title: Multi-field Pattern Matching based on Sparse Feature Sampling.
abstract: We present an approach to pattern matching in 3D multi-field scalar data. Existing pattern matching algorithms work on single scalar or vector fields only, yet many numerical simulations output multi-field data where only a joint analysis of multiple fields describes the underlying phenomenon fully. Our method takes this into account by bundling information from multiple fields into the description of a pattern. First, we extract a sparse set of features for each 3D scalar field using the 3D SIFT algorithm (Scale-Invariant Feature Transform). This allows for a memory-saving description of prominent features in the data with invariance to translation, rotation, and scaling. Second, the user defines a pattern as a set of SIFT features in multiple fields by e.g. brushing a region of interest. Third, we locate and rank matching patterns in the entire data set. Experiments show that our algorithm is efficient in terms of required memory and computational efforts.
authors.forename: Zhongjie
authors.initials: Z
authors.lastname: Wang
authors.forename: Hans-Peter
authors.initials: HP
authors.lastname: Seidel
authors.forename: Tino
authors.initials: T
authors.lastname: Weinkauf