A deformable model with cellular neural network
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 connec...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Conference Paper |
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The Hong Kong Polytechnic University
2005
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/5856 |
| _version_ | 1848744912753786880 |
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| author | Zhong, Yongmin Shirinzadeh, B. Alici, G. Smith, J. |
| author2 | G Baciu |
| author_facet | G Baciu Zhong, Yongmin Shirinzadeh, B. Alici, G. Smith, J. |
| author_sort | Zhong, Yongmin |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T06:09:00Z |
| format | Conference Paper |
| id | curtin-20.500.11937-5856 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T06:09:00Z |
| publishDate | 2005 |
| publisher | The Hong Kong Polytechnic University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-58562022-10-11T07:42:50Z A deformable model with cellular neural network Zhong, Yongmin Shirinzadeh, B. Alici, G. Smith, J. G Baciu M C Lin R W H Lau D Thalmann analogous systems CNN deformable objects deformation 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. 2005 Conference Paper http://hdl.handle.net/20.500.11937/5856 The Hong Kong Polytechnic University restricted |
| spellingShingle | analogous systems CNN deformable objects deformation Zhong, Yongmin Shirinzadeh, B. Alici, G. Smith, J. A deformable model with cellular neural network |
| title | A deformable model with cellular neural network |
| title_full | A deformable model with cellular neural network |
| title_fullStr | A deformable model with cellular neural network |
| title_full_unstemmed | A deformable model with cellular neural network |
| title_short | A deformable model with cellular neural network |
| title_sort | deformable model with cellular neural network |
| topic | analogous systems CNN deformable objects deformation |
| url | http://hdl.handle.net/20.500.11937/5856 |