Learning Probabilistic Models of Contours

We present a methodology for learning spline-based probabilistic models for sets of contours, proposing a new Monte Carlo variant of the EM algorithm to estimate the parameters of a family of distributions defined over the set of spline functions (with fixed complexity). The proposed model effectively captures the major morphological properties of the observed set of contours as well as its variability, as the simulation results presented demonstrate.