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Subpixel Flow Detection by the Hough Transform

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Robot Vision (RobVis 2001)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1998))

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Abstract

In this paper, we show that randomized sampling and voting processes allow to treat linear flow field detection as a model-fitting problem. 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.

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References

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Author information

Authors and Affiliations

  1. Computer Science Division, Dept. of Information and Image Sciences, 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. Center for Image Technology and Robotics (CITR Tamaki), The University of Auckland, Tamaki Campus, Building 731, 1005, Auckland, New Zealand

    Reinhard Klette

  2. Department of Computer Science, The Hebrew University of Jerusalem, Givat Ram, Ross Building, 91904, Jerusalem, Israel

    Shmuel Peleg

  3. Institut für Informatik, Universitäet Kiel, Preusserstr.1-9, 24105, Kiel, Germany

    Gerald Sommer

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© 2001 Springer-Verlag Berlin Heidelberg

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Imiya, A., Iwawaki, K. (2001). Subpixel Flow Detection by the Hough Transform. In: Klette, R., Peleg, S., Sommer, G. (eds) Robot Vision. RobVis 2001. Lecture Notes in Computer Science, vol 1998. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44690-7_18

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  • DOI: https://doi.org/10.1007/3-540-44690-7_18

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41694-4

  • Online ISBN: 978-3-540-44690-3

  • eBook Packages: Springer Book Archive

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