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Abstract:
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 synthetic data and data sets from computational
fluid dynamics and compare it to the stabilized continuous Feature Flow Field
tracking algorithm.
