Simultaneous Classification of Time-Varying Volume Data Based on the Time Histogram

Abstract

An important challenge in the application of direct volume rendering to time-varying data is the specification of transfer functions for all time steps. Very little research has been devoted to this problem, however. To address this issue we propose an approach which allows simultaneous classification of the entire time series. We explore options for transfer function specification that are based, either directly or indirectly, on the time histogram. Furthermore, we consider how to effectively provide feedback for interactive classification by exploring options for simultaneous rendering of the time series, again based on the time histogram. Finally, we apply this approach to several large time-varying data sets where we show that the important features at all times are captured with about the same effort it takes to classify one time step using conventional classification.