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Cloud-Based Object Recognition: A System Proposal

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Robot Intelligence Technology and Applications 2

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 274))

Abstract

In this chapter, we will present a proposal for the cloud – based object recognition system. The system will extract the local features from the image and classify the object on the image using Membership Function ARTMAP (MF ARTMAP) or Gaussian Random Markov Field model. The feature extraction will be based on SIFT, SURF and ORB methods. Whole system will be built on the cloud architecture, to be readily available for the needs of the new emerging technological field of cloud robotics. Besides the system proposal, we specified research and technical goals for the following research.

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Authors and Affiliations

  1. Department of Cybernetics and Artificial Intelligence, Technical University of Kosice, Letna 9, Kosice, 04200, Slovak Republic

    Daniel Lorencik, Martina Tarhanicova & Peter Sincak

Authors
  1. Daniel Lorencik
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  2. Martina Tarhanicova
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  3. Peter Sincak
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Corresponding author

Correspondence to Daniel Lorencik .

Editor information

Editors and Affiliations

  1. Electrical Engineering, KAIST, Daejeon, Korea, Republic of (South Korea)

    Jong-Hwan Kim

  2. Computer and Information Technology, Purdue University, West Lafayette, Indiana, USA

    Eric T . Matson

  3. Civil and Environmental Engg., KAIST, Daejeon, Korea, Republic of (South Korea)

    Hyun Myung

  4. Department of Mechanical Engineering, Auckland University, Auckland, New Zealand

    Peter Xu

  5. University of Waterloo, Waterloo, Ontario, Canada

    Fakhri Karray

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Lorencik, D., Tarhanicova, M., Sincak, P. (2014). Cloud-Based Object Recognition: A System Proposal. In: Kim, JH., Matson, E., Myung, H., Xu, P., Karray, F. (eds) Robot Intelligence Technology and Applications 2. Advances in Intelligent Systems and Computing, vol 274. Springer, Cham. https://doi.org/10.1007/978-3-319-05582-4_61

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  • DOI: https://doi.org/10.1007/978-3-319-05582-4_61

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-05581-7

  • Online ISBN: 978-3-319-05582-4

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