An Improved Algorithm for Surface Fitting Based on B Spline Function

Wang, Zhan; Yu, Jun; Su, Bida; Xie, Xiaodan

doi:10.1109/icic.2011.35

Cited by 4 publications

(2 citation statements)

References 8 publications

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“…If the B-spline neural network structure in figure 1 has m variables [5], the main network steps from input to output is: 1. The calculation of the B-spline basic function [6]. 2.…”

Section: The Fitting Of the B-spline Neural Networkmentioning

confidence: 99%

A BP Training Fitting Method about Multivariate BRDF Based on B-spline Function

Zhan

2012

2012 Fifth International Conference on Information and Computing Science

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For a large number of experimental data, the BRDF surface fitting method based on B-spline function and least squares theory, the ill-conditioned normal equations, the low accuracy of the results and long CPU time may be appeared. Thereby, in this paper by using the BP learning method, combined with the training process of L-M algorithm, an improved method is presented. And the method is applied to the BDRF data processing, the result shows that this method is effective, and has greatly improved in accuracy and reduced running time.

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“…If the B-spline neural network structure in figure 1 has m variables [5], the main network steps from input to output is: 1. The calculation of the B-spline basic function [6]. 2.…”

Section: The Fitting Of the B-spline Neural Networkmentioning

confidence: 99%

A BP Training Fitting Method about Multivariate BRDF Based on B-spline Function

Zhan

2012

2012 Fifth International Conference on Information and Computing Science

Self Cite

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“…Various methods can be found in a survey on scattered data interpolation [38]. For approximation methods, Bsplines have a solid mathematical foundation and have been used in many literatures, such as [34,51] etc. Wavelet frames have also been used to reconstruct implicit surfaces from unorganized point sets in R 3 [16].…”

Section: Introductionmentioning

confidence: 99%

Sparse approximate solution of fitting surface to scattered points by MLASSO model

Hao

Wang

2017

Sci. China Math.

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The goal of this paper is to achieve a computational model and corresponding efficient algorithm for obtaining a sparse representation of the fitting surface to the given scattered data. The basic idea of the model is to utilize the principal shift invariant (PSI) space and the l 1 norm minimization. In order to obtain different sparsity of the approximation solution, the problem is represented as a multilevel LASSO (MLASSO) model with different regularization parameters. The MLASSO model can be solved efficiently by the alternating direction method of multipliers. Numerical experiments indicate that compared to the AGLASSO model and the basic MBA algorithm in [35], the MLASSO model can provide an acceptable compromise between the minimization of the data mismatch term and the sparsity of the solution. Moreover, the solution by the MLASSO model can reflect the regions of the underlying surface where high gradients occur.

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Sparse approximation of fitting surface by elastic net

Hao

2017

Comp. Appl. Math.

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An Improved Algorithm for Surface Fitting Based on B Spline Function

Cited by 4 publications

References 8 publications

A BP Training Fitting Method about Multivariate BRDF Based on B-spline Function

A BP Training Fitting Method about Multivariate BRDF Based on B-spline Function

Sparse approximate solution of fitting surface to scattered points by MLASSO model

Sparse approximation of fitting surface by elastic net

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