Digital Instrumentation Calibration Using Computer Vision

  • Fernando Martín-Rodríguez
  • Esteban Vázquez-Fernández
  • Ángel Dacal-Nieto
  • Arno Formella
  • Víctor Álvarez-Valado
  • Higinio González-Jorge
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6112)

Abstract

This paper describes a computer vision system designed to automatically read the displays of digital instrumentation. The system is used in calibration sessions where many measurements have to be made and where we are interested in getting the whole numerical series downloaded on a host computer. Before our system was running, a human operator had to inspect the instruments at the right times (required by the calibration procedure) and to write down all the results. Note that we are speaking of very simple and sometimes old instruments that usually do not provide a digital interface or a removable memory (and if they do, we do not have a standard interface accepted by all the manufacturers). Results show the benefits of this system, obtaining a success rate higher than 99% in display recognition

Keywords

Computer vision text segmentation character recognition digital instrumentation 

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References

  1. 1.
    Ohya, J., Shio, A., Akamatsu, S.: Recognizing Characters in Scene Images. IEEE Transactions of Pattern Analysis and Machine Intelligence 16(2), 214–220 (1994)CrossRefGoogle Scholar
  2. 2.
    Cowell, J.R.: Syntactic Pattern Recognizer for Vehicle Identification Numbers. Image & Vision Computing (1995)Google Scholar
  3. 3.
    Fernández-Hermida, X., et al.: Automatic and Real Time Recognition of V.L.P.’s (Vehicle License Plates). In: Del Bimbo, A. (ed.) ICIAP 1997. LNCS, vol. 1311, pp. 552–559. Springer, Heidelberg (1997)CrossRefGoogle Scholar
  4. 4.
    Martín-Rodríguez, F., et al.: Localización de Caracteres en Imágenes de Instrumentación Digital. In: Proceedings of URSI-2009 (National Meeting of the International Scientific Radio Union). Santander, Spain (2009)Google Scholar
  5. 5.
    Otsu, N.: A Threshold Selection Method for Gray Level Histograms. IEEE Transactions on System, Man and Cybernetics (1979)Google Scholar
  6. 6.
    González, R.C., Woods, R.E.: Digital Image Processing, 3rd edn. Prentice Hall, Englewood Cliffs (2008)Google Scholar
  7. 7.
    Martín-Rodríguez, F.: Analysis Tools for Gray Level Histograms. In: Proceedings of SPPRA-2003, Signal Processing Pattern Recognition and Applications. Rhodes, Greece (2002), http://www.iasted.org
  8. 8.
    Proceedings of the IEEE (Special Isue on O.C.R.’s) 80(7) (1992)Google Scholar
  9. 9.
    Jain, A.K.: Fundamentals of Digital Image Processing. Prentice Hall, Englewood Cliffs (1989)MATHGoogle Scholar
  10. 10.
    Blue, J.L., et al.: Evaluation of Pattern Classifiers for Fingerprint and OCR Applications. Pattern Recognition (Pergamon Press) 27(4), 485–501 (1994)CrossRefGoogle Scholar
  11. 11.
    Vázquez-Fernández, E., et al.: Human Visual Perception as a Complementary Method for Digit Recognition. In: Proceedings of VIIP-2009 (Visualization, Imaging and Image Processing). Palma de Mallorca, Spain (2009), http://www.iasted.org
  12. 12.
    Corrêa Alegria, F., Cruz Serra, A.: Automatic Calibration of Analog and Digital Measuring Instruments Using Computer Vision. IEEE Transactions on Intrumentation and Measurement 49(1), 94–99 (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Fernando Martín-Rodríguez
    • 1
  • Esteban Vázquez-Fernández
    • 1
    • 2
  • Ángel Dacal-Nieto
    • 2
  • Arno Formella
    • 3
  • Víctor Álvarez-Valado
    • 2
  • Higinio González-Jorge
    • 2
  1. 1.Communications and Signal Theory DepartmentUniversity of Vigo 
  2. 2.Laboratorio Oficial de Metroloxía de Galicia 
  3. 3.Computer Science DepartmentUniversity of Vigo, ETSETVigoSpain

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