TY - GEN A1 - Reininghaus, Jan A1 - Kasten, Jens A1 - Weinkauf, Tino A1 - Hotz, Ingrid T1 - Combinatorial Feature Flow Fields: Tracking Critical Points in Discrete Scalar Fields N2 - 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. T3 - ZIB-Report - 11-02 KW - data analysis KW - feature detection KW - time-varying data KW - topology-based techniques Y1 - 2011 UR - https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/1215 UR - https://nbn-resolving.org/urn:nbn:de:0297-zib-12151 ER -