The State of the Art in Topology‐Based Visualization of Unsteady Flow

Pobitzer, Armin; Peikert, Ronald; Fuchs, Raphael; Schindler, Benjamin; Kühn, Alexander; Theisel, Holger; Matković, Krešimir; Hauser, Helwig

doi:10.1111/j.1467-8659.2011.01901.x

Cited by 97 publications

(58 citation statements)

References 145 publications

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“…However, this is not only an open question for uncertain topology but for topological concepts themselves: even for certain vector fields, general solutions for a time-dependent topology are still an open field of research [25]. Further topics for future work are the extraction of other critical structures like closed orbits and strange attractors, and the use of uncertain vector field visualization as a vector field simplification method.…”

Section: Resultsmentioning

confidence: 99%

Uncertain topology of 3D vector fields

Otto

Germer

Theisel

2011

2011 IEEE Pacific Visualization Symposium

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We present a technique to visualize global uncertainty in stationary 3D vector fields by a topological approach. We start from an existing approach for 2D uncertain vector field topology and extend this into 3D space. For this a number of conceptional and technical challenges in performance and visual representation arise. In order to solve them, we develop an acceleration for finding sink and source distributions. Having these distributions we use overlaps of their corresponding volumes to find separating structures and saddles. As part of the approach, we introduce uncertain saddle and boundary switch connectors and provide algorithms to extract them. For the visual representation, we use multiple direct volume renderings. We test our method on a number of synthetic and real data sets.

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Section: Resultsmentioning

confidence: 99%

Uncertain topology of 3D vector fields

Otto

Germer

Theisel

2011

2011 IEEE Pacific Visualization Symposium

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“…LCS directly describes transport features of particles moving within the flow. A general overview about recent Lagrangian methods for timedependent flow analysis is presented by Pobitzer et al [13]. In video analysis this corresponds to the notion of an edge of a closed moving object within the image.…”

Section: Lagrangian Features and Finite Time Lyapunov Exponents mentioning

confidence: 99%

Detecting People Carrying Objects Utilizing Lagrangian Dynamics

Senst

Kühn

Theisel

et al. 2012

2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance

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The availability of dense motion information in computer vision domain allows for the effective application of Lagrangian techniques that have their origin in fluid flow analysis and dynamical systems theory. A well established technique that has been proven to be useful in image-based crowd analysis are Finite Time Lyapunov Exponents (FTLE). Based on this, we present a method to detect people carrying object and describe a methodology how to apply established flow field methods onto the problem of describing individuals. Further, we reinterpret Lagrangian features in relation to the underlying motion process and show their applicability towards the appearance modeling of pedestrians. This definition allows to increase performance of state-of-the-art methods and is shown to be robust under varying parameter settings and different optical flow extraction approaches.

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“…The Finite-Time Lyapunov exponent (FTLE) [24] has gained increasing attention of late [25]. It quantifies the local separation behavior of the flow.…”

Section: Finite-time Lyapunov Exponentmentioning

confidence: 99%

“…The effect of a change in the time-window length has not been studied sufficiently [25]. A common use of FTLE is to extract Lagrangian Coherent Structures (LCS).…”

Section: Finite-time Lyapunov Exponentmentioning

confidence: 99%

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Visualization of Input Parameters for Stream and Pathline Seeding

McLoughlin¹,

Edmunds²,

Tong³

et al. 2015

ijacsa

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Abstract-Uncertainty arises in all stages of the visualization pipeline. However, the majority of flow visualization applications convey no uncertainty information to the user. In tools where uncertainty is conveyed, the focus is generally on data, such as error that stems from numerical methods used to generate a simulation or on uncertainty associated with mapping visualization primitives to data. Our work is aimed at another source of uncertainty -that associated with user-controlled input parameters. The navigation and stability analysis of user-parameters has received increasing attention recently. This work presents an investigation of this topic for flow visualization, specifically for three-dimensional streamline and pathline seeding. From a dynamical systems point of view, seeding can be formulated as a predictability problem based on an initial condition. Small perturbations in the initial value may result in large changes in the streamline in regions of high unpredictability. Analyzing this predictability quantifies the perturbation a trajectory is subjugated to by the flow. In other words, some predictions are less certain than others as a function of initial conditions. We introduce novel techniques to visualize important user input parameters such as streamline and pathline seeding position in both space and time, seeding rake position and orientation, and inter-seed spacing. The implementation is based on a metric which quantifies similarity between stream and pathlines. This is important for Computational Fluid Dynamics (CFD) engineers as, even with the variety of seeding strategies available, manual seeding using a rake is ubiquitous. We present methods to quantify and visualize the effects that changes in user-controlled input parameters have on the resulting stream and pathlines. We also present various visualizations to help CFD scientists to intuitively and effectively navigate this parameter space. The reaction from a domain expert in fluid dynamics is also reported.

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The State of the Art in Topology‐Based Visualization of Unsteady Flow

Cited by 97 publications

References 145 publications

Uncertain topology of 3D vector fields

Uncertain topology of 3D vector fields

Detecting People Carrying Objects Utilizing Lagrangian Dynamics

Visualization of Input Parameters for Stream and Pathline Seeding

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