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Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/1752

Title: Neural networks based recognition of 3D freeform surface from 2D sketch
Authors: Sun, G
Qin, SF
Wright, DK
Keywords: Artificial intelligence
Freeform surface recognition
Neural networks
Sketch design
Publication Date: 2005
Publisher: IEEE
Citation: IEEE EUROCON2005 “Computer As a Tool”, Belgrade, Serbia & Montenegro, 22-24,2005. pp.1378-1381.
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.
URI: http://bura.brunel.ac.uk/handle/2438/1752
DOI: http://dx.doi.org/10.1109/EURCON.2005.1630217
ISBN: 1-4244-0049-X
Appears in Collections:Design
Dept of Design Research Papers

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