A deformable model with cellular neural network
Yong Zhong, Bijan Shirinzadeh, Gürsel Alıcı, Julian Anderson Smith · Research Online (University of Wollongong) · 2005
This paper presents a new methodology for deformable models by drawing an analogy between cellular neural network (CNN) and elastic deformation. The potential energy stored in an elastic body as a result of a deformation caused by an external force is propagated among mass points by the local connectivity of cells and the CNN dynamics. An improved CNN model is developed for propagating the energy generated by the external force on the object surface. A method is presented to derive the internal forces from the potential energy distribution established by the CNN. The methodology proposed in this paper can not only deal with large-range deformation, but it can also accommodate both isotropic and anisotropic materials by simply modifying capacitors of cells. Examples are presented to demonstrate the efficacy of the proposed methodology.