Reconstruction of anatomical shapes from scattered data using deformable Bezier surfaces

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.