Image-Based Large-Scale Geo-localization in Mountainous Regions View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2016

AUTHORS

Olivier Saurer , Georges Baatz , Kevin Köser , L’ubor Ladický , Marc Pollefeys

ABSTRACT

Given a picture taken somewhere in the world, automatic geo-localization of such an image is an extremely useful task especially for historical and forensic sciences, documentation purposes, organization of the world’s photographs and 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 chapter, 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 at the scale of Switzerland (\(40\,000\,\mathrm {km}^2\)) using over 1000 landscape query images with ground truth GPS position. More... »

PAGES

205-223

Book

TITLE

Large-Scale Visual Geo-Localization

ISBN

978-3-319-25779-2
978-3-319-25781-5

From Grant

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-25781-5_11

DOI

http://dx.doi.org/10.1007/978-3-319-25781-5_11

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1052048677


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