Manifold blurring mean shift algorithms for manifold denoising

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn , Issue , 2010, Pages 1759-1766

  Wang, Weiran ^a   Carreira Perpiñán, Miguel Á ^a  

^a University of California   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DATA POINTS; DE-NOISING; DIMENSIONALITY REDUCTION; LOW-DIMENSIONAL MANIFOLDS; MEAN SHIFT; MEAN SHIFT ALGORITHM; NEAREST-NEIGHBORS; NON-PARAMETRIC;

COMPUTER VISION; NOISE POLLUTION CONTROL;

ALGORITHMS;

EID: 77956006565     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2010.5539845     Document Type: Conference Paper

References (23)

1. M. Á. Carreira-Perpiñán. Fast nonparametric clustering with Gaussian blurring mean-shift. ICML, 2006.
2. M. Á. Carreira-Perpiñán. Generalised blurring mean-shift algorithms for nonparametric clustering. CVPR, 2008.
3. M. Á. Carreira-Perpiñán and R. S. Zemel. Proximity graphs for clustering and manifold learning. NIPS, 2005.
4. Y. Cheng. Mean shift, mode seeking, and clustering. IEEE PAMI, 17(8):790-799, Aug. 1995.
5. E. Choi and P. Hall. Data sharpening as a prelude to density estimation. Biometrika, 86(4):941-947, Dec. 1999.
6. R. R. Coifman, S. Lafon, A. B. Lee,M.Maggioni, B. Nadler, F. Warner, and S. W. Zucker. Geometric diffusions as a tool for harmonic analysis and structure definition of data: Diffusion maps. Proc. Natl. Acad. Sci. USA, 102, 2005.
7. D. DeCoste and B. Schölkopf. Training invariant support vector machines. Machine Learning, 46, 2002.
8. M. Desbrun, M. Meyer, P. Schröder, and A. H. Barr. Implicit fairing of irregular meshes using diffusion and curvature flow. SIGGRAPH, 1999.
9. T. K. Dey. Curve and Surface Reconstruction: Algorithms with Mathematical Analysis. Cambridge U. Press, 2007.
10. K. Fukunaga and L. D. Hostetler. The estimation of the gradient of a density function, with application in pattern recognition. IEEE Trans. Information Theory, IT-21, 1975.
11. M. Hein and M. Maier. Manifold denoising. NIPS, 2007.
12. C. Lange and K. Polthier. Anisotropic smoothing of point sets. Computer Aided Geometric Design, 22, 2005.
13. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. Gradient-based learning applied to document recognition. Proc. IEEE, 86(11):2278-2324, Nov. 1998.
14. D. Levin. Mesh-independent surface interpolation. In G. Brunnett, B. Hamann, H. Müller, and L. Linsen, editors, Geometric Modeling for Scientific Visualization, pages 37-49. Springer-Verlag, 2003.
15. J. Park, Z. Zhang, H. Zha, and R. Kasturi. Local smoothing for manifold learning. CVPR, 2004.
16. M. Pauly, L. Kobbelt, and M. Gross. Point-based multiscale surface representation. ACM Trans. Graphics, 2006.
17. P. Simard, Y. LeCun, , and J. Denker. Efficient pattern recognition using a new transformation distance. NIPS, 1993.
18. R. Subbarao and P. Meer. Nonlinear mean shift over Riemannian manifolds. Int. J. Computer Vision, 84, 2009.
19. G. Taubin. A signal processing approach to fair surface design. SIGGRAPH, 1995.
20. J. B. Tenenbaum, V. de Silva, and J. C. Langford. A global geometric framework for nonlinear dimensionality reduction. Science, 290(5500):2319-2323, Dec. 22 2000.
21. M. E. Tipping and C. M. Bishop. Mixtures of probabilistic principal component analyzers. Neural Computation, 11(2):443-482, Feb. 1999.
22. R. Unnikrishnan and M. Hebert. Denoising manifold and non-manifold point clouds. British Machine Vision Conference (BMVC), 2007.
23. Z. Zhang and H. Zha. Principal manifolds and nonlinear dimension reduction via local tangent space alignment. SIAM J. Sci. Comput., 26(1):313-338, 2004.