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The term "bootstrap" originated in the 19th century as a metaphor for self-improvement or achieving something independently, without external assistance. This concept extends to statistical bootstrapping, a self-contained method for estimating population parameters through resampling, even though it can be computationally intensive. Developed by the American statistician Dr. Bradley Efron in 1979, bootstrapping provides a robust way to perform inference when the original sample size is...
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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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Point Set Denoising Using Bootstrap-Based Radial Basis Function.

Khang Jie Liew1, Ahmad Ramli1, Ahmad Abd Majid1

  • 1School of Mathematical Sciences, Universiti Sains Malaysia, 11800 Minden, Penang, Malaysia.

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|June 18, 2016
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Summary
This summary is machine-generated.

This study introduces a novel bootstrap-based radial basis function smoothing algorithm for effective point set denoising. The method preserves surface features while reducing noise in 3D scanned data.

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Area of Science:

  • Computer Vision
  • Geometric Modeling
  • Numerical Analysis

Background:

  • Noisy data is a significant challenge in 3D scanning and point set modeling.
  • Accurate surface smoothing and denoising are crucial for preserving geometric features.
  • Radial basis functions (RBFs) are effective for surface approximation but require careful parameter selection.

Purpose of the Study:

  • To develop and evaluate a novel smoothing algorithm for point set denoising using RBFs.
  • To improve the accuracy of surface smoothing by incorporating bootstrap test error estimation.
  • To preserve fine geometric details during the denoising process.

Main Methods:

  • Application of bootstrap test error estimation for selecting smoothing parameters of RBFs, specifically thin-plate splines.
  • Development of a smoothing algorithm utilizing a bootstrap-based RBF.
  • Integration of a k-nearest neighbour search for point set projection onto the approximated surface.

Main Results:

  • The proposed bootstrap-based RBF method effectively denoises point sets.
  • Geometric features of the surfaces are well-preserved after applying the smoothing algorithm.
  • The method demonstrates competitive or superior performance compared to existing smoothing techniques.

Conclusions:

  • Bootstrap test error estimation is a viable approach for optimizing RBF smoothing parameters.
  • The proposed algorithm offers an effective solution for point set denoising in 3D scanning applications.
  • This method successfully balances noise reduction with feature preservation.