Flow visualization research has made rapid advances in recent years, especially in the area of topology-based flow visualization. The ever increasing size of scientific data sets favors algorithms that are capable of extracting important subsets of the data, leaving the scientist with a more manageable representation that may be visualized interactively. Extracting the topology of a flow achieves the goal of obtaining a compact representation of a vector or tensor field while simultaneously retaining its most important features. We present the state of the art in topology-based flow visualization techniques. We outline numerous topology-based algorithms categorized according to the type and dimensionality of data on which they operate and according to the goal-oriented nature of each method. Topology tracking algorithms are also discussed. The result serves as a useful introduction and overview to research literature concerned with the study of topology-based flow visualization. Keywords: flow visualization, feature-based flow visualization, flow topology, state of the art report Research in topology-based flow visualization is making rapid advances. Helman and Hesselink introduced the visualization community to the notion of flow topology in 1989 [21, 23] . Classical flow oriented topology research is based on the detection and classification of critical points in the vector field, as shown in Figure 1 .2. What makes topology-based methods attractive is their ability to represent very large data sets in a concise and compact manner. Unlike other flow visualization approaches ⋆ Robert S. Laramee's current affiliation is: The
Extracting and visualizing turbulence in vector fields has long been of interest to the visualization community REF .
Topology-Based Flow Visualization, The State of the Art
Topology-based Methods in Visualization
Topology-based Methods in Visualization
Computer Science