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Automated quality control of printed flasks and bottles

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Abstract

The paper faces the quality control problem for printed flasks, bottles and cans, used as containers for drugs and beverages. The control is mainly aimed at identifying ink spots and faded prints produced by a serigraphic process, but the approach is generically applicable to any kind of printing and printed cylindrical surface. Differently from the existing systems, based on the acquisition of good printed samples, the automatic control is based on the original digital image feeded to the printing system. Therefore, the control takes place directly between the ideal model and the result of a complex printing process including a number of distortion and noise sources. Problems related to image acquisition, reconstruction and alignment are investigated; a novel technique for image-model verification, based on adaptive local deformation, is also proposed and tested over a significant set of samples. A complete prototype system designed for such quality control is finally described and its operating capability on the field is discussed.

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References

  1. Azariadis P.N., Sapidis N.S.: Planar development of free-form surfaces: quality evaluation and visual inspection. Computing 72(1-2), 13–27 (2004). doi:10.1007/s00607-003-0043-1

    Article  MATH  MathSciNet  Google Scholar 

  2. Badekas E., Papamarkos N.: Optimal combination of document binarization techniques using a self-organizing map neural network. Eng. Appl. Artif. Intell. 20(1), 11–24 (2007). doi:10.1016/j.engappai.2006.04.003

    Article  Google Scholar 

  3. Baschera, P., Grandjean, E.: Effects of repetitive tasks with different degrees of difficulty on critical fusion frequency (CFF) and subjective state. Ergonomics 22(4) (1979)

  4. Chandu, K., Saber, E., Wu, W.: A mutual information based automatic registration and analysis algorithm for defect identification in printed documents. In: ICIP (3), pp. 449–452 (2007)

  5. Gonzalez R.C., Woods R.E.: Digital Image Processing. Addison-Wesley, Reading (1992)

    Google Scholar 

  6. Grattoni P., Spertino M.: A mosaicing approach for the acquisition and representation of 3d painted surfaces for conservation and restoration purposes. Mach. Vis. Appl. 15(1), 1–10 (2003). doi:10.1007/s00138-003-0128-z

    Article  Google Scholar 

  7. Huber-Mörk R., Ramoser H., Penz H., Mayer K., Heiss-Czedik D., Vrabl A.: Region based matching for print process identification. Pattern Recogn. Lett. 28(15), 2037–2045 (2007). doi:10.1016/j.patrec.2007.06.008

    Article  Google Scholar 

  8. Katafuchi N., Sano M., Ohara S., Okudaira M.: A method for inspecting industrial parts surfaces based on an optics model. Mach. Vis. Appl. 12(4), 170–176 (2000). doi:10.1007/s001380050137

    Article  Google Scholar 

  9. Lee M.F.R., Silva C.W., Croft E.A., Wu Q.M.J.: Machine vision system for curved surface inspection. Mach. Vis. Appl. 12(4), 177–188 (2000). doi:10.1007/s001380000043

    Article  Google Scholar 

  10. Liang, J., DeMenthon, D., Doermann, D.: Camera-based document image mosaicing. International Conference. Pattern Recognit. 2, 476–479 (2006). doi:10.1109/ICPR.2006.352

  11. Murrell K.F.H.: Operator variability and its industrial consequences. Int. J. Prod. Res. 1(3), 39–55 (1961)

    Article  Google Scholar 

  12. Aleixos N.J., Blasco F.N.E.M.: Multispectral inspection of citrus in real-time using machine vision and digital signal processors. Comput. Electr. Agric. 23(2), 121–137 (2002)

    Article  Google Scholar 

  13. Niblack W.: An Introduction to Digital Image Processing. Prentice-Hall, Englewood Cliffs (1986)

    Google Scholar 

  14. Ning Q., Yudong Z.: Physiological computation of binocular disparity. Vis. Res. 37(13), 1811–1827 (1997)

    Article  Google Scholar 

  15. Nishiara H.: Prism: a practical real-time imaging stereo matcher. Opt. Eng. 23(5), 536–545 (1984)

    Google Scholar 

  16. Pratt W.K.: Digital Image Processing: PIKS Inside, 3rd edn. Wiley, New York (2001)

    Book  Google Scholar 

  17. Puech, W., Chassery, J., Bors, A.G., Pitas, I.: Mosaicing of paintings on curved surfaces. In: WACV ’96: Proceedings of the 3rd IEEE Workshop on Applications of Computer Vision (WACV ’96), p. 44. IEEE Computer Society, Washington, DC, USA (1996)

  18. Sato, T., Iketani, A., Ikeda, S., Kanbara, M., Nakajima, N., Yokoya, N.: D-12-12 video mosaicing for curved surface by 3-D reconstruction using feature points. In: Proceedings of the IEICE General Conference 2005(2), 162 (20050307). http://ci.nii.ac.jp/naid/110004746342/en/

  19. Sato, T., Iketani, A., Ikeda, S., Kanbara, M., Nakajima, N., Yokoya, N.: Video mosaicing for curved documents by structure from motion. In: SIGGRAPH ’06: ACM SIGGRAPH 2006 Sketches, p. 126. ACM, New York, NY, USA (2006). doi:10.1145/1179849.1180007

  20. Schalkoff R.: Digital Image Processing and Computer Vision. Wiley, New York (1989)

    Google Scholar 

  21. Shum H.Y., Szeliski R.: Systems and experiment paper: construction of panoramic image mosaics with global and local alignment. Int. J. Comput. Vis. 36(2), 101–130 (2000). doi:10.1023/A:1008195814169

    Article  Google Scholar 

  22. Smith S.M., Brady J.M.: Susan—a new approach to low level image processing. Int. J. Comput. Vis. 23(1), 45–78 (1997). doi:10.1023/A:1007963824710

    Article  Google Scholar 

  23. Szeliski R.: Image alignment and stitching: a tutorial. Found. Trends Comput. Graph. Vis. 2(1), 1–104 (2006). doi:10.1561/0600000009

    Article  Google Scholar 

  24. Trier, O.D., Taxt, T.: Evaluation of binarization methods for utility map images. In: Proceedings of International Conference on Image Processing II ICIP94, pp. 1046–1050 (1994)

  25. Vartiainen, J., Lyden, S., Sadovnikov, A., Kamarainen, J.K., Lensu, L., Paalanen, P., Kälviäinen, H.: Automating visual inspection of print quality. In: ICIAR (2), pp. 877–885 (2006)

  26. Zeise, E., Burningham, N.: Standardization of perceptually based image quality for printing systems. In: IS&T’s NIP18: International Conference on Digital Printing Technologies. The Society for Imaging Science and Technology (2002)

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Authors and Affiliations

  1. DEIR, University of Sassari, Via Torre Tonda, 34, 07100, Sassari, Italy

    Enrico Grosso & Andrea Lagorio

  2. DAP, University of Sassari, Piazza Duomo, 6, 07041, Alghero, Italy

    Massimo Tistarelli

Authors
  1. Enrico Grosso
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  2. Andrea Lagorio
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  3. Massimo Tistarelli
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Correspondence to Enrico Grosso.

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Grosso, E., Lagorio, A. & Tistarelli, M. Automated quality control of printed flasks and bottles. Machine Vision and Applications 22, 269–281 (2011). https://doi.org/10.1007/s00138-009-0228-5

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  • DOI: https://doi.org/10.1007/s00138-009-0228-5

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