Deformable Object Modelling Through Cellular Neural Network

Abstract

This paper presents a new methodology for thedeformable object modelling by drawing an analogybetween cellular neural network (CNN) and elasticdeformation. The potential energy stored in an elasticbody as a result of a deformation caused by an externalforce is propagated among mass points by the non-linearCNN activity. An improved autonomous CNN model isdeveloped for propagating the energy generated by theexternal force on the object surface in the naturalmanner of heat conduction. A heat flux based method ispresented to derive the internal forces from the potentialenergy distribution established by the CNN. Theproposed methodology models non-linear materials withnon-linear CNN rather than geometric non-linearity inthe most existing deformation methods. It can not onlydeal with large-range deformations due to the localconnectivity of cells and the CNN dynamics, but it canalso accommodate both isotropic and anisotropicmaterials by simply modifying conductivity constants.Examples are presented tThis paper presents a new methodology for the deformable object modelling 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 non-linear CNN activity. An improved autonomous CNN model is developed for propagating the energy generated by the external force on the object surface in the natural manner of heat conduction. A heat flux based method is presented to derive the internal forces from the potential energy distribution established by the CNN. The proposed methodology models non-linear materials with non-linear CNN rather than geometric non-linearity in the most existing deformation methods. It can not only deal with large-range deformations due to the local connectivity of cells and the CNN dynamics, but it can also accommodate both isotropic and anisotropic materials by simply modifying conductivity constants. Examples are presented to demonstrate the efficacy of the proposed methodology.