@Article{wang16a,
  author    = {Z.~Wang and H.-P.~Seidel and T.~Weinkauf},
  title     = {Multi-field Pattern Matching based on Sparse Feature Sampling},
  journal   = {IEEE Transactions on Visualization and Computer Graphics (Proc. IEEE VIS)},
  year      = {2016},
  volume    = {22},
  number    = {1},
  pages     = {807--816},
  month     = {January},
  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.},
  url = {http://tinoweinkauf.net/publications/abswang16a.html},
}