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.
Originalsprog | Engelsk |
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Titel | Computer Vision – ECCV 2012 : 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012, Proceedings, Part III |
Redaktører | Andrew Fitzgibbon, Svetlana Lazebnik, Pietro Perona, Yoichi Sato, Cardelia Schmid |
Antal sider | 13 |
Forlag | Springer |
Publikationsdato | 2012 |
Sider | 638-650 |
ISBN (Trykt) | 978-3-642-33711-6 |
ISBN (Elektronisk) | 978-3-642-33712-3 |
DOI | |
Status | Udgivet - 2012 |
Begivenhed | 12th European Conference on Computer Vision - Florence, Italien Varighed: 7 okt. 2012 → 13 okt. 2012 Konferencens nummer: 12 |
Konference
Konference | 12th European Conference on Computer Vision |
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Nummer | 12 |
Land/Område | Italien |
By | Florence |
Periode | 07/10/2012 → 13/10/2012 |
Navn | Lecture notes in computer science |
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Vol/bind | 7574 |
ISSN | 0302-9743 |
Emneord
- Det Natur- og Biovidenskabelige Fakultet
- Datalogi
- Computer Vision
- Interest point detector