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Abstract

We propose a method that allows users to define flow features in form
of patterns represented as sparse sets of stream line segments. Our
approach finds similar occurrences in the same or other time steps.
Related approaches define patterns using dense, local stencils or
support only single segments. Our patterns are defined sparsely and
can have a significant extent, i.e., they are integration-based and
not local. This allows for a greater flexibility in defining features
of interest. Similarity is measured using intrinsic curve properties
only, which enables invariance to location, orientation, and scale.
Our method starts with splitting stream lines using globally-consistent
segmentation criteria. It strives to maintain the visually apparent
features of the flow as a collection of stream line segments. Most
importantly, it provides similar segmentations for similar flow structures.
For user-defined patterns of curve segments, our algorithm finds
similar ones that are invariant to similarity transformations. We
showcase the utility of our method using different 2D and 3D flow
fields.