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Two Algorithms for Motion Estimation from Alternate Exposure Images

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Video Processing and Computational Video

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 7082))

Abstract

Most algorithms for dense 2D motion estimation assume pairs of images that are acquired with an idealized, infinitively short exposure time. In this work we compare two approaches that use an additional, motion-blurred image of a scene to estimate highly accurate, dense correspondence fields.

We consider video sequences that are acquired with alternating exposure times so that a short-exposure image is followed by a long-exposure image that exhibits motion-blur. For both motion estimation algorithms we employ an image formation model that relates the motion blurred image to two enframing short-exposure images. With this model we can decipher the motion information encoded in the long-exposure image, but also estimate occlusion timings which are a prerequisite for artifact-free frame interpolation. The first approach solves for the motion in a pointwise least squares formulation while the second formulates a global, total variation regularized problem. Both approaches are evaluated in detail and compared to each other and state-of-the-art motion estimation algorithms.

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Author information

Authors and Affiliations

  1. Institut für Computergraphik, TU Braunschweig, Germany

    Anita Sellent, Martin Eisemann & Marcus Magnor

Authors
  1. Anita Sellent
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  2. Martin Eisemann
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  3. Marcus Magnor
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Editor information

Editors and Affiliations

  1. Department of Computer Science, TU München, Germany

    Daniel Cremers

  2. Computer Graphics Lab, Mühlenpfordstrasse 23, 38106, Braunschweig, Germany

    Marcus Magnor

  3. Technische Universität München, Germany

    Martin R. Oswald

  4. Technion, Haifa, Israel

    Lihi Zelnik-Manor

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Sellent, A., Eisemann, M., Magnor, M. (2011). Two Algorithms for Motion Estimation from Alternate Exposure Images. In: Cremers, D., Magnor, M., Oswald, M.R., Zelnik-Manor, L. (eds) Video Processing and Computational Video. Lecture Notes in Computer Science, vol 7082. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24870-2_2

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  • DOI: https://doi.org/10.1007/978-3-642-24870-2_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-24869-6

  • Online ISBN: 978-3-642-24870-2

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