Attribute-Based Feature Tracking

Abstract

Visualization of time-dependent data is an enormous task because of the vast amount of data involved. However, most of the time the scientist is mainly interested in the evolution of certain features. Therefore, it suffices to show the evolution of these features. The task of the visualization system is to extract the features from all frames, to track the features, i.e. to determine the correspondences between features in successive frames, and finally to visualize the tracking results. This paper describes a tracking system that uses feature data to track the features and to determine their evolution in time. The feature data consists of basic attributes such as position, size, and mass. For each set of attributes a number of correspondence functions can be tested which results in a correspondence factor. This factor makes it possible to quantify the goodness of the match between two features in successive time frames. Since the algorithm uses only the feature data instead of the grid data, it is feasible to perform an extensive multi-pass search for continuing paths.