DOI QR코드

DOI QR Code

동적계획법을 이용한 자동화 공정에서의 제품 ID 마크 자동분할 알고리듬 개발

Development of an Image Segmentation Algorithm using Dynamic Programming for Object ID Marks in Automation Process

  • 유동훈 (동명정보대학교 메카트로닉스공학과) ;
  • 안인모 (마산대학 전기콤퓨터공학) ;
  • 김민성 (동명정보대학교 정보통신공학) ;
  • 강동중 (동명정보대학교 메카트로닉스공학과)
  • 발행 : 2004.08.01

초록

This paper presents a method to segment object ID(identification) marks on poor quality images under uncontrolled lighting conditions of automated inspection process. The method is based on dynamic programming using multiple templates and normalized gray-level correlation (NGC) method. If the lighting condition is not good and hence, we can not control the image quality, target image to be inspected presents poor quality ID marks and it is not easy to identify and recognize the ID characters. Conventional several methods to segment the interesting ID mark regions fail on the bad quality images. In this paper, we propose a multiple template method, which uses combinational relation of multiple templates from model templates to match several characters of the inspection images. To increase the computation speed to segment the ID mark regions, we introduce the dynamic programming based algorithm. Experimental results using images from real factory automation(FA) environment are presented.

키워드

참고문헌

  1. R. Haralick and L. Shapiro, 'Survey: Image segmentation technique,' Computer Vision, Graphics, and Image Processing, vol. 29, pp. 100-132, 1985 https://doi.org/10.1016/S0734-189X(85)90153-7
  2. S. Horowits and T. Pavlidis, 'Picture segmentation by a tree traversal algorithm,' J. CM, vol. 23, pp. 368-388, 1976 https://doi.org/10.1145/321941.321956
  3. S. M. Lee, 'Low rate video coding using 3-D segmentation with two change detection masks,' ISO/IEC/JTC1/SC29/WG11 MPEG93/941, 1993
  4. C. K. Chow and T. Kaneko, 'Boundary detection of radiographic images by a thresholding method,' Frontiers of Pattern Recognition, Academic Press, New York, 1972
  5. A. Rosenfeld and A. C. Kak, 'Digital picture processing,' Academic Press, New York, 1976
  6. S. Manickam, S. D. Roth, T. Bushman, 'Intelligent and Optimal Normalized Correlation for High-Speed Pattern Matching,' Datacube Technical Paper, Datacube Incorpolation, 2000
  7. 강동중, 노태정, '고속 검사자동화를 위한 에지기 반점 상관 알고리즘의 개발,' 제어.자동화.시스템공학회 논문집, 9권 8호, pp. 640-646, 2003 https://doi.org/10.5302/J.ICROS.2003.9.8.640
  8. R.E. Neapolitan and K. Naimipour, 'Foundations of algorithms using C++ pseudocode,' Jones and Barlet Publishing, 1988
  9. A. Amini, T. E. Weymouth, R.C. Jain, 'Using dynamic programming for solving variational problems in vision,' IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 12, 1991 https://doi.org/10.1109/34.57681
  10. D. J. Kang, 'A fast and stable algorithm for medical images,' Pattern Reccognition Letters, vol. 20, pp. 507-512, 1999 https://doi.org/10.1016/S0167-8655(99)00019-7
  11. D. J. Kang, J. E. Ha, and I. S. Kweon, 'Fast Object Recognition using Dynamic Programming from Combination of Salient Line Groups,' Pattern Recognition, vol. 36, pp. 79-90, 2003 https://doi.org/10.1016/S0031-3203(02)00046-8
  12. Matrox Image Library, User Guide, Matrox Inc, Version 7, pp.241-260, 2002