Adaptive Evolution of 3D Curves for Quality Control

Abstract

In the area of quality control by vision, the reconstruction of 3D curves is a convenient tool to detect and quantify possible anomalies. Whereas other methods exist that allow us to describe surface elements, the contour approach will prove to be useful to reconstruct the object close to discontinuities, such as holes or edges. We present an algorithm for the reconstruction of 3D parametric curves, based on a fixed complexity model, embedded in an iterative framework of control point insertion. The successive increase of degrees of freedom provides for a good precision while avoiding to over-parameterize the model. The curve is reconstructed by adapting the projections of a 3D NURBS snake to the observed curves in a multi-view setting. The sampling of the curve is adjusted as a function of the local visibility in the different views. The optimization of the curve is performed with respect to the control points using an gradient-based energy minimization method, whereas the insertion procedure relies on the computation of the distance from the curve to the image edges.