Smoothing Three-Dimensional Manifold Data, with Application to Tectonic Fault Detection-Reference-Cited by-同舟云学术

Smoothing Three-Dimensional Manifold Data, with Application to Tectonic Fault Detection

Published:2016-04-07 Issue:5 Volume:48 Page:487-510
ISSN:1874-8961
Container-title:Mathematical Geosciences
language:en
Short-container-title:Math Geosci

Author:

Grillenzoni Carlo

Publisher

Springer Science and Business Media LLC

Subject

General Earth and Planetary Sciences,Mathematics (miscellaneous)

Link

http://link.springer.com/content/pdf/10.1007/s11004-015-9630-x.pdf

Reference27 articles.

1. Aliyari-Ghassabeh Y, Linder T, Takahara G (2013) On convergence properties of the subspace constrained mean shift. Pattern Recognit 46(11):3140–3147

2. Biedermann S, Yang M (2015) Designs for selected nonlinear models. In: Dean A, Morris M, Stufken J, Bingham D (eds) Handbook of design and analysis of experiments. Chapman & Hall, London, pp 515–547

3. Carreira-Perpiñán MÁ (2006) Fast nonparametric clustering with Gaussian blurring mean-shift. In: Proceedings of 23rd international conference on machine learning. Pittsburgh, pp 153–160

4. Caumon G, Collon-Drouaillet P (2014) Special issues on three-dimensional structural modeling. Math Geosci 46(8):905–908

5. Chen Y (1995) Mean shift, mode seeking and clustering. IEEE Trans Pattern Anal Mach Intell 17(8):790–799

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