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
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;
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
DOI :
10.1109/WKDD.2010.91
