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Online Multibody Factorization Based on Bayesian Principal Component Analysis of Gaussian Mixture Models

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Advances in Neuro-Information Processing (ICONIP 2008)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5506))

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Abstract

An online multibody factorization method for recovering the shape of each object from a sequence of monocular images is proposed. We formulate multibody factorization problem of data matrix of feature positions as the parameter estimation of the mixtures of probabilistic principal component analysis (MPPCA) and use the variational inference method as an estimation algorithm that concurrently performs classification of each feature points and the three-dimensional structures of each object. We also apply the online variational inference method make the algorithm suitable for real-time applications.

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Authors and Affiliations

  1. Graduate School of Information Science, Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma, Nara, Japan

    Kentarou Hitomi, Kazushi Ikeda & Tomohiro Shibata

  2. DENSO CORPORATION, 1-1, Showa-cho, Kariya, Aichi, 4488661, Japan

    Kentarou Hitomi, Takashi Bando & Naoki Fukaya

Authors
  1. Kentarou Hitomi
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  2. Takashi Bando
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  3. Naoki Fukaya
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  4. Kazushi Ikeda
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  5. Tomohiro Shibata
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Editor information

Editors and Affiliations

  1. Kyushu Institute of Technology, Network Design and Research Center, 680-4 Fukuoka, 820-8502, Kawazu, Iizuka, Japan

    Mario Köppen

  2. Knowledge Engineering and Discovery Research Institute (KEDRI), School of Computing and Mathematical Sciences, Auckland University of Technology, 350 Queen Street, 10110, Auckland, New Zealand

    Nikola Kasabov

  3. Department of Electrical and Computer Engineering, Robotics Laboratory, Auckland University of Technology, 38 Princes Street, 1142, Auckland, New Zealand

    George Coghill

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© 2009 Springer-Verlag Berlin Heidelberg

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Hitomi, K., Bando, T., Fukaya, N., Ikeda, K., Shibata, T. (2009). Online Multibody Factorization Based on Bayesian Principal Component Analysis of Gaussian Mixture Models. In: Köppen, M., Kasabov, N., Coghill, G. (eds) Advances in Neuro-Information Processing. ICONIP 2008. Lecture Notes in Computer Science, vol 5506. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02490-0_83

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  • DOI: https://doi.org/10.1007/978-3-642-02490-0_83

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-02489-4

  • Online ISBN: 978-3-642-02490-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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