DocumentCode :
2995031
Title :
User-Driven Geolocation of Untagged Desert Imagery Using Digital Elevation Models
Author :
Tzeng, Eric ; Zhai, Antonia ; Clements, Matthew ; Townshend, Raphael ; Zakhor, Avideh
Author_Institution :
Signetron Inc., Univ. of California, Berkeley, Berkeley, CA, USA
fYear :
2013
fDate :
23-28 June 2013
Firstpage :
237
Lastpage :
244
Abstract :
We propose a system for user-aided visual localization of desert imagery without the use of any metadata such as GPS readings, camera focal length, or field-of-view. The system makes use only of publicly available digital elevation models (DEMs) to rapidly and accurately locate photographs in non-urban environments such as deserts. Our system generates synthetic skyline views from a DEM and extracts stable concavity-based features from these skylines to form a database. To localize queries, a user manually traces the skyline on an input photograph. The skyline is automatically refined based on this estimate, and the same concavity-based features are extracted. We then apply a variety of geometrically constrained matching techniques to efficiently and accurately match the query skyline to a database skyline, thereby localizing the query image. We evaluate our system using a test set of 44 ground-truthed images over a 10, 000 km2 region of interest in a desert and show that in many cases, queries can be localized with precision as fine as 100 m2.
Keywords :
digital elevation models; feature extraction; geographic information systems; geometry; geophysical image processing; image matching; photography; query processing; visual databases; DEM; concavity-based features extraction; database skyline; digital elevation models; geometrically constrained matching techniques; ground-truthed images; nonurban environments; photograph location; query image; query skyline; synthetic skyline views; untagged desert imagery; user-aided visual localization; user-driven geolocation; Cameras; Databases; Feature extraction; Geology; Image edge detection; Tiles; Vectors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Vision and Pattern Recognition Workshops (CVPRW), 2013 IEEE Conference on
Conference_Location :
Portland, OR
Type :
conf
DOI :
10.1109/CVPRW.2013.42
Filename :
6595881
Link To Document :
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