Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Pattern Recognition |
| Untertitel | 33rd DAGM Symposium, Proceedings |
| Seiten | 276-285 |
| Seitenumfang | 10 |
| ISBN (elektronisch) | 978-3-642-23123-0 |
| Publikationsstatus | Veröffentlicht - 2011 |
| Veranstaltung | 33rd Annual Symposium of German Pattern Recognition Association, DAGM 2011 - Frankfurt/Main, Deutschland Dauer: 31 Aug. 2011 → 2 Sept. 2011 |
Publikationsreihe
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Band | 6835 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (elektronisch) | 1611-3349 |
Abstract
The profile of a 10mm wide and 1μm deep grinding imprint is as unique as a human fingerprint. To utilize this for fingerprinting mechanical components, a robust and strong characterization has to be used. We propose a feature-based approach, in which features of a 1D profile are detected and described in its 2D space-frequency representation. We show that the approach is robust on depth maps as well as intensity images of grinding imprints. To estimate the probability of misclassification, we derive a model and learn its parameters. With this model we demonstrate that our characterization has a false positive rate of approximately 10-20 which is as strong as a human fingerprint.
ASJC Scopus Sachgebiete
- Mathematik (insg.)
- Theoretische Informatik
- Informatik (insg.)
- Allgemeine Computerwissenschaft
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Pattern Recognition : 33rd DAGM Symposium, Proceedings. 2011. S. 276-285 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 6835 LNCS).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Fingerprints for Machines
T2 - 33rd Annual Symposium of German Pattern Recognition Association, DAGM 2011
AU - Dragon, Ralf
AU - Mörke, Tobias
AU - Rosenhahn, Bodo
AU - Ostermann, Jörn
PY - 2011
Y1 - 2011
N2 - The profile of a 10mm wide and 1μm deep grinding imprint is as unique as a human fingerprint. To utilize this for fingerprinting mechanical components, a robust and strong characterization has to be used. We propose a feature-based approach, in which features of a 1D profile are detected and described in its 2D space-frequency representation. We show that the approach is robust on depth maps as well as intensity images of grinding imprints. To estimate the probability of misclassification, we derive a model and learn its parameters. With this model we demonstrate that our characterization has a false positive rate of approximately 10-20 which is as strong as a human fingerprint.
AB - The profile of a 10mm wide and 1μm deep grinding imprint is as unique as a human fingerprint. To utilize this for fingerprinting mechanical components, a robust and strong characterization has to be used. We propose a feature-based approach, in which features of a 1D profile are detected and described in its 2D space-frequency representation. We show that the approach is robust on depth maps as well as intensity images of grinding imprints. To estimate the probability of misclassification, we derive a model and learn its parameters. With this model we demonstrate that our characterization has a false positive rate of approximately 10-20 which is as strong as a human fingerprint.
UR - http://www.scopus.com/inward/record.url?scp=80053027902&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-23123-0_28
DO - 10.1007/978-3-642-23123-0_28
M3 - Conference contribution
AN - SCOPUS:80053027902
SN - 9783642231223
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 276
EP - 285
BT - Pattern Recognition
Y2 - 31 August 2011 through 2 September 2011
ER -