Measurement Method of the Width of the Strands of Cut Tobacco Based on Digital Image Processing

Liu, Jun; Chen, Zhihao; Zhao, Jijun; Wang, Shu; Ding, Li; Wu, Xiaosong; Zhang, Long; Feng, Qian

doi:10.2478/cttr-2019-0007

Abstract

The width of cut tobacco strands is an important indicator for physical parameters as well as for the smoking quality. In some countries, cut width helps to distinguish fine-cut tobacco and pipe tobacco and thus differentiates taxation rate. A new method for rapid measurement of the width of cut tobacco strands was developed based on digital image processing, because the method described in ISO 20193, though easy to implement in factories, proved time consuming and generated high testing costs. The essence of this method is to determine the statistic width of incisions. The straight-line segments represent the width of strands of cut tobacco, from which the determination of the width for randomly placed tobacco strands could be achieved. Five kinds of samples (‘ISO collaborative study samples 0.4 mm, 1.0 mm, 1.6 mm and 3.0 mm’ and ‘Guangdong baked 0.9 mm’) were used to study the comparability of the measurement results between the method presented in this work and the current ISO method. Results show that accuracy and repeatability are comparable. In addition, the testing efficiency of the method presented in this work appears to be higher than the current ISO method, and it is thus a promising alternative method for measuring the width of strands of cut tobacco.

References

1. Zhang, H.L.: Effect of Width of Cut Lamina on Tar of Cigarette Smoke; Tob. Sci. Technol. (1996) 5–7. DOI: 10.16135/j.issn1002-0861.1996.05.002
Search in Google Scholar Back to article
2. Yang, Y., H. Gao, H. Wang, Z. Liu, Y.Z. Wang, H.J. Lin, Y.X. Cui, and Y.K. Hua: Effects of Combination of Different Width Cut Tobacco on Cigarette Physical Indexes and its Comprehensive Stability; J. Yunnan Agric. Univ. 32 (2017) 488–497.
Search in Google Scholar Back to article
3. Yao, G.M., W.H. Wang, X.Z. Yin, S.H. Li, and Q. Li: Effect of the Size Proportion of Cut Tobacco on the Filling Power of Cut Tobacco and Cigarette Making Quality; J. Zhengzhou Inst. Light Ind. (Nat. Sci.) 18 (2003) 62–64.
Search in Google Scholar Back to article
4. International Organisation of Standardization (ISO): ISO 20193:2012 — Analysis of Tobacco and Tobacco Products — Determination of the Width of the Strands of Cut Tobacco; ISO, Geneva, Switzerland, 2012. Available at: https://www.iso.org/standard/52696.html (accessed July 2019)
Search in Google Scholar Back to article
5. Lu, Y., T.Q. Chen, J. Chen, J. Zhang, and A. Tisler: Machine Vision Systems Using Machine Learning for Industrial Product Inspection; Proceedings of SPIE – The International Society for Optical Engineering 4567 (2001) 161–170. DOI: 10.1117/12.45525310.1117/12.455253
Open DOI Search in Google Scholar Back to article
6. Lahanjar, F., R. Bernard, F. Pernuš, and S. Kovačič: Machine Vision System for Inspecting Electric Plates; Comput. Ind. 47 (2002) 113–122. DOI: 10.1016/S0166-3615(01)00134-810.1016/S0166-3615(01)00134-8
Open DOI Search in Google Scholar Back to article
7. Zhang, Z.: A Flexible New Technique for Camera Calibration; IEEE Trans. Pattern Anal. Mach. Intell. 22 (2000) 1330–1334. DOI: 10.1109/34.88871810.1109/34.888718
Open DOI Search in Google Scholar Back to article
8. Kannala, J. and S.S. Brandt: A Generic Camera Model and Calibration Method for Conventional, Wide-Angle, and Fish-Eye Lenses; IEEE Trans. Pattern Anal. Mach. Intell. 28 (2006) 1335–1340. DOI: 10.1109/TPAMI.2006.15310.1109/TPAMI.2006.153
Search in Google Scholar Back to article
9. Mayer, A. and H. Greenspan: An Adaptive Mean-Shift Framework for MRI Brain Segmentation; IEEE Trans. Med. Imaging, 28 (2009) 1238–1250. DOI: 10.1109/TMI.2009.201385010.1109/TMI.2009.2013850
Open DOI Search in Google Scholar Back to article
10. Duda, R.O. and P. E. Hart: Use of the Hough Transformation to Detect Lines and Curves in Pictures; Commun. ACM 15 (1972) 11–15. Available at: https://www.cse.unr.edu/~bebis/CS474/Handouts/HoughTransformPaper.pdf (accessed July 2019)10.1145/361237.361242
Search in Google Scholar Back to article
11. Xia, Y.W., Q. Feng, Y. Zhao, L. Xiang, Z. Zhu, Y. Liu, J. Liu, L. Zhang, J. Zhao, Q. Zhong, H. Yi, and R. Du: Method for Measuring Width of Tobacco Shred Based on Computer Vision; Tob. Sci. Technol. 9 (2014) 10–14. DOI: 10.3969/j.issn.1002-0861.2014.09.002
Search in Google Scholar Back to article
12. Goshtasby, A.: Correction of Image Deformation From Lens Distortion Using Bezier Patches; Comput. Vision Graph. 47 (1989) 385–394. DOI: 10.1016/0734-189X(89)90120-510.1016/0734-189X(89)90120-5
Open DOI Search in Google Scholar Back to article
13. Brown, D.C.: Close Range Camera Calibration; Photo-gramm. Eng. 37 (1971) 855–866.
Search in Google Scholar Back to article
14. Gharib, M. and S. Ghani: Free Vibration Analysis of Linear Particle Chain Impact Damper; J. Sound Vib. 332 (2013) 6254–6264. DOI: 10.1016/j.jsv.2013.07.01310.1016/j.jsv.2013.07.013
Open DOI Search in Google Scholar Back to article
15. Eldahshan, K., M. Youssef, E. Masameer, and M.A. Mustafa: Comparison of Segmentation Framework on Digital Microscope Images for Acute Lymphoblastic Leukemia Diagnosis Using RGB and HSV Color Spaces; Biotechnol. Bioeng. 2 (2015) 27–34. DOI: 10.5120/15590-442610.5120/15590-4426
Open DOI Search in Google Scholar Back to article
16. Joshi, M.D., A.H. Karode, and S.R. Suralkar: White Blood Cells Segmentation and Classification to Detect Acute Leukemia; Int. J. Emerg. Trends Technol. Comput. Sci. 2 (2013) 147–151. Available at: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.387.699&rep=rep1&type=pdf (accessed August 2019)
Search in Google Scholar Back to article
17. Cheng, Y.Z.: Mean Shift, Mode Seeking, and Clustering; IEEE Transactions on Pattern Analysis and Machine Intelligence 17 (1995) 790–799. DOI: 10.1109/34.40056810.1109/34.400568
Open DOI Search in Google Scholar Back to article
18. Zhang, Y.C., H. Guo, F. Chen, and H. Yang: Weighted Kernel Mapping Model With Spring Simulation Based Watershed Transformation for Level Set Image Segmentation; Neurocomputing 249 (2017) 1–18. DOI: 10.1016/j.neucom.2017.01.04410.1016/j.neucom.2017.01.044
Open DOI Search in Google Scholar Back to article
19. Strang, G.: Linear Algebra and its Applications; 3^rd edition, Harcourt, Brace, Jovanovic, San Diego, CA, USA, 1988. ISBN-13: 978-0155510050
Search in Google Scholar Back to article
20. Rosenfeld, A. and J.L. Pfaltz: Sequential Operations in Digital Picture Processing; JACM 13 (1966) 471–494. DOI: 10.1145/321356.32135710.1145/321356.321357
Open DOI Search in Google Scholar Back to article
21. Bhattacharya, P.: Connected Component Labeling for Binary Images on Reconfigurable Mesh Architectures; J. Syst. Architect. 42 (1996) 309–313. DOI: 10.1016/1383-7621(96)00027-610.1016/1383-7621(96)00027-6
Open DOI Search in Google Scholar Back to article
22. Samet, H.: Connected Component Labeling Using Quadtrees; JACM 28 (1981) 487–501. Available at: https://www.cs.umd.edu/users/hjs/pubs/SametJACM81.pdf (accessed July 2019)10.1145/322261.322267
Search in Google Scholar Back to article
23. He, X.G., J. Tian, L.F. Wu, and Y.Y: Zhang: Illumination Normalization with Morphological Quotient Image; J. Softw. 18 (2007) 2318–2325. DOI: 10.1360/jos18231810.1360/jos182318
Open DOI Search in Google Scholar Back to article
24. Li, X.F., D.W. Ma, and Y.J. Nian: The Research on Algorithm of Image’s Erosion and Dilation; Image Technol. 01 (2005) 37–39. DOI: 10.3969/j.issn.1001-0270.2005.01.00910.3969/j.issn.1001-0270.2005.01.009
Open DOI Search in Google Scholar Back to article
25. Cheng, J., Q. Liu, and H. Lu: Texture Classification Using Kernel Independent Component Analysis; Proceedings of the 17^th International Conference on Pattern Recognition 1 (2004) 23–26. DOI: 10.1109/ICPR.2004.133423110.1109/ICPR.2004.1334231
Open DOI Search in Google Scholar Back to article
26. Liu X.W. and L. Cheng: Independent Filters for Texture Classification; Proceedings of IEEE International Conference on Image Processing 3 (2002) 24–28. DOI: 10.1109/ICIP.2002.103891710.1109/ICIP.2002.1038917
Open DOI Search in Google Scholar Back to article
27. Liu, X. and L. Cheng: Independent Spectral Representations of Images for Recognition; J. Opt. Soc. Am. A 20 (2003) 1271–1282. DOI: 10.1364/JOSAA.20.00127110.1364/JOSAA.20.001271
Open DOI Search in Google Scholar Back to article

Measurement Method of the Width of the Strands of Cut Tobacco Based on Digital Image Processing

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