Surface Approximation using Growing Self-Organizing Nets and Gradient Information
In this paper we show how to improve the performance of two self-organizing neural networks used to approximate the shape of a 2D or 3D object by incorporating gradient information in the adaptation stage. The methods are based on the growing versions of the Kohonen's map and the neural gas net...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2007-01-01
|
| Series: | Applied Bionics and Biomechanics |
| Online Access: | http://dx.doi.org/10.1080/11762320701797745 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832550411812208640 |
|---|---|
| author | Jorge Rivera-Rovelo Eduardo Bayro-Corrochano |
| author_facet | Jorge Rivera-Rovelo Eduardo Bayro-Corrochano |
| author_sort | Jorge Rivera-Rovelo |
| collection | DOAJ |
| description | In this paper we show how to improve the performance of two self-organizing neural networks used to approximate the shape of a 2D or 3D object by incorporating gradient information in the adaptation stage. The methods are based on the growing versions of the Kohonen's map and the neural gas network. Also, we show that in the adaptation stage the network utilizes efficient transformations, expressed as versors in the conformal geometric algebra framework, which build the shape of the object independent of its position in space (coordinate free). Our algorithms were tested with several images, including medical images (CT and MR images). We include also some examples for the case of 3D surface estimation. |
| format | Article |
| id | doaj-art-41a759c1669144e09e3a6c1899a16562 |
| institution | Kabale University |
| issn | 1176-2322 1754-2103 |
| language | English |
| publishDate | 2007-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Bionics and Biomechanics |
| spelling | doaj-art-41a759c1669144e09e3a6c1899a165622025-02-03T06:06:51ZengWileyApplied Bionics and Biomechanics1176-23221754-21032007-01-014312513610.1080/11762320701797745Surface Approximation using Growing Self-Organizing Nets and Gradient InformationJorge Rivera-Rovelo0Eduardo Bayro-Corrochano1Department of Electrical Engineering and Computer Sciences, CINVESTA V del IPN, Unidad Guadalajara, Av. Científica 1145, El Bajío, Zapopan, Jalisco, 45010, MexicoDepartment of Electrical Engineering and Computer Sciences, CINVESTA V del IPN, Unidad Guadalajara, Av. Científica 1145, El Bajío, Zapopan, Jalisco, 45010, MexicoIn this paper we show how to improve the performance of two self-organizing neural networks used to approximate the shape of a 2D or 3D object by incorporating gradient information in the adaptation stage. The methods are based on the growing versions of the Kohonen's map and the neural gas network. Also, we show that in the adaptation stage the network utilizes efficient transformations, expressed as versors in the conformal geometric algebra framework, which build the shape of the object independent of its position in space (coordinate free). Our algorithms were tested with several images, including medical images (CT and MR images). We include also some examples for the case of 3D surface estimation.http://dx.doi.org/10.1080/11762320701797745 |
| spellingShingle | Jorge Rivera-Rovelo Eduardo Bayro-Corrochano Surface Approximation using Growing Self-Organizing Nets and Gradient Information Applied Bionics and Biomechanics |
| title | Surface Approximation using Growing Self-Organizing Nets and Gradient Information |
| title_full | Surface Approximation using Growing Self-Organizing Nets and Gradient Information |
| title_fullStr | Surface Approximation using Growing Self-Organizing Nets and Gradient Information |
| title_full_unstemmed | Surface Approximation using Growing Self-Organizing Nets and Gradient Information |
| title_short | Surface Approximation using Growing Self-Organizing Nets and Gradient Information |
| title_sort | surface approximation using growing self organizing nets and gradient information |
| url | http://dx.doi.org/10.1080/11762320701797745 |
| work_keys_str_mv | AT jorgeriverarovelo surfaceapproximationusinggrowingselforganizingnetsandgradientinformation AT eduardobayrocorrochano surfaceapproximationusinggrowingselforganizingnetsandgradientinformation |