DocumentCode
1862803
Title
Scattered Points Denoising of TC-Bézier Surface Fitting
Author
Liu, Xumin ; Xu, Jing ; Xu, Weixiang ; Guan, Yong
Author_Institution
Sch. of Inf. Eng., Capital Normal Univ., Beijing, China
fYear
2010
fDate
9-10 Jan. 2010
Firstpage
371
Lastpage
374
Abstract
Fit TC-Bezier surface with radial base function neural network, and build a radial base function network model which is suitable for surface reconstruction. A method is proposed in this paper, which suggests how to denoise and reconstructing free surface with radial base function neural network. This method simulates the internal relation between the points on surface using the study and training of scattered points made by neural cell. The result of simulation experiment shows that this model has strong capability of fitting surface, keeping the topological characteristic of the original points basically, and it also has function of smoothing noise. Besides, the speed of studying is fast and it can get surface of good fairness.
Keywords
radial basis function networks; surface fitting; TC-Bezier surface fitting; fitting surface; free surface; neural cell; radial base function network model; radial base function neural network; scattered points denoising; smoothing noise; surface reconstruction; topological characteristics; Artificial neural networks; Backpropagation algorithms; Feedforward neural networks; Multi-layer neural network; Neural networks; Noise reduction; Scattering; Smoothing methods; Surface fitting; Surface reconstruction; Scattered Points Denoising; Surface Fitting; radial base function neural network;
fLanguage
English
Publisher
ieee
Conference_Titel
Knowledge Discovery and Data Mining, 2010. WKDD '10. Third International Conference on
Conference_Location
Phuket
Print_ISBN
978-1-4244-5397-9
Electronic_ISBN
978-1-4244-5398-6
Type
conf
DOI
10.1109/WKDD.2010.91
Filename
5432585
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