Journal of KIISE:Databases (한국정보과학회논문지:데이타베이스)

Korean Institute of Information Scientists and Engineers (한국정보과학회)
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A Film-Defect Inspection System Using Image Segmentation and Template Matching Techniques

영상 세그멘테이션 및 템플리트 매칭 기술을 응용한 필름 결함 검출 시스템

  • 윤영근 (한국외국어대학교 산업정보시스템공학부) ;
  • 이석룡 (한국외국어대학교 산업정보시스템공학부) ;
  • 박호현 (중앙대학교 전기전자공학부) ;
  • 정진완 (한국과학기술원 전산학과) ;
  • 김상희 (국방과학연구소 기술1-2팀)
  • Published : 2007.04.15
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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.

본 논문에서는 TFT-LCD에 사용되는 편광 필름(polarized film)의 제작 과정 중 최종 단계에서 수행되는 필름의 결함 검출 및 결함 유형을 판정하기 위한 필름 결함 검출 시스템(Film Defect Inspection System: FDIS)을 설계하고 이를 구현하였다. 제안한 시스템은 영상 세그멘테이션 기법을 이용하여 편광 필름 영상으로부터 결함을 검출하였고, 검출된 결함의 영상을 분석하여 결함 유형을 판정할 수 있도록 설계되었다. 결함 유형의 판정은 결함 영역의 형태적 특성 및 질감(texture) 등의 특징을 추출하여 템플리트(template) 데이타베이스에 저장된 기준(reference) 결함 영상과 비교함으로써 수행된다. FDIS를 이용한 실험 결과, 테스트 영상에서 모든 결함 영역을 빠른 시간 안에 (평균 0.64초), 정확히 검출하였으며(Precision 1.0, Recall 1.0), 결함 유형을 판정하는 실험에서도 평균 Precision 0.96, Recall 0.95로 정확도가 매우 높은 것을 관찰할 수 있었다. 또한 회전 변형을 적용한 경우의 결함 유형 검출 실험에서도 평균 Precision 0.95, Recall 0.89로 제안한 기법이 회전 변환에 대하여 견고함을 보여 주었다.

Keywords

References

  1. 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 https://doi.org/10.1109/IMTC.1994.352006
  2. 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 https://doi.org/10.1109/ROBOT.1992.220027
  3. 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 https://doi.org/10.1080/00207540410001716480
  4. N. S. Chang and N. S. Fu, 'Query-By Pictorial Example,' Proceedings of IEEE Trans. On Software Engineering, (1980) https://doi.org/10.1109/TSE.1980.230801
  5. 박종성, 정규원, 강찬구, '비전 시스템을 이용한 LCD용 편광 필름의 결함 검사에 관한 연구', 산업과학기술연구 논문집, 17권(2003), pp. 47-54
  6. 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
  7. 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 https://doi.org/10.1109/83.951532
  8. Y. L. Chang and X. Li, 'Adaptive Image Region Growing,' IEEE Transactions on Image Processing, Vol. 3(1994), pp. 868-872 https://doi.org/10.1109/83.336259
  9. S. A. Hijjatoleslami and J. Kittler, 'Region Growing: A New Approach,' IEEE Transactions on Image Processing, Vol. 7(1998), pp. 1079-1084 https://doi.org/10.1109/83.701170
  10. 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 https://doi.org/10.1109/34.49050
  11. 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 https://doi.org/10.1109/34.250843
  12. H. D. Cheng, et al., 'Color Image Segmentation: advances and prospects,' Pattern Recognition, Vol. 34(2001), pp. 2259-2281 https://doi.org/10.1016/S0031-3203(00)00149-7
  13. 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 https://doi.org/10.1109/TSMC.1973.4309314
  14. USC-SIPI Image Database, (http://sipi.usc.edu/services/database/)