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dc.contributor.authorSun, G-
dc.contributor.authorQin, SF-
dc.contributor.authorWright, DK-
dc.identifier.citationIEEE EUROCON2005 “Computer As a Tool”, Belgrade, Serbia & Montenegro, 22-24,2005. pp.1378-1381.en
dc.description.abstractIn 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.en
dc.format.extent2669718 bytes-
dc.subjectArtificial intelligenceen
dc.subjectFreeform surface recognitionen
dc.subjectNeural networksen
dc.subjectSketch designen
dc.titleNeural networks based recognition of 3D freeform surface from 2D sketchen
dc.typeConference Paperen
Appears in Collections:Design
Dept of Design Research Papers

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