FROM EDGELS TO PARAMETRIC CURVES

In many applications more than one curve type is needed to explain the edge data reasonably well. In this paper we present a robust algorithm which is designed to work concurrently with different curve types where each curve type selects its own domain of applicability. The major components of our algorithm are: a) A data-driven exploration of the edge data which produces estimates of possible curves. b) A selection which regards the curves produced by the exploration as hypotheses and decides which of them are needed to explain the edge data. c) A robust extension of a least squares algorithm which is initialized with the selected curves. We demonstrate with examples that our algorithm is robust and produces highly accurate results on both dense and sparse edge data.