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

perform better when there is movement within an object of interest, as opposed to the object of
interest itself moving.
3.2 Importance Functions
Fig. 3: Example mapping from timesteps to visualized result. Here, the function on the right maps
to the opacity of the associated timestep. Normal distributions are a natural way to visualize a
particular time point, as the slow ramp-up and ramp-down provides a useful mechanism to
discern where the peak is in the visualization.
In many instances, viewing the entire time sequence at one time can result in far too much
information being displayed at one time. The result can be difficult to interpret, and may even
imply an evolution of the data's features which did not actually occur. For this reason, it is
important to have a mechanism by which the user can choose which which timesteps are visible.
To this end, we propose the use of window functions which enable the user to choose how this
mapping works. These importance functions are multi-channel functions which detail the
emphasis of a particular timestep in the visualization. Large values indicate an emphatic
response, whereas zero indicates that the timestep should not contribute to the visualization. A
sample importance function is depicted in Figure 3, right. Here, time is represented linearly, with
earlier timesteps appearing to the left. The white line indicates the emphasis on the timestep in
that area. Users can click and drag to create any arbitrary function, and the visualization (Figure
3, left) is updated in real time. Right clicking allows the importance function to be translated
horizontally, allowing an easy way to quickly obtain a ‘movie’ depicting the evolution of the data
over time.
Timesteps can be selected individually for transfer function modification. Initially, all timesteps
utilize a simple smoothstep transfer function. When a user makes a change to a timestep’s
transfer function, the initial configuration shares that modification across the transfer functions
for all timesteps. However, users may unlock particular timesteps to define a custom transfer
function. This follows the practical reality of transfer functions for time-dependent data: it is
common for a single transfer function to be appropriate for a large number of timesteps, with a
few timesteps not following the general trend and requiring some minor modifications from the
mode.
An alternative formulation to is to consider the transfer function space to be 4 dimensional
instead of 3 dimensional. While this allows a more general specification by the user, we have