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Shape Recovery from an Unorganized Image Sequence

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Machine Learning and Data Mining in Pattern Recognition (MLDM 2003)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2734))

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Abstract

We propose a method for recovering a 3D object from an unorganized image sequence, in which the order of the images and the corresponding points among the images are unknown, using a random sampling and voting process. Least squares methods such that the factorization method and the 8-point algorithm are not directly applicable to an unorganized image sequence, because the corresponding points are a priori unknown. The proposed method repeatedly generates relevant shape parameters from randomly sampled data as a series of hypotheses, and finally produces the solutions supported by a large number of the hypotheses. The method is demonstrated on synthetic and real data.

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Author information

Authors and Affiliations

  1. Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology, 4259 Nagatsuta, Midori-ku, Yokohama, 226-8502, Japan

    Kazuhiko Kawamoto & Kaoru Hirota

  2. Institute of Media and Information Technology, Chiba University, 1-33 Yayoi-cho, Inage-ku, 263-8522, Chiba, Japan

    Atsushi Imiya

  3. Software Research Division, National Institute of Informatics, 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo, 101-8430, Japan

    Atsushi Imiya

Authors
  1. Kazuhiko Kawamoto
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  2. Atsushi Imiya
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  3. Kaoru Hirota
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Editor information

Editors and Affiliations

  1. Institute of Computer Vision and Applied Computer Sciences, Arndtstr. 4, 04275, Leipzig, Germany

    Petra Perner

  2. Center for Automation Research, University of Maryland, College Park, Maryland, 20742-3275, USA

    Azriel Rosenfeld

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© 2003 Springer-Verlag Berlin Heidelberg

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Kawamoto, K., Imiya, A., Hirota, K. (2003). Shape Recovery from an Unorganized Image Sequence. In: Perner, P., Rosenfeld, A. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2003. Lecture Notes in Computer Science, vol 2734. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45065-3_34

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  • DOI: https://doi.org/10.1007/3-540-45065-3_34

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40504-7

  • Online ISBN: 978-3-540-45065-8

  • eBook Packages: Springer Book Archive

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