Journal of Digital Contents Society (디지털콘텐츠학회 논문지)

Digital Contents Society (한국디지털콘텐츠학회)
권호 표지
DOI QR코드

DOI QR Code

Performance evaluation of Edge-based Method for classification of Gelatin Capsules

젤라틴 캡슐의 분류를 위한 에지 기반 방법 성능 평가

  • Kwon, Ki-Hyeon (Department of Information & Communication Engineering, Kangwon National University) ;
  • Choi, In-Soo (Department of Hotel Culinary Arts, Tongwon University)
  • 권기현 (강원대학교 정보통신공학과) ;
  • 최인수 (동원대학교 호텔조리학과)
  • Received : 2016.12.21
  • Accepted : 2017.02.25
  • Published : 2017.02.28
Download PDF
⟨ Previous Next ⟩

Abstract

In order to solve problems in automatic quality inspection of tablet capsules, computation-efficient image processing technique, appropriate threshold setting, edge detection and segmentation methods are required. And since existing automatic system for quality inspection of tablet capsules is of very high cost, it needs to be reduced through the realization of low-price hardware system. This study suggests a technique that uses low-cost camera module to obtain image and inspects dents on tablet capsules and sorting them by applying TLS curve fitting technique and edge-based image segmentation. In order to assess the performance, the major classifications algorithm of PCA, ICA and SVM are used to evaluate training time, test time and accuracy for capsule image area and curve fitting edge data sets.

태블릿 캡슐의 품질 검사를 자동으로 해내기 위해서는 효율적인 이미지 처리기법, 적절한 임계치 설정, 에지 검출 그리고 세그멘테이션 방법 등이 필요하다. 그리고 기 존재하는 태블릿 캡슐의 품질 자동 검사 장비는 매우 고가이므로 품질 검사의 용이성을 높이기 위해서 저가의 하드웨어 시스템이 도입 되어야하다. 본 연구에서는 저가 카메라 모듈을 사용하여 이미지를 취득하고 전최소자승법 커브 피팅, 에지기반 이미지 세그멘테이션 방법을 사용하여 태블릿 캡슐의 함몰을 검사한다. 제안한 방법의 성능을 보이기 위해서 주요 분류 알고리즘인 PCA, ICA, SVM 방법을 사용하여 캡슐이미지 영역 데이터세트와 커브 피팅 에지 데이터세트에 대하여 훈련시간, 테스트시간 그리고 분류 정확도를 구하였다.

Keywords

References

  1. A.C. Karloff, N.E. Scott, and R. Muscedere, "A flexible design for a cost effective, high throughput inspection system for pharmaceutical capsules," in IEEE International Conference Industrial Technology (ICIT'2008), Chengdu, China, April 21-24, 2008.
  2. M. Klukkert, J. X. Wu, J. Rantanen, J. Carstensen, T. Rades, C. Leopold, "Multispectral UV imaging for fast and non-destructive quality control of chemical and physical tablet attributes," European Journal of Pharmaceutical Sciences, Vol. 90, pp. 85-95, 2016. https://doi.org/10.1016/j.ejps.2015.12.004
  3. A. Novikovaa, J. M. Carstensenb, T. Radesc, Prof. Dr. Claudia S. Leopolda, "Multispectral UV imaging for surface analysis of MUPS tablets with special focus on the pellet distribution," International Journal of Pharmaceutics, Vol. 515, Issues 1-2, pp.374-3832016, 2016. https://doi.org/10.1016/j.ijpharm.2016.09.087
  4. A.K. Bhandaria, A. Kumarb,, S. Chaudharyb,, G.K. Singhc, "A novel color image multilevel thresholding based segmentation using nature inspired optimization algorithms," Expert Systems with Applications, Vol. 63, pp. 112-133, 2016. https://doi.org/10.1016/j.eswa.2016.06.044
  5. H. Zhang, P. Morrow, S. McClean, K. Saetzler, "Coupling edge and region-based information for boundary finding in biomedical imagery." Pattern Recognition, Vol. 45, Issue 2, pp. 672-684, 2012. https://doi.org/10.1016/j.patcog.2011.07.014
  6. Y. Park, S. Park, Y. Seo, "An Extracting Text Area Using Adaptive Edge Enhanced MSER in Real World Image," Journal of Digital Contents Society, Vol. 17 No.4, pp.219-226, Aug. 2016. https://doi.org/10.9728/dcs.2016.17.4.219
  7. S. Garcia-Munoz, D. S. Gierer, "Coating uniformity assessment for colored immediate release tablets using multivariate image analysis," International Journal of Pharmaceutics, Vol. 395, Issues 1-2, pp. 104-113, 2010. https://doi.org/10.1016/j.ijpharm.2010.05.026
  8. J.M. Prats-Montalbana, A. de Juanb, A. Ferrera, "Multivariate image analysis: A review with applications," Chemometrics and Intelligent Laboratory Systems, Vol. 107, Issue 1, pp. 1-23, 2011. https://doi.org/10.1016/j.chemolab.2011.03.002
  9. K. McGinnity, R. Varbanov, E. Chicken, "Cross-validated wavelet block thresholding for non-Gaussian errors," Computational Statistics & Data Analysis, Vol. 106, pp. 127-137, 2017. https://doi.org/10.1016/j.csda.2016.09.010
  10. M. Zortea, E. Flores, J. Scharcanski, "A simple weighted thresholding method for the segmentation of pigmented skin lesions in macroscopic images," Pattern Recognition, Vol. 64, pp. 92-104, 2017. https://doi.org/10.1016/j.patcog.2016.10.031
  11. M. A. Turk and A. P. Pentland, "Face Recognition Using Eigenfaces," in IEEE CVPR, pp. 586-591, 1991.
  12. M. S. Bartlett, J. R. Movellan, and T. J. Sejnowski, "Face Recognition by Independent Component Analysis," in IEEE Transactions on Neural Networks, Vol. 13, pp. 1450-1464, 2002. https://doi.org/10.1109/TNN.2002.804287
  13. B. Heisele, P. Ho, and T. Poggio, "Face Recognition with Support Vector Machines: Global versus Component-Based Approach," in ICCV. Vol. 2 Vancouver, Canada, pp. 688.694, 2001.
  14. D. Thomas G. "Total least-squares spiral curve fitting." Journal of Surveying Engineering 125.4 pp. 159-176, 1999. https://doi.org/10.1061/(ASCE)0733-9453(1999)125:4(159)