Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 205-221 |
| Seitenumfang | 17 |
| Fachzeitschrift | Visual Computer |
| Jahrgang | 31 |
| Ausgabenummer | 2 |
| Frühes Online-Datum | 4 Nov. 2014 |
| Publikationsstatus | Veröffentlicht - Feb. 2015 |
Abstract
This publication is a contribution to basic research in image comparison using eigenvalue spectra as features. The differential-geometric approach of eigenvalue spectrum-based descriptors is naturally applicable to shape data, but so far little work has been done to transfer it to the setting of image data painted on a rectangle or general curved surface. We present a new semi-global feature descriptor that also contains information about geometry of shapes visible in the image. This may not only improve the performance of the resulting distance measures, but may even enable us to approach the partial matching problem using eigenvalue spectra, which were previously only considered as global feature descriptors. We introduce some concepts that are useful in designing and understanding the behaviour of similar fingerprinting algorithms for images (and surfaces) and discuss some preliminary results.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Software
- Informatik (insg.)
- Maschinelles Sehen und Mustererkennung
- Informatik (insg.)
- Computergrafik und computergestütztes Design
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in: Visual Computer, Jahrgang 31, Nr. 2, 02.2015, S. 205-221.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Subimage sensitive eigenvalue spectra for image comparison
T2 - Can one hear what’s painted on a drum?
AU - Berger, Benjamin
AU - Vais, Alexander
AU - Wolter, Franz Erich
N1 - Publisher Copyright: © 2014, Springer-Verlag Berlin Heidelberg.
PY - 2015/2
Y1 - 2015/2
N2 - This publication is a contribution to basic research in image comparison using eigenvalue spectra as features. The differential-geometric approach of eigenvalue spectrum-based descriptors is naturally applicable to shape data, but so far little work has been done to transfer it to the setting of image data painted on a rectangle or general curved surface. We present a new semi-global feature descriptor that also contains information about geometry of shapes visible in the image. This may not only improve the performance of the resulting distance measures, but may even enable us to approach the partial matching problem using eigenvalue spectra, which were previously only considered as global feature descriptors. We introduce some concepts that are useful in designing and understanding the behaviour of similar fingerprinting algorithms for images (and surfaces) and discuss some preliminary results.
AB - This publication is a contribution to basic research in image comparison using eigenvalue spectra as features. The differential-geometric approach of eigenvalue spectrum-based descriptors is naturally applicable to shape data, but so far little work has been done to transfer it to the setting of image data painted on a rectangle or general curved surface. We present a new semi-global feature descriptor that also contains information about geometry of shapes visible in the image. This may not only improve the performance of the resulting distance measures, but may even enable us to approach the partial matching problem using eigenvalue spectra, which were previously only considered as global feature descriptors. We introduce some concepts that are useful in designing and understanding the behaviour of similar fingerprinting algorithms for images (and surfaces) and discuss some preliminary results.
KW - Eigenvalue
KW - Fingerprint
KW - Image comparison
KW - Image retrieval
KW - Laplace
KW - Partial matching
KW - Perturbation theory
UR - http://www.scopus.com/inward/record.url?scp=84925484171&partnerID=8YFLogxK
U2 - 10.1007/s00371-014-1038-y
DO - 10.1007/s00371-014-1038-y
M3 - Article
AN - SCOPUS:84925484171
VL - 31
SP - 205
EP - 221
JO - Visual Computer
JF - Visual Computer
SN - 0178-2789
IS - 2
ER -