Visualization of time-dependent data with feature tracking and event detection

Reinders, Freek; Post, Frits H.; Spoelder, H.J.W.

doi:10.1007/pl00013399

Cited by 95 publications

(63 citation statements)

References 19 publications

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“…Defining and tracking features [23] is a common solution to visualizing large timevarying data sets. We focus on scalar fields, where many feature definitions are based on isosurfaces [20].…”

Section: Related Workmentioning

confidence: 99%

Computing and Visualizing Time-Varying Merge Trees for High-Dimensional Data

Oesterling¹,

Heine

Weber

et al. 2017

Mathematics and Visualization

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We introduce a new method that identifies and tracks features in arbitrary dimensions using the merge tree-a structure for identifying topological features based on thresholding in scalar fields. This method analyzes the evolution of features of the function by tracking changes in the merge tree and relates features by matching subtrees between consecutive time steps. Using the time-varying merge tree, we present a structural visualization of the changing function that illustrates both features and their temporal evolution. We demonstrate the utility of our approach by applying it to temporal cluster analysis of high-dimensional point clouds.

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Section: Related Workmentioning

confidence: 99%

Computing and Visualizing Time-Varying Merge Trees for High-Dimensional Data

Oesterling¹,

Heine

Weber

et al. 2017

Mathematics and Visualization

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“…Banks and Singer [3] used a predictorcorrector method to reconstruct and track vortex tubes from turbulent time-dependent flows. Reinders et al [22] matched several attributes of features and tracked feature paths based on the motion continuity. Verma and Pang [29] proposed comparative visualization tools for analyzing vector datasets based on streamlines.…”

Section: Related Workmentioning

confidence: 99%

Interactive Storyboard for Overall Time-Varying Data Visualization

Shen

2008

2008 IEEE Pacific Visualization Symposium

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Large amounts of time-varying datasets create great challenges for users to understand and explore them. This paper proposes an efficient visualization method for observing overall data contents and changes throughout an entire time-varying dataset. We develop an interactive storyboard approach by composing sample volume renderings and descriptive geometric primitives that are generated through data analysis processes. Our storyboard system integrates automatic visualization generation methods and interactive adjustment procedures to provide new tools for visualizing and exploring time-varying datasets. We also provide a flexible framework to quantify data differences and automatically select representative datasets through exploring scientific data distribution features. Since this approach reduces the visualized data amount into a more understandable size and format for users, it can be used to effectively visualize, represent, and explore a large time-varying dataset. Initial user study results show that our approach shortens the exploration time and reduces the number of datasets that users visualized individually. This visualization method is especially useful for situations that require close observance or are not capable of interactive rendering, such as documentation and demonstration.

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“…In some follow-up work they also use the volume overlap of features to decide correspondences [25,26]. Reinders et al [22] use very similar ideas combined with motion prediction to improve matching accuracy.…”

Section: Prior Workmentioning

confidence: 99%

Understanding the Structure of the Turbulent Mixing Layer in Hydrodynamic Instabilities

Laney

Bremer

Mascarenhas

et al. 2006

IEEE Trans. Visual. Comput. Graphics

144

102

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Abstract-When a heavy fluid is placed above a light fluid, tiny vertical perturbations in the interface create a characteristic structure of rising bubbles and falling spikes known as Rayleigh-Taylor instability. Rayleigh-Taylor instabilities have received much attention over the past half-century because of their importance in understanding many natural and man-made phenomena, ranging from the rate of formation of heavy elements in supernovae to the design of capsules for Inertial Confinement Fusion. We present a new approach to analyze Rayleigh-Taylor instabilities in which we extract a hierarchical segmentation of the mixing envelope surface to identify bubbles and analyze analogous segmentations of fields on the original interface plane. We compute meaningful statistical information that reveals the evolution of topological features and corroborates the observations made by scientists. We also use geometric tracking to follow the evolution of single bubbles and highlight merge/split events leading to the formation of the large and complex structures characteristic of the later stages. In particular we (i) Provide a formal definition of a bubble; (ii) Segment the envelope surface to identify bubbles; (iii) Provide a multi-scale analysis technique to produce statistical measures of bubble growth; (iv) Correlate bubble measurements with analysis of fields on the interface plane; (v) Track the evolution of individual bubbles over time. Our approach is based on the rigorous mathematical foundations of Morse theory and can be applied to a more general class of applications.

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Visualization of time-dependent data with feature tracking and event detection

Cited by 95 publications

References 19 publications

Computing and Visualizing Time-Varying Merge Trees for High-Dimensional Data

Computing and Visualizing Time-Varying Merge Trees for High-Dimensional Data

Interactive Storyboard for Overall Time-Varying Data Visualization

Understanding the Structure of the Turbulent Mixing Layer in Hydrodynamic Instabilities

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