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Image Orientation Estimation with Convolutional Networks

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Pattern Recognition (DAGM 2015)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9358))

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Abstract

Rectifying the orientation of scanned documents has been an important problem that was solved long ago. In this paper, we focus on the harder case of estimating and correcting the exact orientation of general images, for instance, of holiday snapshots. Especially when the horizon or other horizontal and vertical lines in the image are missing, it is hard to find features that yield the canonical orientation of the image. We demonstrate that a convolutional network can learn subtle features to predict the canonical orientation of images. In contrast to prior works that just distinguish between portrait and landscape orientation, the network regresses the exact orientation angle. The approach runs in real-time and, thus, can be applied also to live video streams.

P. Fischer—Supported by a scholarship of the Deutsche Telekom Stiftung.

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Authors and Affiliations

  1. Department of Computer Science, University of Freiburg, Freiburg im Breisgau, Germany

    Philipp Fischer, Alexey Dosovitskiy & Thomas Brox

Authors
  1. Philipp Fischer
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  2. Alexey Dosovitskiy
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  3. Thomas Brox
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Corresponding author

Correspondence to Philipp Fischer .

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Editors and Affiliations

  1. Institute of Computer Science III, University of Bonn, Bonn, Germany

    Juergen Gall

  2. MPI for Intelligent Systems, University of Tübingen, Tübingen, Germany

    Peter Gehler

  3. Computer Vision Group, Visual Computing Institute, RWTH Aachen, Aachen, Germany

    Bastian Leibe

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Fischer, P., Dosovitskiy, A., Brox, T. (2015). Image Orientation Estimation with Convolutional Networks. In: Gall, J., Gehler, P., Leibe, B. (eds) Pattern Recognition. DAGM 2015. Lecture Notes in Computer Science(), vol 9358. Springer, Cham. https://doi.org/10.1007/978-3-319-24947-6_30

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  • DOI: https://doi.org/10.1007/978-3-319-24947-6_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24946-9

  • Online ISBN: 978-3-319-24947-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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