Feature recognition in 3D surface models using self-organizing maps

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dc.contributor.advisor Prof. Z. Katz Dr. J.J. Naude en
dc.contributor.author Buhr, Richard Otto
dc.date.accessioned 2008-11-18T09:06:20Z
dc.date.available 2008-11-18T09:06:20Z
dc.date.issued 2008-11-18T09:06:20Z
dc.date.submitted 2003-06-01
dc.identifier.uri http://hdl.handle.net/10210/1729
dc.description M.Ing. en
dc.description.abstract This project investigates the use of Self-Organizing Maps (SOM) for feature recognition and analysis in 3D objects. Object data was generated to simulate data obtained from 3D scanning and trained using SOM. The trained data was analysed using speci cally developed software. The feature recognition and analysis process can be summarized as follows: a 3D object le is converted to a pure 3D data le, this data le is trained using the SOM algorithm after which the output is analyzed using a 3D object viewer and SOM data display. en
dc.language.iso en en
dc.subject Self-organizing maps en
dc.subject Neural networks (Computer science) en
dc.subject Computer vision en
dc.title Feature recognition in 3D surface models using self-organizing maps en
dc.type Thesis en

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