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Statistics of Flow Vectors and Its Application to the Voting Method for the Detection of Flow Fields

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Machine Learning and Data Mining in Pattern Recognition (MLDM 2001)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2123))

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Abstract

In this paper, we show that the randomized sampling and voting process detects linear flow filed as a model-fitting problem. We introduce a random sampling method for solving the least-square model-fitting-problem using a mathematical property for the construction of pseudo-inverse. If we use an appropriate number of images from a sequence of images, it is possible to detect subpixel motion in this sequence. We use the accumulator space for the unification of these flow vectors which are computed from different time intervals. Numerical examples for the test image sequences show the performance of our method.

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References

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Author information

Authors and Affiliations

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

    Atsushi Imiya & Keisuke Iwawaki

Authors
  1. Atsushi Imiya
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  2. Keisuke Iwawaki
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Editor information

Editors and Affiliations

  1. Institute of Computer Vision and Applied Computer Sciences, Arno-Nitzsche-Str. 45, 04277, Leipzig, Germany

    Petra Perner

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© 2001 Springer-Verlag Berlin Heidelberg

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Imiya, A., Iwawaki, K. (2001). Statistics of Flow Vectors and Its Application to the Voting Method for the Detection of Flow Fields. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2001. Lecture Notes in Computer Science(), vol 2123. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44596-X_24

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  • DOI: https://doi.org/10.1007/3-540-44596-X_24

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42359-1

  • Online ISBN: 978-3-540-44596-8

  • eBook Packages: Springer Book Archive

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