOI   ScienceOn
2 USC-SIPI Image Database, (http://sipi.usc.edu/services/database/)
3 박종성, 정규원, 강찬구, '비전 시스템을 이용한 LCD용 편광 필름의 결함 검사에 관한 연구', 산업과학기술연구 논문집, 17권(2003), pp. 47-54
4 D. Androutsos, et al, 'Image Retrieval Using Directional Detail Histograms,' Proc. of SPIE Storage and Retrieval for image and Video Databases VI, (1998), pp. 129-137
5 J. Fan and D. K. Yau, 'Automatic Image segmentation by Integrating Color-Edge Extraction and Seeded Region Growing,' IEEE Transactions on Image Processing, Vol. 10(2001), pp.1454-1466   DOI   ScienceOn
6 Y. L. Chang and X. Li, 'Adaptive Image Region Growing,' IEEE Transactions on Image Processing, Vol. 3(1994), pp. 868-872   DOI   ScienceOn
7 S. A. Hijjatoleslami and J. Kittler, 'Region Growing: A New Approach,' IEEE Transactions on Image Processing, Vol. 7(1998), pp. 1079-1084   DOI   ScienceOn
8 Chi-Jie Lu, Du-Ming Tsai, '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, Vol. 42, No. 20 (October, 2004), pp. 4331-4351   DOI   ScienceOn
9 N. S. Chang and N. S. Fu, 'Query-By Pictorial Example,' Proceedings of IEEE Trans. On Software Engineering, (1980)   DOI   ScienceOn
10 K. Nakashima, 'Hybrid inspection system for LCD color filter panels,' Proceedings of the 10th International Conference on Instrumentation and measurement Technology, (1994), pp. 689-692   DOI
11 S. M. Sokolov, A. S. Treskunov, 'Automatic vision system for final test of liquid crystal display,' Proceedings of the 1992 IEEE International Conference on Robotics and Automation, (1992), pp. 1578-1582   DOI
12 H. D. Cheng, et al., 'Color Image Segmentation: advances and prospects,' Pattern Recognition, Vol. 34(2001), pp. 2259-2281   DOI   ScienceOn
13 T. Pavlidis and Y. T. Liow, 'Integrating Region Growing and Edge Detection,' IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 12(1990), pp. 225-233   DOI   ScienceOn
14 C. Chu and J. K. Aggarwal, 'The Integration of Image Segmentation Maps Using Region and Edge Information, IEEE Transactions on Pattern Analysis and Machine Intelligence,' Vol. 15(1993), pp. 1241-1252   DOI   ScienceOn