Segmentation of Flow Fields using Pattern Matching

Abstract

Due to the amount of data nowadays, automatic detection, classification and visualization of features is necessary for a thorough inspection of flow data sets. Pattern matching using vector valued templates has already been applied successfully for the detection of features. In this paper, the approach is extended to automatically compute feature based segmentations of flow data sets. Different problems of the segmentation like the influence of thresholds, overlapping features, and classification errors are discussed. Visualizations of the segmentation display important structures of the flow and highlight the interesting features. The segmentation algorithm presented in this paper is applicable to 2D and 3D vector fields as well as to time-dependent data.