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Conference Paper

Kernel Methods for Implicit Surface Modeling

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons84193

Schölkopf,  B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Schölkopf, B., Giesen, J., & Spalinger, S. (2005). Kernel Methods for Implicit Surface Modeling. Advances in Neural Information Processing Systems, 1193-1200.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-D521-0
Abstract
We describe methods for computing an implicit model of a hypersurface that is given only by a finite sampling. The methods work by mapping the sample points into a reproducing kernel Hilbert space and then determining regions in terms of hyperplanes.