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Large Scale Visual Geo-Localization of Images in Mountainous Terrain

  • Georges Baatz
  • Olivier Saurer
  • Kevin Köser
  • Marc Pollefeys
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7573)

Abstract

Given a picture taken somewhere in the world, automatic geo-localization of that image is a task that would be extremely useful e.g. for historical and forensic sciences, documentation purposes, organization of the world’s photo material and also intelligence applications. While tremendous progress has been made over the last years in visual location recognition within a single city, localization in natural environments is much more difficult, since vegetation, illumination, seasonal changes make appearance-only approaches impractical. In this work, we target mountainous terrain and use digital elevation models to extract representations for fast visual database lookup. We propose an automated approach for very large scale visual localization that can efficiently exploit visual information (contours) and geometric constraints (consistent orientation) at the same time. We validate the system on the scale of a whole country (Switzerland, 40 000km2) using a new dataset of more than 200 landscape query pictures with ground truth.

Keywords

Digital Elevation Model Visual Word Visible Horizon Query Image Iterative Close Point 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Georges Baatz
    • 1
  • Olivier Saurer
    • 1
  • Kevin Köser
    • 1
  • Marc Pollefeys
    • 1
  1. 1.Department of Computer ScienceETH ZurichSwitzerland

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