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Probabilistic Color Optical Flow

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Part of the Lecture Notes in Computer Science book series (LNIP,volume 3663)

Abstract

Usually, optical flow computation is based on grayscale images and the brightness conservation assumption. Recently, some authors have investigated in transferring gradient-based grayscale optical flow methods to color images. These color optical flow methods are restricted to brightness and color conservation over time. In this paper, a correlation-based color optical flow method is presented that allows for brightness and color changes within an image sequence. Further on, the correlation results are used for a probabilistic evaluation that combines the velocity information gained from single color frames to a joint velocity estimate including all color frames. The resulting color optical flow is compared to other representative multi-frame color methods and standard single-frame grayscale methods.

Keywords

  • Color Image
  • Color Space
  • Grayscale Image
  • Color Channel
  • Consecutive Image

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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© 2005 Springer-Verlag Berlin Heidelberg

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Willert, V., Eggert, J., Clever, S., Körner, E. (2005). Probabilistic Color Optical Flow. In: Kropatsch, W.G., Sablatnig, R., Hanbury, A. (eds) Pattern Recognition. DAGM 2005. Lecture Notes in Computer Science, vol 3663. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550518_2

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  • DOI: https://doi.org/10.1007/11550518_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28703-2

  • Online ISBN: 978-3-540-31942-9

  • eBook Packages: Computer ScienceComputer Science (R0)