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International Workshop on Support Vector Machines

SVM 2002: Pattern Recognition with Support Vector Machines pp 112–119Cite as

Maintenance Training of Electric Power Facilities Using Object Recognition by SVM

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2388))

Abstract

We are developing a support system for maintenance training of electric power facilities using augmented reality. To develop in the system, we evaluated the use of Support Vector Machine Classifier (SVM) for object recognition. This paper presents our experimental results of object recognition by combinations of SVMs. The recognition results of over 10,000 images show very high performance rates. The support system that uses the combinations of SVMs works in real time without special marks or sensors.

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References

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Author information

Authors and Affiliations

  1. Central Research Institute of Electric Power Industry, Tokyo, 201-8511, Japan

    Chikahito Nakajima

  2. Department of Information Engineering, University of Siena, Via Roma 56, 53100, Siena, Italy

    Massimiliano Pontil

Authors
  1. Chikahito Nakajima
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  2. Massimiliano Pontil
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Korea University, Anam-dong, Seongbuk-ku, Seoul, 136-701, Korea

    Seong-Whan Lee

  2. Dipartimento di Informatica e Scienze dell’Informazione, Università di Genova, Via Dodecaneso 35, 16146, Genova, Italy

    Alessandro Verri

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© 2002 Springer-Verlag Berlin Heidelberg

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Cite this paper

Nakajima, C., Pontil, M. (2002). Maintenance Training of Electric Power Facilities Using Object Recognition by SVM. In: Lee, SW., Verri, A. (eds) Pattern Recognition with Support Vector Machines. SVM 2002. Lecture Notes in Computer Science, vol 2388. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45665-1_9

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  • DOI: https://doi.org/10.1007/3-540-45665-1_9

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44016-1

  • Online ISBN: 978-3-540-45665-0

  • eBook Packages: Springer Book Archive

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