| id |
ecaade2014_149 |
| authors |
Matthias Standfest |
| year |
2014 |
| title |
Unsupervised Symmetric Polygon Mesh Mapping - The Dualism of Mesh Representation and Its Implementation for Many Layered Self-Organizing Map Architectures |
| source |
Thompson, Emine Mine (ed.), Fusion - Proceedings of the 32nd eCAADe Conference - Volume 1, Department of Architecture and Built Environment, Faculty of Engineering and Environment, Newcastle upon Tyne, England, UK, 10-12 September 2014, pp. 505-513 |
| doi |
https://doi.org/10.52842/conf.ecaade.2014.1.505
|
| wos |
WOS:000361384700050 |
| summary |
With this paper we present a fully automated semantic shape similarity detection based on N-rings with further potential for shape synthesis in a topological correct feature space. Therefore a way of symmetric encoding of geometry, optimized for the use as feature-vector in self-organizing maps, is introduced. Furthermore we present a modified kernel for the detection of the best matching unit in self-organizing maps especially designed for a data topology differing from the default predecessor/successor structure. Finally we provide the results of a conducted experiment clustering building blocks of an area in Zürich, Switzerland. |
| keywords |
Unsupervised machine learning; geometry clustering; self-organizing map; mesh synthesis; probabilistic modelling |
| series |
eCAADe |
| email |
|
| full text |
 file.pdf (539,963 bytes) |
| references |
Content-type: text/plain
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| last changed |
2022/06/07 07:58 |
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