Multi-field Pattern Matching based on Sparse Feature Sampling

作者：Wang Zhongjie^*; Seidel Hans Peter; Weinkauf Tino

来源：IEEE Transactions on Visualization and Computer Graphics, 2016, 22(1): 807-816.

DOI：10.1109/TVCG.2015.2467292

• Pattern matching
• multi-field visualization

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.

• 出版日期2016-1