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Proceedings Paper

Neural network approach to reconstructing complex Bezier surfaces
Author(s): George K. Knopf
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Paper Abstract

Many computer vision, computer graphics and computer-aided design applications require mathematical models of existing objects to be generated from measured surface points. The geometric model of a complex surface can be created by joining numerous low-order bi-parametric surface patches, and adjusting the control parameters such that the constituent patches meet seamlessly at their common boundaries. In this paper a two-layer neural network, called the Bernstein Basis Function (BBF) network, is proposed for computing the control points of a defining polygon net that will generate a Bezier surface that 'best' approximates the data in a local segmented region. Complex surfaces are reconstructed by using several simultaneously updated networks, each corresponding to a separate surface patch. A smooth transition between the adjacent Bezier surface patches is achieved by imposing additional positional and tangential continuity constraints on the weights during the adaptation process. This method is illustrated by adaptively stitching together several patches to form a smooth surface.

Paper Details

Date Published: 26 September 1997
PDF: 10 pages
Proc. SPIE 3208, Intelligent Robots and Computer Vision XVI: Algorithms, Techniques, Active Vision, and Materials Handling, (26 September 1997); doi: 10.1117/12.290324
Show Author Affiliations
George K. Knopf, Univ. of Western Ontario (Canada)


Published in SPIE Proceedings Vol. 3208:
Intelligent Robots and Computer Vision XVI: Algorithms, Techniques, Active Vision, and Materials Handling
David P. Casasent, Editor(s)

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