Quality Inspection of Dented Capsule using Curve Fitting-based Image Segmentation
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초록
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.
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.
참고문헌 (13)
- A.C. Karloff, "A flexible design for a cost effective, high throughput inspection system for pharmaceutical capsules", IEEE International Conference Industrial Technology (ICIT'2008) (2008)
- Marten Klukkert, "Multispectral UV imaging for fast and non-destructive quality control of chemical and physical tablet attributes", European Journal of Pharmaceutical Sciences, 90, pp.85~95 (2016)
- Anna Novikovaa, "Multispectral UV imaging for surface analysis of MUPS tablets with special focus on the pellet distribution", International Journal of Pharmaceutics, 515(1-2), pp.374~383 (2016)
- A. K. Bhandaria, "A novel color image multilevel thresholding based segmentation using nature inspired optimization algorithms", Expert Systems with Applications, 63, pp.112~133 (2016)
- Huaizhong Zhang, "Coupling edge and region-based information for boundary finding in biomedical imagery", Pattern Recognition, 45(2), pp.672~684 (2012)
- Salvador García-Muñoz, "Coating uniformity assessment for colored immediate release tablets using multivariate image analysis", International Journal of Pharmaceutics, 395(1-2), pp.104~113 (2010)
- J. M. Prats-Montalbána, "Multivariate image analysis : A review with applications", Chemometrics and Intelligent Laboratory Systems, 107(1), pp.1~23 (2011)
- K. McGinnity, "Cross-validated wavelet block thresholding for non-Gaussian errors", Computational Statistics & Data Analysis, 106, pp.127~137 (2017)
- Maciel Zortea, "A simple weighted thresholding method for the segmentation of pigmented skin lesions in macroscopic images", Pattern Recognition, 64, pp.92~104 (2017)
- Davis, Thomas G., "Total least-squares spiral curve fitting", Journal of surveying engineering, 125(4), pp.159~176 (1999)
- M. A. Turk, "Face Recognition Using Eigenfaces", IEEE CVPR, pp.586~591 (1991)
- M. S. Bartlett, "Face Recognition by Independent Component Analysis", IEEE Transactions on Neural Networks, 13, pp.1450~1464 (2002)
- B. Heisele, "Face Recognition with Support Vector Machines : Global versus Component-Based Approach", ICCV, 2, pp.688~694 (2001)