Title :
SO dynamic deformation for building of 3-D models
Author :
Chen, Sei-Wang ; Stockman, George C. ; Chang, Kuo-En
Author_Institution :
Dept. of Inf. & Comput. Educ., Nat. Taiwan Normal Univ., Taipei, Taiwan
fDate :
3/1/1996 12:00:00 AM
Abstract :
Three-dimensional (3D) modeling based on an ensemble of multilayer self-organizing (SO) neural networks is described. Our objective for 3D modeling is to construct a representation of a 3D object shape from sensed surface points acquired from the object. Current modeling techniques can be classified into two categories: the static and the dynamic approaches, where the former grounded in computational geometry, and the latter rooted in the mechanics of elastic materials. In this paper, a neural-based dynamic modeling approach is presented. The method used is proved to converge and experimental results are shown which support its applicability to real problems
Keywords :
computational geometry; computer vision; feedforward neural nets; image representation; self-organising feature maps; solid modelling; stereo image processing; 3D modeling; 3D object shape representation; computational geometry; dynamic deformation; dynamic modeling; elastic materials; machine vision; multilayer self-organizing neural networks; surface points; Buildings; Computational geometry; Deformable models; Equations; Multi-layer neural network; Neural networks; Neurons; Prototypes; Shape; Solid modeling;
Journal_Title :
Neural Networks, IEEE Transactions on
