Winding angle assisted particle tracing in distribution-based vector field

Abstract

Distribution models are widely used for data reduction applications. The Gaussian mixture model (GMM) is a powerful tool to capture multiple-peak distributions. For distribution-based vector field datasets represented by GMM, there are still loss of information which sometimes causes too much error when performing flow line tracing tasks. As a compensation, we analyze the vector transition pattern between consecutive vector directions. The vector transition is depicted by distributions of winding angles. When performing streamline and pathline tracing, we utilize the winding angle to estimate a conditional distribution of local vectors, using the Bayes Theorem. The conditional distribution can be used for both Monte Carlo flow line tracing, and single flow line tracing. We applied our distribution model on data reduction applications, and demonstrated that improved flow line tracing quality was achieved.