Neural network approach to reconstructing complex Bezier surfaces

George K. Knopf

doi:10.1117/12.290324

26 September 1997 Neural network approach to reconstructing complex Bezier surfaces

George K. Knopf

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Proceedings Volume 3208, Intelligent Robots and Computer Vision XVI: Algorithms, Techniques, Active Vision, and Materials Handling; (1997) https://doi.org/10.1117/12.290324
Event: Intelligent Systems and Advanced Manufacturing, 1997, Pittsburgh, PA, United States

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.

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George K. Knopf "Neural network approach to reconstructing complex Bezier surfaces", Proc. SPIE 3208, Intelligent Robots and Computer Vision XVI: Algorithms, Techniques, Active Vision, and Materials Handling, (26 September 1997); https://doi.org/10.1117/12.290324

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KEYWORDS

Neural networks

Computer graphics

Mathematical modeling

Computer aided design

Computer vision technology

Image segmentation

Machine vision

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