Scheduled System Maintenance:
Some services will be unavailable Sunday, March 29th through Monday, March 30th. We apologize for the inconvenience.
By Topic

Neural network architecture for 3D object representation

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Cretu, A.-M. ; Sch. of Inf. Technol. & Eng., Ottawa Univ., Ont., Canada ; Petriu, E.M. ; Patry, G.G.

The paper discusses a neural network architecture for 3D object modeling. A multi-layered feedforward structure having as inputs the 3D-coordinates of the object points is employed to model the object space. Cascaded with a transformation neural network module, the proposed architecture can be used to generate and train 3D objects, perform transformations, set operations and object morphing. A possible application for object recognition is also presented.

Published in:

Haptic, Audio and Visual Environments and Their Applications, 2003. HAVE 2003. Proceedings. The 2nd IEEE Internatioal Workshop on

Date of Conference:

20-21 Sept. 2003