A Sampling Strategy Based on B-wavelets Decomposition

Luca Pagani; Paul J. Scott

doi:10.1016/j.procir.2016.01.028

A Sampling Strategy Based on B-wavelets Decomposition

Luca Pagani, Paul J. Scott

Research output: Contribution to journal › Conference article › peer-review

8 Citations (Scopus)

Abstract

Finding optimal sampling strategy is a central problem in surface reconstruction. In this paper a sampling strategy on different scales of a free-form surface is proposed. Non-Uniform Rational B-spline (NURBS) surfaces are used to represent the nominal shape; the decomposition is performed through the orthogonal B-wavelet basis. A sampling based on each level of the shape decomposition is then proposed to detect the points of interest that reflect the variations between different levels. The number of the samples is selected according to the complexity of the shape, which is represented by the number of the internal knots.

Original language	English
Pages (from-to)	29-34
Number of pages	6
Journal	Procedia CIRP
Volume	43
Early online date	9 May 2016
DOIs	https://doi.org/10.1016/j.procir.2016.01.028
Publication status	Published - 9 May 2016
Event	14th CIRP Conference on Computer Aided Tolerancing - Gothenburg, Sweden Duration: 18 May 2016 → 20 May 2016 Conference number: 14

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10.1016/j.procir.2016.01.028Licence: CC BY-NC-ND

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title = "A Sampling Strategy Based on B-wavelets Decomposition",

abstract = "Finding optimal sampling strategy is a central problem in surface reconstruction. In this paper a sampling strategy on different scales of a free-form surface is proposed. Non-Uniform Rational B-spline (NURBS) surfaces are used to represent the nominal shape; the decomposition is performed through the orthogonal B-wavelet basis. A sampling based on each level of the shape decomposition is then proposed to detect the points of interest that reflect the variations between different levels. The number of the samples is selected according to the complexity of the shape, which is represented by the number of the internal knots.",

keywords = "B-splines, B-wavelets, Curve Reconstruction, Decomposition, Sampling, Surface Reconstruction",

author = "Luca Pagani and Scott, {Paul J.}",

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language = "English",

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T1 - A Sampling Strategy Based on B-wavelets Decomposition

AU - Pagani, Luca

AU - Scott, Paul J.

N1 - Conference code: 14

PY - 2016/5/9

Y1 - 2016/5/9

N2 - Finding optimal sampling strategy is a central problem in surface reconstruction. In this paper a sampling strategy on different scales of a free-form surface is proposed. Non-Uniform Rational B-spline (NURBS) surfaces are used to represent the nominal shape; the decomposition is performed through the orthogonal B-wavelet basis. A sampling based on each level of the shape decomposition is then proposed to detect the points of interest that reflect the variations between different levels. The number of the samples is selected according to the complexity of the shape, which is represented by the number of the internal knots.

AB - Finding optimal sampling strategy is a central problem in surface reconstruction. In this paper a sampling strategy on different scales of a free-form surface is proposed. Non-Uniform Rational B-spline (NURBS) surfaces are used to represent the nominal shape; the decomposition is performed through the orthogonal B-wavelet basis. A sampling based on each level of the shape decomposition is then proposed to detect the points of interest that reflect the variations between different levels. The number of the samples is selected according to the complexity of the shape, which is represented by the number of the internal knots.

KW - B-splines

KW - B-wavelets

KW - Curve Reconstruction

KW - Decomposition

KW - Sampling

KW - Surface Reconstruction

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U2 - 10.1016/j.procir.2016.01.028

DO - 10.1016/j.procir.2016.01.028

M3 - Conference article

AN - SCOPUS:84978512534

VL - 43

SP - 29

EP - 34

JO - Procedia CIRP

JF - Procedia CIRP

SN - 2212-8271

T2 - 14th CIRP Conference on Computer Aided Tolerancing

Y2 - 18 May 2016 through 20 May 2016

ER -

A Sampling Strategy Based on B-wavelets Decomposition

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