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ARUP; ISBN: 978-0-9562121-5-3 - CMBBE 2012 - Cardiff University

ARUP; ISBN: 978-0-9562121-5-3 - CMBBE 2012 - Cardiff University

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CHRONOPHOTOGRAPHIC VISUALIZATION FOR TIME-DEPENDENT DATA SETS<br />

T. Fogal 1 and J. Krüger 2<br />

Fig. 1: Chronophotographic visualization of a bouncing ball. Successive timesteps are placed to<br />

the right of one another. As the ball hits the right wall and reverses direction, this appears as a<br />

compression of the spatial subdivision.<br />

1. ABSTRACT<br />

Conventional visualization tools for time-dependent data provide a user interface widget which<br />

allows the user to set which time step is currently active. However, such an interaction paradigm<br />

discourages qualitative comparison between timesteps. Various preprocessing techniques have<br />

been presented to enable analysis of multiple timesteps, but the purely quantitative nature of such<br />

approaches leaves much to be desired. We propose using chronophotographic visualizations to<br />

enable simultaneous visualization of a large set of time steps. Using this technique, researchers<br />

can quickly obtain a broad understanding of how data changes over time, as movement is<br />

prominently displayed using chronophotographic images.<br />

2. INTRODUCTION AND RELATED WORK<br />

Growing data sizes present larger challenges to researchers in all fields. Sequences of threedimensional<br />

data tracking the mechanical response of organs or joints are now becoming<br />

commonplace. Effective analysis of such data could have an important impact for localized<br />

radiation treatments 1 , for example. However, despite numerous advantages, uptake of this<br />

dimension of data has been slow to permeate clinical practice.<br />

There has been a variety of previous work applicable to visualizing time-dependent data. van<br />

Wijk and van Liere introduce HyperSlice, a method for visualizing multi-dimensional data as a<br />

set of orthogonal two-dimensional slices 3 . Mascarenhas and Snoeyink provide a survey of<br />

isocontour visualization in the context of time dependent fields 4 . Bajaj et al. introduce Hypervolume<br />

visualization, a technique for visualizing data of many dimensions which could easily be<br />

applied to 4D data 5 . Jankun-Kelly and Ma investigate the creation of transfer functions for time-<br />

1 Student, Interactive Visualization and Data Analysis Group, Universität des Saarland,<br />

Saarbrücken, Germany.<br />

2 Professor, Interactive Visualization and Data Analysis Group, Universität des Saarlnad,<br />

Saarbrücken, Germany.

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