TY - CONF
T1 - Curve reconstruction from noisy samples
AU - Cheng, Siu Wing
AU - Funke, Stefan
AU - Golin, Mordecai
AU - Kumar, Piyush
AU - Poon, Sheung Hung
AU - Ramos, Edgar
N1 - Funding Information:
* Corresponding author. E-mail addresses: scheng@cs.ust.hk (S.-W. Cheng), funke@mpi-sb.mpg.de (S. Funke), golin@cs.ust.hk (M. Golin), piyush@ams.sunysb.edu (P. Kumar), hung@cs.ust.hk (S.-H. Poon), eramosn@cs.uiuc.edu (E. Ramos). 1 Research of S.-W. Cheng and S.-H. Poon are partly supported by Research Grant Council, Hong Kong, China (project no. HKUST 6190/02E and HKUST 6169/03E). Research of M. Golin is partly supported by Research Grant Council, Hong Kong, China (project no. HKUST 6082/01E and HKUST 6206/02E). 2 Partly supported by the IST Programme of the EU as a Shared-cost RTD (FET Open) Project under Contract No IST-2000-26473 (ECG—Effective Computational Geometry for Curves and Surfaces).
PY - 2003
Y1 - 2003
N2 - We present an algorithm to reconstruct a collection of disjoint smooth closed curves from n noisy samples. Our noise model assumes that the samples are obtained by first drawing points on the curves according to a locally uniform distribution followed by a uniform perturbation of each point in the normal direction with a magnitude smaller than the minimum local feature size. The reconstruction is faithful with a probability that approaches 1 as n increases. We expect that our approach can lead to provable algorithms under less restrictive noise models and for handling non-smooth features.
AB - We present an algorithm to reconstruct a collection of disjoint smooth closed curves from n noisy samples. Our noise model assumes that the samples are obtained by first drawing points on the curves according to a locally uniform distribution followed by a uniform perturbation of each point in the normal direction with a magnitude smaller than the minimum local feature size. The reconstruction is faithful with a probability that approaches 1 as n increases. We expect that our approach can lead to provable algorithms under less restrictive noise models and for handling non-smooth features.
KW - Curve reconstruction
KW - Probabilistic analysis
KW - Sampling
UR - http://www.scopus.com/inward/record.url?scp=0037700433&partnerID=8YFLogxK
U2 - 10.1145/777792.777838
DO - 10.1145/777792.777838
M3 - Paper
AN - SCOPUS:0037700433
SP - 302
EP - 311
T2 - Nineteenth Annual Symposium on Computational Geometry
Y2 - 8 June 2003 through 10 June 2003
ER -