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

By F. Post, Benjamin Vrolijk, Helwig Hauser, R. Laramee, Helmut Doleisch. Computer graphics forum (Print), 2003

525 citations recorded by the source corpus.

Subfields: Computational Mechanics, Global and Planetary Change, Signal Processing

View Paper: https://www.semanticscholar.org/paper/ee8f43eb2f5ee7cd565d5358992a1977b54cc74a

Citations in the corpus

• Visualizing High-Dimensional Data: Advances in the Past Decade (2017, unknown): “It uses support vector regression (SVR) [SS04b] to fit a model to high-dimensional data, highlights discrepancies between the data and the model, and computes sensitivity information on the model.”
• Visualization and Visual Analysis of Multifaceted Scientific Data: A Survey (2013, unknown): “Typical (semi)automated analysis methods that are often combined with InfoVis techniques include [24], [25]: data reduction via sampling or algorithmic feature extraction [82]; clustering [68], [83], [84] where data items are grouped by similarity; and dimensionality reduction that aims to reduce th”