Refining Hierarchical Radial Basis Function Networks
Stefano Ferrari, Francis Be, N. Alberto Borghese, Vincenzo Piuri · 2007
The hierarchical radial basis function (HRBF) Network is a neural model that proved its ability in surface reconstruction problem. The algebraic error is used to drive the HRBF configuration procedure and for evaluating the reconstruction ability of the network. While for function approximation the algebraic distance is the appropriate error metric, for computer graphics applications, such as model reconstruction by 3D scanning, the geometric distance is a more suitable error metric. In this paper, we propose a modified HRBF model which makes use of the geometric error as a measure of the reconstruction accuracy.