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Jet-Based Local Image Descriptors

  • Anders Boesen Lindbo Larsen
  • Sune Darkner
  • Anders Lindbjerg Dahl
  • Kim Steenstrup Pedersen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7574)

Abstract

We present a general novel image descriptor based on higherorder differential geometry and investigate the effect of common descriptor choices. Our investigation is twofold in that we develop a jet-based descriptor and perform a comparative evaluation with current state-of-the-art descriptors on the recently released DTU Robot dataset. We demonstrate how the use of higher-order image structures enables us to reduce the descriptor dimensionality while still achieving very good performance. The descriptors are tested in a variety of scenarios including large changes in scale, viewing angle and lighting. We show that the proposed jet-based descriptor is superior to state-of-the-art for DoG interest points and show competitive performance for the other tested interest points.

Keywords

Interest Point Image Patch View Angle Linear Path Interest Point Detector 
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

  • Anders Boesen Lindbo Larsen
    • 1
  • Sune Darkner
    • 1
  • Anders Lindbjerg Dahl
    • 2
  • Kim Steenstrup Pedersen
    • 1
  1. 1.Department of Computer ScienceUniversity of CopenhagenDenmark
  2. 2.Department of Informatics and Mathematical ModellingTechnical University of DenmarkDenmark

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