A cellular neural network based deformable model

This paper presents a new methodology for the deformation of soft objects by drawing an analogy between cellular neural network and elastic deformation. An improved cellular neural network model is developed to simulate the deformation of soft objects. The cellular neural network model incorporates...

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Bibliographic Details
Main Authors: Zhong, Yongmin, Shirinzadeh, B., Smith, J.
Other Authors: D Coutellier
Format: Conference Paper
Published: Springer Verlag 2006
Subjects:
Online Access:http://hdl.handle.net/20.500.11937/17214
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author Zhong, Yongmin
Shirinzadeh, B.
Smith, J.
author2 D Coutellier
author_facet D Coutellier
Zhong, Yongmin
Shirinzadeh, B.
Smith, J.
author_sort Zhong, Yongmin
building Curtin Institutional Repository
collection Online Access
description This paper presents a new methodology for the deformation of soft objects by drawing an analogy between cellular neural network and elastic deformation. An improved cellular neural network model is developed to simulate the deformation of soft objects. The cellular neural network model incorporates the internal elastic forces and the external force to model the deformation dynamics. A finite volume based method is presented to derive the discrete differential operators over irregular nets for obtaining the internal elastic forces. The finite volume method enforces the conservation of energy in a discrete sense and provides an intuitively geometric discretization rather than an interpolating function in the finite element method to calculate internal elastic forces. The proposed methodology not only models the deformation dynamics in continuum mechanics, but it also simplifies the complex deformation problem by simple setting the cellular neural network templates.
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format Conference Paper
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institution Curtin University Malaysia
institution_category Local University
last_indexed 2025-11-14T07:20:23Z
publishDate 2006
publisher Springer Verlag
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spelling curtin-20.500.11937-172142022-10-27T07:54:09Z A cellular neural network based deformable model Zhong, Yongmin Shirinzadeh, B. Smith, J. D Coutellier X Fischer cellular - neural network finite volume method and analogy systems deformation deformable object This paper presents a new methodology for the deformation of soft objects by drawing an analogy between cellular neural network and elastic deformation. An improved cellular neural network model is developed to simulate the deformation of soft objects. The cellular neural network model incorporates the internal elastic forces and the external force to model the deformation dynamics. A finite volume based method is presented to derive the discrete differential operators over irregular nets for obtaining the internal elastic forces. The finite volume method enforces the conservation of energy in a discrete sense and provides an intuitively geometric discretization rather than an interpolating function in the finite element method to calculate internal elastic forces. The proposed methodology not only models the deformation dynamics in continuum mechanics, but it also simplifies the complex deformation problem by simple setting the cellular neural network templates. 2006 Conference Paper http://hdl.handle.net/20.500.11937/17214 Springer Verlag restricted
spellingShingle cellular - neural network
finite volume method and analogy systems
deformation
deformable object
Zhong, Yongmin
Shirinzadeh, B.
Smith, J.
A cellular neural network based deformable model
title A cellular neural network based deformable model
title_full A cellular neural network based deformable model
title_fullStr A cellular neural network based deformable model
title_full_unstemmed A cellular neural network based deformable model
title_short A cellular neural network based deformable model
title_sort cellular neural network based deformable model
topic cellular - neural network
finite volume method and analogy systems
deformation
deformable object
url http://hdl.handle.net/20.500.11937/17214