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Investigating the Segmentation of Freeform Triangulated Surfaces Using a Self-Organizing Map

[+] Author Affiliations
Alexander D. MacLennan, Geoff West

Curtin University of Technology, Perth, WA, Australia

Michael Cardew-Hall

Australian National University, Canberra, ACT, Australia

Paper No. DETC2006-99472, pp. 51-59; 9 pages
doi:10.1115/DETC2006-99472
From:
  • ASME 2006 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 3: 26th Computers and Information in Engineering Conference
  • Philadelphia, Pennsylvania, USA, September 10–13, 2006
  • Conference Sponsors: Design Engineering Division and Computers and Information in Engineering Division
  • ISBN: 0-7918-4257-8 | eISBN: 0-7918-3784-X
  • Copyright © 2006 by ASME

abstract

Freeform surfaces can be used to describe manufactured objects. These surfaces can be represented as point clouds, triangulated surfaces and range images. Before these objects can be analysed in any way they need to be broken down into their constituent parts. Using this description stamped parts can be indexed and retrieved to assist in determining how to manufacture a part that has similar properties. One means of performing this task is to segment the object based upon its surface properties. Curvature can be used to describe the behaviour of a surface. In order to use these metrics a single Self-Organizing Map is used to automatically categorise surface into regions of similar curvature. The SOM is first trained using a small number of simple shapes and curvature metrics. It is then used to segment an object that is a mixture of free form surfaces and planes. The combination of these metrics, shapes and the use of a SOM allows for the representation of many types of surfaces. The shapes and curvature metrics used to train the model determine how sensitive it is to different surface descriptions. This technique is successfully applied to a complex object that combines free form surfaces and planar surfaces using robust discrete curvature metrics.

Copyright © 2006 by ASME

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