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Adaptive Rendering for Large-Scale Skyline Characterization and Matching

  • Jiejie Zhu
  • Mayank Bansal
  • Nick Vander Valk
  • Hui Cheng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7583)

Abstract

We propose an adaptive rendering approach for large-scale skyline characterization and matching with applications to automated geo-tagging of photos and images. Given an image, our system automatically extracts the skyline and then matches it to a database of reference skylines extracted from rendered images using digital elevation data (DEM). The sampling density of these rendering locations determines both the accuracy and the speed of skyline matching. The proposed approach successfully combines global planning and local greedy search strategies to select new rendering locations incrementally. We report quantitative and qualitative results from synthesized and real experiments, where we achieve a computational speedup of around 4X.

Keywords

Transformation Model Query Image Query Skyline Skyline Point Reprojection Error 
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

  • Jiejie Zhu
    • 1
  • Mayank Bansal
    • 1
  • Nick Vander Valk
    • 1
  • Hui Cheng
    • 1
  1. 1.Vision Technologies Lab.SRI InternationalPrincetonUSA

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