Shape recognition with spectral distances
Bronstein MM, Bronstein AM. 2011. Shape recognition with spectral distances. IEEE Transactions on Pattern Analysis and Machine Intelligence. 33(5), 1065–1071.
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Journal Article
| Published
| English
Scopus indexed
Author
Bronstein, Michael M;
Bronstein, Alex M.ISTA 
Abstract
Recent works have shown the use of diffusion geometry for various pattern recognition applications, including nonrigid shape analysis. In this paper, we introduce spectral shape distance as a general framework for distribution-based shape similarity and show that two recent methods for shape similarity due to Rustamov and Mahmoudi and Sapiro are particular cases thereof.
Publishing Year
Date Published
2011-05-01
Journal Title
IEEE Transactions on Pattern Analysis and Machine Intelligence
Publisher
Institute of Electrical and Electronics Engineers
Volume
33
Issue
5
Page
1065-1071
ISSN
IST-REx-ID
Cite this
Bronstein MM, Bronstein AM. Shape recognition with spectral distances. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2011;33(5):1065-1071. doi:10.1109/tpami.2010.210
Bronstein, M. M., & Bronstein, A. M. (2011). Shape recognition with spectral distances. IEEE Transactions on Pattern Analysis and Machine Intelligence. Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/tpami.2010.210
Bronstein, Michael M, and Alex M. Bronstein. “Shape Recognition with Spectral Distances.” IEEE Transactions on Pattern Analysis and Machine Intelligence. Institute of Electrical and Electronics Engineers, 2011. https://doi.org/10.1109/tpami.2010.210.
M. M. Bronstein and A. M. Bronstein, “Shape recognition with spectral distances,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 5. Institute of Electrical and Electronics Engineers, pp. 1065–1071, 2011.
Bronstein MM, Bronstein AM. 2011. Shape recognition with spectral distances. IEEE Transactions on Pattern Analysis and Machine Intelligence. 33(5), 1065–1071.
Bronstein, Michael M., and Alex M. Bronstein. “Shape Recognition with Spectral Distances.” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 5, Institute of Electrical and Electronics Engineers, 2011, pp. 1065–71, doi:10.1109/tpami.2010.210.
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