Neural Networks Based Recognition of 3D Freeform Surface from 2D Sketch

Author

Sun, Guangmin ; Qin, S.F. ; Wright, D.K.

Author_Institution

Dept. of Electron. Eng., Beijing Univ. of Technol.

Volume

2

fYear

2005

fDate

21-24 Nov. 2005

Firstpage

1378

Lastpage

1381

Abstract

In this paper, the back propagation (BP) network and radial basis function (RBF) neural network are employed to recognize and reconstruct 3D freeform surface from 2D freehand sketch. Some tests and comparison experiments have been made to evaluate the performance for the reconstruction of freeform surfaces of both networks using simulation data. The experimental results show that both BP and RBF based freeform surface reconstruction methods are feasible; and the RBF network performed better. The RBF average point error between the reconstructed 3D surface data and the desired 3D surface data is less than 0.05 over all our 75 test sample data

Keywords

backpropagation; computer graphics; image recognition; image reconstruction; radial basis function networks; surface fitting; surface reconstruction; 2D sketch; 3D freeform surface; artificial intelligence; back propagation network; freeform surface recognition; neural networks; radial basis function neural network; Artificial neural networks; Design engineering; Image reconstruction; Multi-layer neural network; Neural networks; Radial basis function networks; Reconstruction algorithms; Sun; Surface reconstruction; Testing; Artificial intelligence; Freeform surface recognition; Neural networks; Sketch design;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer as a Tool, 2005. EUROCON 2005.The International Conference on

Conference_Location

Belgrade

Print_ISBN

1-4244-0049-X

Type

conf

DOI

10.1109/EURCON.2005.1630217

Filename

1630217