Publication Date:
2022-07-19
Description:
We propose a combinatorial algorithm to track critical points of 2D
time-dependent scalar fields. Existing tracking algorithms such as Feature
Flow Fields apply numerical schemes utilizing derivatives of the data,
which makes them prone to noise and involve a large number of computational
parameters. In contrast, our method is robust against noise
since it does not require derivatives, interpolation, and numerical integration.
Furthermore, we propose an importance measure that combines the
spatial persistence of a critical point with its temporal evolution. This
leads to a time-aware feature hierarchy, which allows us to discriminate
important from spurious features. Our method requires only a single,
easy-to-tune computational parameter and is naturally formulated in an
out-of-core fashion, which enables the analysis of large data sets. We apply
our method to a number of data sets and compare it to the stabilized
continuous Feature Flow Field tracking algorithm.
Language:
English
Type:
reportzib
,
doc-type:preprint
Format:
application/pdf
