@Article{saikia17b,
author = {H.~Saikia and T.~Weinkauf},
title = {Global Feature Tracking and Similarity Estimation in Time-Dependent Scalar Fields},
journal = {Computer Graphics Forum (Proc. EuroVis)},
year = {2017},
volume = {36},
number = {3},
pages = {1--11},
month = {June},
abstract = {We present an algorithm for tracking regions in time-dependent scalar
fields that uses global knowledge from all time steps for determining
the tracks. The regions are defined using merge trees, thereby representing
a hierarchical segmentation of the data in each time step. The similarity
of regions of two consecutive time steps is measured using their
volumetric overlap and a histogram difference. The main ingredient
of our method is a directed acyclic graph that records all relevant
similarity information as follows: the regions of all time steps
are the nodes of the graph, the edges represent possible short feature
tracks between consecutive time steps, and the edge weights are given
by the similarity of the connected regions. We compute a feature
track as the global solution of a shortest path problem in the graph.
We use these results to steer the - to the best of our knowledge
- first algorithm for spatio-temporal feature similarity estimation.
Our algorithm works for 2D and 3D time-dependent scalar fields. We
compare our results to previous work, showcase its robustness to
noise, and exemplify its utility using several real-world data sets.},
url = {http://tinoweinkauf.net/publications/abssaikia17b.html},
}