DOI QR코드

DOI QR Code

Quality Inspection of Dented Capsule using Curve Fitting-based Image Segmentation

  • Kwon, Ki-Hyeon (Dept. of Electronics, Information & Communication Engineering, Kangwon National University) ;
  • Lee, Hyung-Bong (Dept. of Computer Science & Engineering, Gangneung-Wonju National University)
  • Received : 2016.09.30
  • Accepted : 2016.12.28
  • Published : 2016.12.31

Abstract

Automatic quality inspection by computer vision can be applied and give a solution to the pharmaceutical industry field. Pharmaceutical capsule can be easily affected by flaws like dents, cracks, holes, etc. In order to solve the quality inspection problem, it is required computationally efficient image processing technique like thresholding, boundary edge detection and segmentation and some automated systems are available but they are very expensive to use. In this paper, we have developed a dented capsule image processing technique using edge-based image segmentation, TLS(Total Least Squares) curve fitting technique and adopted low cost camera module for capsule image capturing. We have tested and evaluated the accuracy, training and testing time of the classification recognition algorithms like PCA(Principal Component Analysis), ICA(Independent Component Analysis) and SVM(Support Vector Machine) to show the performance. With the result, PCA, ICA has low accuracy, but SVM has good accuracy to use for classifying the dented capsule.

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. Marten Klukkert, Jian X. Wu, Jukka Rantanen, Jens M. Carstensen, Thomas Rades, Claudia S. 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. Anna Novikovaa, Jens M. Carstensenb, Thomas 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. Huaizhong Zhang, Philip Morrow, Sally McClean, Kurt 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. Salvador Garcia-Munoz, Daniel 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
  7. 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
  8. 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
  9. Maciel Zortea, Eliezer Flores, Jacob 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
  10. Davis, 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)
  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," 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