Implicit surface modelling as an eigenvalue problem

ICML 2005 - Proceedings of the 22nd International Conference on Machine Learning

Volumn , Issue , 2005, Pages 937-944

  Walder, Christian ^a   Chapelle, Olivier ^a   Schölkopf, Bernhard ^a  

^a MAX PLANCK INSTITUTE FOR BIOLOGICAL CYBERNETICS   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

FUNCTIONS; INTERPOLATION; OPTIMAL SYSTEMS; PROBLEM SOLVING; TOPOLOGY;

EIGENVALUE PROBLEM; EMBEDDING FUNCTION; SURFACE MODELING;

EIGENVALUES AND EIGENFUNCTIONS;

EID: 31844433764     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (14)

1. Alexa, M., Behr, J., Cohen-Or, D., Fleishman, S., Levin, D., & Silva, C. T. (2001). Point set surfaces. IEEE Visualization 2001 (pp. 21-28). San Diego, California: IEEE Computer Society.
2. Carr, J. C., Beatson, R. K., Cherrie, J. B., Mitchell, T. J., Fright, W. R., McCallum, B. C., & Evans, T. R. (2001). Reconstruction and representation of 3d objects with radial basis functions. ACM SIGGRAPH 2001 (pp. 67-76). ACM Press.
3. Duchon, J. (1976). Interpolation des fonctions de deux variables suivant le principe de la flexion des plaques minces. RAIRO Analyse Numrique, 10, 5-12.
4. Girosi, F., Jones, M., & Poggio, T. (1993). Priors stabilizers and basis functions: From regularization to radial, tensor and additive splines (Technical Report AI 1430). Massachusetts Institute of Technology.
5. Golub, G. H., & Van Loan, C. F. (1996). Matrix computations. Baltimore MD: The Johns Hopkins University Press. 2nd edition.
6. Ohtake, Y., Belyaev, A., & Seidel, H.-P. (2003). A multi-scale approach to 3d scattered data interpolation with compactly supported basis functions. Proc. SMI 2003 (p. 292). IEEE Computer Society.
7. Ohtake, Y., Belyaev, A., & Seidel, H.-P. (2004). Ridge-valley lines on meshes via implicit surface fitting. ACM Trans. Graph., 23, 609-612.
8. Schölkopf, B., Giesen, J., & Spalinger, S. (2005). Kernel methods for implicit surface modeling. In L. K. Saul, Y. Weiss and L. Bottou (Eds.), Advances in neural information processing systems 17. Cambridge, MA: MIT Press.
9. Schölkopf, B., & Smola, A. J. (2002). Learning with kernels: Support vector machines, regularization, optimization, and beyond. Cambridge MA: MIT Press.
10. Shen, C., O'Brien, J. F., & Shewchuk, J. R. (2004). Interpolating and approximating implicit surfaces from polygon soup. ACM SIGGRAPH 2004. Los Angeles, California: ACM Press.
11. Sleijpen, G. L. G., & Van der Vorst, H. A. (1996). A Jacobi-Davidson iteration method for linear eigenvalue problems. SIAM Journal on Matrix Analysis and Applications, 17, 401-425.
12. Smola, A., & Schölkopf, B. (1997). On a kernel-based method for pattern recognition, regression, approximation and operator inversion. Algorithmica, 22, 211-231.
13. Vapnik, V. (1998). Statistical learning theory. New York: John Wiley and Sons, inc.
14. Walder, C., Lovell, B., & Kootsookos, P. (2003). Algebraic curve fitting support vector machines. In Dicta 2003, 693-702. Sydney.