Jet-based local image descriptors

Anders Boesen Lindbo Larsen, Sune Darkner, Anders Lindbjerg Dahl, Kim Steenstrup Pedersen

9 Citationer (Scopus)
3877 Downloads (Pure)

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.

OriginalsprogEngelsk
TitelComputer Vision – ECCV 2012 : 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012, Proceedings, Part III
RedaktørerAndrew Fitzgibbon, Svetlana Lazebnik, Pietro Perona, Yoichi Sato, Cardelia Schmid
Antal sider13
ForlagSpringer
Publikationsdato2012
Sider638-650
ISBN (Trykt)978-3-642-33711-6
ISBN (Elektronisk)978-3-642-33712-3
DOI
StatusUdgivet - 2012
Begivenhed12th European Conference on Computer Vision - Florence, Italien
Varighed: 7 okt. 201213 okt. 2012
Konferencens nummer: 12

Konference

Konference12th European Conference on Computer Vision
Nummer12
Land/OmrådeItalien
ByFlorence
Periode07/10/201213/10/2012
NavnLecture notes in computer science
Vol/bind7574
ISSN0302-9743

Emneord

  • Det Natur- og Biovidenskabelige Fakultet
  • Datalogi
  • Computer Vision
  • Interest point detector

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