Advertisement

Abstract

The paper deals with a machine vision for a recognition and identification of manufactured parts. The inspection line has been constructed. Stepper motors, direct current motors and electromagnetic coils have been used as actuators. Printed circuit boards have been designed and made for a power supplying and signal level conversion. A feedback is acquired by two–position switches. For the practical realization, various construction components have been used, for example buffers for objects prepared for the inspection and inspected objects, manipulation arms and manipulation platform. The main control unit is a compact programmable logical controller, which controls hardware parts during the inspection cycle. The image capturing has been done with a common web camera. Algorithms for the image processing has been programmed in MATLAB Simulink. Data between the control system and the image processing system are exchanged via a mutual communication protocol.

Keywords

Image processing Machine vision MATLAB Programmable logical devices Electric motor 

Notes

Acknowledgements

The paper was supported by the projects: Center for Intelligent Drives and Advanced Machine Control (CIDAM) project, reg. no. TE02000103 funded by the Technology Agency of the Czech Republic, project reg. no. SP2016/83 funded by the Student Grant Competition of VSB-Technical University of Ostrava.

References

  1. 1.
    Hu, F., He, X., Niu, T.: Study on the key image processing technology in the inspection of packing quality for small-pack cigarettes. In: Second Workshop on Digital Media and Its Application in Museum & Heritages, Chongqing, pp. 67–71 (2007). doi: 10.1109/DMAMH.2007.62
  2. 2.
    Islam, M.J., Ahmadi, M., Sid-Ahmed, M.A.: Image processing techniques for quality inspection of gelatin capsules in pharmaceutical applications. In: 10th International Conference on Control, Automation, Robotics and Vision, ICARCV 2008, Hanoi, pp. 862–867 (2008). doi: 10.1109/ICARCV.2008.4795630
  3. 3.
    Arroyo, E., Lima, J., Leitão, P.: Adaptive image pre-processing for quality control in production lines. In: IEEE International Conference on Industrial Technology (ICIT), Cape Town, pp. 1044–1050 (2013). doi: 10.1109/ICIT.2013.6505816
  4. 4.
    Mahale, B., Korde, S.: Rice quality analysis using image processing techniques. In: International Conference for Convergence of Technology (I2CT), Pune, pp. 1–5 (2014). doi: 10.1109/I2CT.2014.7092300
  5. 5.
    Kuzu, A., Kuzu, A.T., Rahimzadeh, K., Bogasyan, S., Gokasan, M., Bakkal, M.: Autonomous hole quality determination using image processing techniques. In: IEEE 23rd International Symposium on Industrial Electronics (ISIE), Istanbul, pp. 966–971 (2014). doi: 10.1109/ISIE.2014.6864743
  6. 6.
    Sahoo, S.K., Pine, S., Mohapatra, S.K., Choudhury, B.B.: An effective quality inspection system using image processing techniques. In: International Conference on Communications and Signal Processing (ICCSP), Melmaruvathur, pp. 1426–1430 (2015). doi: 10.1109/ICCSP.2015.7322748
  7. 7.
    Fischer, R.B.: Dictionary of Computer Vision and Image Processing. Wiley, England (2005). ISBN 978-0-470-01526-1CrossRefGoogle Scholar
  8. 8.
    Havle, O.: Automa. Machine Vision, 1st part (Czech). http://automa.cz/index.php?id_document=36550
  9. 9.
    Rusnak, J.: Design of a vision system with KUKA robot. M.S. thesis, Institute of Products Mach System and Robotics, Faculty of Mechanical Engineering, Brno University of Technology, Brno, Czech Republic (2011)Google Scholar
  10. 10.
    Graves, C.: Machine vision reaches top gear. IEE Rev. 44(6), 265–267 (1998). doi: 10.1049/ir:19980609 CrossRefGoogle Scholar
  11. 11.
    Gonzalez, R.C., Woods, R.E., Eddins, S.L.: The Digital Image Processing Using MATLAB, 827 p. Gatesmark Publishing, USA (2009). ISBN 978–0982085400Google Scholar
  12. 12.
    Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. IEEE Trans. Pattern Anal. Mach. Intell. 24(4), 509–522 (2002). doi: 10.1109/34.993558 CrossRefGoogle Scholar
  13. 13.
    Unser, M.: Splines: a perfect fit for signal and image processing. IEEE Signal Process. Mag. 16(6), 22–38 (1999). doi: 10.1109/79.799930 CrossRefGoogle Scholar
  14. 14.
    Banham, M.R., Katsaggelos, A.K.: Digital image restoration. IEEE Signal Process. Mag. 14(2), 24–41 (1997). doi: 10.1109/79.581363 CrossRefGoogle Scholar
  15. 15.
    Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. PAMI–8(6), 679–698 (1986). doi: 10.1109/TPAMI.1986.4767851 CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Electronics, Faculty of Electrical Engineering and Computer ScienceVSB-Technical University of OstravaOstravaCzech Republic

Personalised recommendations