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An Energy Visualization by Camera Monitoring

  • Tetsuya Fujisawa
  • Tadahito Egawa
  • Kazuhiko Taniguchi
  • Syoji Kobashi
  • Yutaka Hata
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 268)

Abstract

This paper proposes an energy visualization system by a camera. For monitoring, a single camera captures gas meter image at fixed intervals. The system applies edge detection and the connected-component labeling to extract numeral regions in counters of a gas mater. Gas consumption is estimated based on shape characteristics of numerals. The system uses number of endpoints and holes in numerical character, and it calculates a direction histogram and sum of absolute difference (SAD). The system recognizes the numeral by fuzzy inference from the acquired shape characteristic. When the system failed to recognize gas consumption by some accidents, the consumption is interpolated from time-serious data. In the result, our method estimated 32 and 29 numerals in 33 pieces for front and slant measurement respectively. For a continual monitoring in a day, the system successfully estimated dynamic gas consumption change and visualized them.

Keywords

energy visualization image processing numeral recognition fuzzy inference gas consumption 

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References

  1. 1.
    Murakanmi, S., Bogaki, K., Tanaka, T., Hayama, H., Yoshino, H., Akabayashi, S., Inoue, T., Iio, A., Hokoi, S., Ozaki, A., Ishiyama, Y.: Detail Survey of Long-Term Energy Consumption for 80 Houses in Principal Cities of Japan - Description of the houses and end use structure of annual energy consumption. J. Environ. Eng., AIJ 603, 93–100 (2006) (in Japanese)Google Scholar
  2. 2.
    Yamazaki, T., Jung, J., Kim, Y., Hahn, M., Toyomura, T., Teng, R., Tan, Y., Matsuyama, T.: Energy Management in Home Environment Using a Power Sensor Network. Technical Report of IEICE 107, 71–76 (2008) (in Japanese)Google Scholar
  3. 3.
    Yamamoto, S., Takahashi, K., Okushi, A., Matsumoto, S., Nakamura, M.: A study of services using large-scale house log in Smart city. Technical Report of IEICE 112, 19–24 (2012) (in Japanese)Google Scholar
  4. 4.
    Wakabayashi, T., Tsuruoka, S., Kimura, F., Miyake, Y.: Study on feature selection in handwritten numeral recognition. Transactions of the Institute of Electronics, Information and Communication Engineers, J78-D-o.11, 1627–1638 (1995) (in Japanese)Google Scholar
  5. 5.
    Hata, Y., He, X., Miyawaki, F., Yamato, K.: Japanese Document Reader System. In: Proc. of the 2nd Singapore Int. Conf. on Image Processing, pp. 194–197 (1992)Google Scholar
  6. 6.
    Raman, M., Himanshu, A.: Study and Comparison of Various Image Edge Detection Techniques. International Journal of Image Processing 3, 1–12 (2009)CrossRefGoogle Scholar
  7. 7.
    Canny, J.: A Computational Approach to Edge Detection. IEEE Trans. Pattern Analysis and Machine Intelligence 8, 679–714 (1986)CrossRefGoogle Scholar
  8. 8.
    Otsu, N.: A Threshold Selection Method from Gray-Level Histograms. IEEE Transactions on Systems, Man, and Cybernetics, SMC-9, 62–66 (1979)Google Scholar
  9. 9.
    Sohail, K., Umar, I., Saquib, S., Asim, A.: Bhattacharyya Coefficient in Correlation of Gray-Scale Objects. Journal of Multimedia 1, 56–61 (2006)Google Scholar
  10. 10.
    Kanazawa, S., Taniguchi, K., Asari, K., Kuramoto, K., Kobashi, S., Hata, Y.: A Fuzzy Automated Object Classification by Infared Laser Camera. In: Proc of SPIE, vol. 8058 (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Tetsuya Fujisawa
    • 1
  • Tadahito Egawa
    • 2
  • Kazuhiko Taniguchi
    • 2
  • Syoji Kobashi
    • 1
    • 3
  • Yutaka Hata
    • 1
    • 3
  1. 1.Graduate School of EngineeringUniversity of HyogoHyogoJapan
  2. 2.Kinden CorporationKyotoJapan
  3. 3.WPI Immunology Frontier Research CenterOsaka UniversityOsakaJapan

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