Reconstruction of anatomical shapes from scattered data using deformable Bezier surfaces

George K. Knopf; Archana Sangole

doi:10.1117/12.444106

4 October 2001 Reconstruction of anatomical shapes from scattered data using deformable Bezier surfaces

George K. Knopf, Archana Sangole

Proceedings Volume 4564, Optomechatronic Systems II; (2001) https://doi.org/10.1117/12.444106
Event: Intelligent Systems and Advanced Manufacturing, 2001, Boston, MA, United States

Abstract

An important task in reverse engineering and computer-aided- design applications is to create a mathematical model of surface geometry based on coordinate measurements. A two- step techniques that fits parametric surfaces to partial or whole human body measurements for free-form surface reconstruction is described in this paper. The first step of the proposed technique employs a self-organizing feature map to adaptively parameterize non-uniformly spaced coordinate points. The second step uses a Bernstein Basis Function (BBF) network to fit a deformable Bezier surface to the parameterized data. Once the adaption phase is compete, the weights of the BBF network can be utilized by a variety of commercially available geometric modeling and CAD/CAM packages for shape reconstruction. An experimental study is presented to demonstrate the effectiveness of the BBF network for generating smooth Bezier surfaces of complex anatomical shapes.

Citation Download Citation

George K. Knopf and Archana Sangole "Reconstruction of anatomical shapes from scattered data using deformable Bezier surfaces", Proc. SPIE 4564, Optomechatronic Systems II, (4 October 2001); https://doi.org/10.1117/12.444106

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