Visualization of Noh mask curvature features

Abstract

We present a method of stable extraction of curvature features from a scattered data obtained by measuring a Noh mask shape. First, since the Noh mask shape can be represented as the graph of a function, we obtain a height function representation of the data. Then, we smooth the height function data by a Gaussian filter and by an iterative nonlinear filter. Finally we compute curvature features using finite-difference approximations. We test our approach detecting convex/concave, saddle, cylindrical, and plane regions on the Noh mask scattered data.