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Image Orientation Estimation with Convolutional Networks

  • Philipp Fischer
  • Alexey Dosovitskiy
  • Thomas Brox
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9358)

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.

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Copyright information

© Springer International Publishing Switzerland 2015

Open Access This chapter is distributed under the terms of the Creative Commons Attribution Noncommercial License, which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.

Authors and Affiliations

  • Philipp Fischer
    • 1
  • Alexey Dosovitskiy
    • 1
  • Thomas Brox
    • 1
  1. 1.Department of Computer ScienceUniversity of FreiburgFreiburg im BreisgauGermany

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