2016 IEEE Scientific Visualization Contest Winner: Visual and Structural Analysis of Point-based Simulation Ensembles

Gralka, Patrick; Grottel, Sebastian; Staib, Joachim; Schatz, Karsten; Karch, Grzegorz; Hirschler, Manuel; Krone, Michael; Reina, Guido; Gumhold, Stefan; Ertl, Thomas

doi:10.1109/mcg.2017.3301120

Cited by 7 publications

(7 citation statements)

References 6 publications

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“…Particle simulations are used across a plethora of scientific fields. Analyzing and understanding this kind of data is a hot topic, as evidenced by the fact that 2015, 2016 and 2019 scientific visualization contests all focused on particle datasets [6], [10], [11]. For each of these datasets, the tasks of feature extraction and tracking were critical for understanding the development of the simulated phenomena over time.…”

Section: The Proposed Systemmentioning

confidence: 99%

“…Prior to doing topological analysis on the data set have re-sampled irregular volumetric points to one regular volume [2]. Similar to this, 2016 SciVis contest winners [6] suggested a technique that first create a smooth scalar field before extracting crucial points with the use of a contour tree. In problem to solve features from a particle data set that were defined over many variables have employed a region growing approach in attribute space [28].…”

Section: Feature Extraction and Trackingmentioning

confidence: 99%

“…In this case, while feature tracking can be assisted by tracking the movement of corresponding particles, it is still necessary to perform feature tracking to identify the dynamic set of feature particles in all time steps [5]. Moreover, there are situations when particle indices are not available [6] or discarded to reduce the storage size.…”

Section: Introductionmentioning

confidence: 99%

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Novel extraction and tracking method used in multiplelevel for computer vision

Akram

Abdulateef

2023

IJEECS

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Various scientific applications, including cosmological simulation, fluid simulation, and molecular dynamics, depend heavily on the analysis of particle data. Although there are techniques for feature extraction and tracking regarding volumetric data, it is more difficult to do such tasks for particle data due to the lack of explicit connectivity information. Even though one could transform the particle data to volume beforehand, doing so runs the risk of incurring error and growing the data size. In order to facilitate feature extraction and tracking for scientific particle data, we adopt a deep learning (DL) method in this research. In order to capture the relation between physical features and spatial locations in a neighborhood, we use a DL model that generates latent vectors. Through clustering the latent vectors, characteristics could be retrieved from the vectors. The Cam-shift tracking algorithm, which just needs inference of the latent vector for chosen regions of interest, is implemented in the feature space to accomplish quick feature tracking. With the use of two datasets, we test our approach and contrast it with other approaches already in use.

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Section: The Proposed Systemmentioning

confidence: 99%

Section: Feature Extraction and Trackingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Novel extraction and tracking method used in multiplelevel for computer vision

Akram

Abdulateef

2023

IJEECS

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“…These visualizations can be static [RPS01, LBM ∗ 06, BWT ∗ 11] or interactive [WCBP12, LGW ∗ 20], and a number of sophisticated approaches have been used to highlight the hierarchical structure [LWM ∗ 17, LGW ∗ 20] or to allow adaptive thresholds [WKK ∗ 15]. A comprehensive review of the corresponding literature is beyond the scope of this paper; we refer the reader to the large collection of papers following the 2016 Visualization Contest [Sci16] on analyzing time dependent particle simulations [GEG ∗ 18,LAS ∗ 17,SPD ∗ 19] as a starting point.…”

Section: Related Workmentioning

confidence: 99%

Leveraging Topological Events in Tracking Graphs for Understanding Particle Diffusion

McDonald

Shrestha

et al. 2021

Computer Graphics Forum

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Single particle tracking (SPT) of fluorescent molecules provides significant insights into the diffusion and relative motion of tagged proteins and other structures of interest in biology. However, despite the latest advances in high‐resolution microscopy, individual particles are typically not distinguished from clusters of particles. This lack of resolution obscures potential evidence for how merging and splitting of particles affect their diffusion and any implications on the biological environment. The particle tracks are typically decomposed into individual segments at observed merge and split events, and analysis is performed without knowing the true count of particles in the resulting segments. Here, we address the challenges in analyzing particle tracks in the context of cancer biology. In particular, we study the tracks of KRAS protein, which is implicated in nearly 20% of all human cancers, and whose clustering and aggregation have been linked to the signaling pathway leading to uncontrolled cell growth. We present a new analysis approach for particle tracks by representing them as tracking graphs and using topological events – merging and splitting, to disambiguate the tracks. Using this analysis, we infer a lower bound on the count of particles as they cluster and create conditional distributions of diffusion speeds before and after merge and split events. Using thousands of time‐steps of simulated and in‐vitro SPT data, we demonstrate the efficacy of our method, as it offers the biologists a new, detailed look into the relationship between KRAS clustering and diffusion speeds.

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“…Gralka et al. [GGS*18] describe a system for the visual and structural investigation of this data via multiple views, drilling down from diagrams of ensemble metrics to investigation of the 3D data, abstracted finger topology and vortex core lines forming in the data. Favelier et al.…”

Section: Related Work In Visualization and Porous Media Researchmentioning

confidence: 99%

Visual Analysis of Two‐Phase Flow Displacement Processes in Porous Media

Frey

Scheller

Karadimitriou

et al. 2021

Computer Graphics Forum

Self Cite

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We developed a new visualization approach to gain a better understanding of the displacement of one fluid phase by another in porous media. This is based on a recent experimental parameter study with varying capillary numbers and viscosity ratios. We analyse the temporal evolution of characteristic values in this two‐phase flow scenario and discuss how to directly compare experiments across different temporal scales. To enable spatio‐temporal analysis, we introduce a new abstract visual representation showing which paths through the porous medium were occupied and for how long. These transport networks allow to assess the impact of different acting forces and they are designed to yield expressive comparability and linking to the experimental parameter space both supported by additional visual cues. This joint work of porous media experts and visualization researchers yields new insights regarding two‐phase flow on the microscale, and our visualization approach contributes towards the overarching goal of the domain scientists to characterize porous media flow based on capillary numbers and viscosity ratios.

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2016 IEEE Scientific Visualization Contest Winner: Visual and Structural Analysis of Point-based Simulation Ensembles

Cited by 7 publications

References 6 publications

Novel extraction and tracking method used in multiplelevel for computer vision

Novel extraction and tracking method used in multiplelevel for computer vision

Leveraging Topological Events in Tracking Graphs for Understanding Particle Diffusion

Visual Analysis of Two‐Phase Flow Displacement Processes in Porous Media

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