Open Access
Issue |
Int. J. Simul. Multidisci. Des. Optim.
Volume 13, 2022
Computation Challenges for engineering problems
|
|
---|---|---|
Article Number | 26 | |
Number of page(s) | 9 | |
DOI | https://doi.org/10.1051/smdo/2022011 | |
Published online | 23 December 2022 |
- E. Kalogerakis, A. Hertzmann, K. Singh, Learning 3D Mesh segmentation and labelling, ACM Trans. Graph. 29, 3 (2010) [CrossRef] [Google Scholar]
- A. Van Biesbroeck, F. Shang, D. Bassir, CAD model segmentation via deep learning, Int. J. Comput. Methods 18, 4–7 (2021) [Google Scholar]
- S.M. Holland, Principal components analysis (PCA) (Departmentof Geology, University of Georgia, Athens, GA, 30602–2501, 2008) [Google Scholar]
- P. Wang, P. Wang, Z. Qu, Y. Gao, Z. Shen, A refined coherent point drift (CPD) algorithm for point set registration, Sci. China Inf. Sci. 54, 2639–2646 (2011) [CrossRef] [MathSciNet] [Google Scholar]
- A. Myronenko, X. Song, Point set registration: Coherent point drift, IEEE Trans. Pattern Anal. Mach. Intell. 32, 2262–2275 (2010) [CrossRef] [Google Scholar]
- J.E. Mottershead, C. Mares, S. James, M.I. Friswell, Stochastic model updating: part 2—application to a set of physical structures, Mech. Syst. Signal Process. 20, 2171–2185 (2006) [CrossRef] [Google Scholar]
- Myronenko, X. Song, M.A. Carreira-Perpinán, Non-rigid point set registration: Coherent point drift, In Advances in Neural Information Processing Systems (2007), pp. 1009–1016 [Google Scholar]
- Z. Li, J. Wang, Least squares image matching: a comparison of the performance of robust estimators, ISPRS Ann. Photogram. Remote Sens. Spat. Inf. Sci. 2, 37 (2014) [CrossRef] [Google Scholar]
- J. Lee et al., An in-depth comparison of subgraph isomorphism algorithms in graph databases, in Proceedings of the VLDB Endowment (VLDB Endowment, 2012), vol. 6, pp. 133–144 [CrossRef] [Google Scholar]
- D. Tang, J. Man, L. Tang, Y. Feng, Q. Yang, WEDMS: An advanced mean shift clustering algorithm for LDoS attacks detection, Ad Hoc Networks 102, 2–3 (2020) [Google Scholar]
- J.A. Hartigan, M.A. Wong, Algorithm AS 136: A k-means clustering algorithm, J. Royal Stat. Soc. Ser. C 28, 100–108 (1979) [Google Scholar]
- T. Kanungo, D.M. Mount, N.S. Netanyahu, C.D. Piatko, R. Silverman, A.Y. Wu, An efficient k-means clustering algorithm: analysis and implementation, IEEE Trans. Pattern Anal. Mach. Intell. 24, 881–892 (2002) [CrossRef] [Google Scholar]
- M. Ester, H.P. Kriegel, J. Sander, X. Xu, A density-based algorithm for discovering clusters in large spatial databases with noise, Kdd 96, 226–231 (1996) [Google Scholar]
- G. Kumar, P.K. Bhatia, A detailed review of feature extraction in image processing systems, in Advanced Computing & Communication Technologies F (ACCT), 2014 Fourth International Conference on (IEEE, 2014), pp. 5–12 [CrossRef] [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.