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International Symposium on Visual Computing

ISVC 2016: Advances in Visual Computing pp 359–369Cite as

View-Based 3D Objects Recognition with Expectation Propagation Learning

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10073))

Abstract

In this paper, we develop an expectation propagation learning framework for the inverted Dirichlet (ID) and Dirichlet mixture models. The main goal is to implement an algorithm to recognize 3D objects. Those objects are in our case from a view-based 3D models database that we have assembled. Following specific rules determined by analyzing the results of our tests, we have been able to get promising recognition rates. Experimental results are presented with different object classes by comparing recognition rates and confidence levels according to different tuning parameters.

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Notes

  1. 1.

    https://www.rocq.inria.fr/gamma/gamma/download/download.php.

  2. 2.

    http://tf3dm.com/3d-models/vehicles.

  3. 3.

    http://www.3dmodelfree.com/3dmodel/list420-1.htm.

  4. 4.

    https://www.rocq.inria.fr/gamma/gamma/download/download.php.

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Acknowledgment

This research was funded by he NSTIP KACST grant (13-INF1123-10) and NSERC.

Author information

Authors and Affiliations

  1. Concordia University, Montreal, Québec, Canada

    Adrien Bertrand & Nizar Bouguila

  2. Department of Computer Engineering, College of Computer Systems, Umm Al-Qura University, Makkah, Saudi Arabia

    Faisal R. Al-Osaimi

Authors
  1. Adrien Bertrand
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  2. Faisal R. Al-Osaimi
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  3. Nizar Bouguila
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Corresponding author

Correspondence to Nizar Bouguila .

Editor information

Editors and Affiliations

  1. University of Nevada, Reno, Nevada, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, California, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, Berkeley, California, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, Nevada, USA

    Darko Koracin

  5. The Australian National University, O’Malley, Aust Capital Terr, Australia

    Fatih Porikli

  6. Pilot AI Labs, Redwood City, California, USA

    Sandra Skaff

  7. University of Florida, Gainesville, Florida, USA

    Alireza Entezari

  8. Google Inc., Mountain View, California, USA

    Jianyuan Min

  9. Osaka University, Osaka, Japan

    Daisuke Iwai

  10. The MOVES Institute, Monterey, California, USA

    Amela Sadagic

  11. University of Arizona, Tucson, Arizona, USA

    Carlos Scheidegger

  12. Université Paris-Sud, Orsay, France

    Tobias Isenberg

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Cite this paper

Bertrand, A., Al-Osaimi, F.R., Bouguila, N. (2016). View-Based 3D Objects Recognition with Expectation Propagation Learning. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2016. Lecture Notes in Computer Science(), vol 10073. Springer, Cham. https://doi.org/10.1007/978-3-319-50832-0_35

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  • DOI: https://doi.org/10.1007/978-3-319-50832-0_35

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50831-3

  • Online ISBN: 978-3-319-50832-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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