The State of the Art in Flow Visualisation: Feature Extraction and Tracking

TLDR

Flow visualisation tackles large multivariate datasets, leveraging advances in computing to develop techniques such as texturing, feature extraction, vector field clustering, and topology extraction. The article surveys state‑of‑the‑art feature‑based flow visualisation methods. It reviews feature extraction methods by type, discusses tracking and event‑detection algorithms, and demonstrates corresponding visualisation techniques. ACM CSS classification: I.3.8 Computer Graphics—applications.

Abstract

Abstract Flow visualisation is an attractive topic in data visualisation, offering great challenges for research. Very large data sets must be processed, consisting of multivariate data at large numbers of grid points, often arranged in many time steps. Recently, the steadily increasing performance of computers again has become a driving force for new advances in flow visualisation, especially in techniques based on texturing, feature extraction, vector field clustering, and topology extraction. In this article we present the state of the art in feature‐based flow visualisation techniques. We will present numerous feature extraction techniques, categorised according to the type of feature. Next, feature tracking and event detection algorithms are discussed, for studying the evolution of features in time‐dependent data sets. Finally, various visualisation techniques are demonstrated. ACM CSS: I.3.8 Computer Graphics— applications