Open AccessPoint Set Denoising Using Bootstrap-Based Radial Basis Function
Author(s) -
Khang Jie Liew,
Ahmad Ramli,
Ahmad Abd. Majid
Publication year - 2016
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0156724
Subject(s) - smoothing , thin plate spline , smoothing spline , spline (mechanical) , radial basis function , noise reduction , basis (linear algebra) , algorithm , point (geometry) , basis function , computer science , set (abstract data type) , function (biology) , mathematics , mathematical optimization , pattern recognition (psychology) , artificial intelligence , statistics , mathematical analysis , spline interpolation , geometry , artificial neural network , structural engineering , evolutionary biology , engineering , bilinear interpolation , biology , programming language
This paper examines the application of a bootstrap test error estimation of radial basis functions, specifically thin-plate spline fitting, in surface smoothing. The presence of noisy data is a common issue of the point set model that is generated from 3D scanning devices, and hence, point set denoising is one of the main concerns in point set modelling. Bootstrap test error estimation, which is applied when searching for the smoothing parameters of radial basis functions, is revisited. The main contribution of this paper is a smoothing algorithm that relies on a bootstrap-based radial basis function. The proposed method incorporates a k -nearest neighbour search and then projects the point set to the approximated thin-plate spline surface. Therefore, the denoising process is achieved, and the features are well preserved. A comparison of the proposed method with other smoothing methods is also carried out in this study.