Screen-space silhouettes for visualizing ensembles of 3D isosurfaces

Demir, I.; Kehrer, Johannes; Westermann, Rüdiger

doi:10.1109/pacificvis.2016.7465271

Cited by 20 publications

(5 citation statements)

References 47 publications

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“…For example, Ferstl et al 12 use PCA to convert streamlines into a structure preserving Euclidean space. Furthermore, a lot of approaches are implemented by feature extraction algorithms like k-series in the work 5,36,6 , and the work based on DBSCAN 10,26 , AHC 12,13,37,38,39,40 and topic modeling 41,42,43,44,45,46,47,48,49,50,51,52,53 .…”

Section: Spatio-temporal Simulation Data Visualizationmentioning

confidence: 99%

eBoF: Interactive Temporal Correlation Analysis for Ensemble Data Based on Bag-of-Features

Ding

Qian

Chen

et al. 2022

Preprint

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We propose eBoF, a novel time-varying ensemble data visualization approach based on bag-of-features (BoF). In the eBoF model, we take a simple and monotone interval from all target variables of ensemble scalar data as a local feature patch of BoF model and the duration time of each interval (i.e., feature patch) as its frequency. The feature clusters in ensemble runs are then identified based on the similarity of temporal correlations. eBoF generates the clusters together with their probability distribution across all the feature patches while storing the geo-spatial information, which is often lost in the traditional topic modelling or clustering algorithms. The probability distribution across different clusters can help to generate reasonable clustering results evaluated by the domain knowledge. We conduct several case studies and performance analyses. We also consult the domain experts to evaluate the proposed eBoF model. Evaluation results suggest the proposed eBoF can provide insightful and comprehensive evidence on ensemble simulation data analysis.

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Section: Spatio-temporal Simulation Data Visualizationmentioning

confidence: 99%

eBoF: Interactive Temporal Correlation Analysis for Ensemble Data Based on Bag-of-Features

Ding

Qian

Chen

et al. 2022

Preprint

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“…This method produces a bubble-like shading effect. This effect is described in Demir et al [30]. Programmable Transfer Functions can not only implement the silhouette shading effect; they also enable the user to manipulate the silhouette parameters in real time.…”

Section: Silhouette Shadingmentioning

confidence: 99%

Visualization of WiFi Signals Using Programmable Transfer Functions

et al. 2022

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In this paper, we show how volume rendering with a Programmable Transfer Function can be used for the effective and comprehensible visualization of WiFi signals. A traditional transfer function uses a low-dimensional lookup table to map the volumetric scalar field to color and opacity. In this paper, we present the concept of a Programmable Transfer Function. We then show how generalizing traditional lookup-based transfer functions to Programmable Transfer Functions enables us to leverage view-dependent and real-time attributes of a volumetric field to depict the data variations of WiFi surfaces with low and high-frequency components. Our Programmable Transfer Functions facilitate interactive knowledge discovery and produce meaningful visualizations.

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“…Increasing in complexity, we see the use of pairwise sequential animation and screen door tinting to show the differences between ensemble members using value changes to field points [PPA*12], the comparison of 2D function ensembles on three levels of details (i.e. surface plot, domain‐oriented and member‐oriented) [PPBT12], the comparison of 3D scalar field ensembles using mean isosurface [DKW16] and grouping ensemble members using hierarchical clustering [HHBY16]. Although these tools offer different ways to visualize and compare different data types, they are limited in the number of ensemble members that they visualize.…”

Section: Related Workmentioning

confidence: 99%

Making Sense of Scientific Simulation Ensembles With Semantic Interaction

Dahshan

Polys

Jayne

et al. 2020

Computer Graphics Forum

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In the study of complex physical systems, scientists use simulations to study the effects of different models and parameters. Seeking to understand the influence and relationships among multiple dimensions, they typically run many simulations and vary the initial conditions in what are known as ‘ensembles’. Ensembles are then a number of runs that are each multi‐dimensional and multi‐variate. In order to understand the connections between simulation parameters and patterns in the output data, we have been developing an approach to the visual analysis of scientific data that merges human expertise and intuition with machine learning and statistics. Our approach is manifested in a new visualization tool, GLEE (Graphically‐Linked Ensemble Explorer), that allows scientists to explore, search, filter and make sense of their ensembles. GLEE uses visualization and semantic interaction (SI) techniques to enable scientists to find similarities and differences between runs, find correlation(s) between different parameters and explore relations and correlations across and between different runs and parameters. Our approach supports scientists in selecting interesting subsets of runs in order to investigate and summarize the factors and statistics that show variations and consistencies across different runs. In this paper, we evaluate our tool with experts to understand its strengths and weaknesses for optimization and inverse problems.

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Screen-space silhouettes for visualizing ensembles of 3D isosurfaces

Cited by 20 publications

References 47 publications

eBoF: Interactive Temporal Correlation Analysis for Ensemble Data Based on Bag-of-Features

eBoF: Interactive Temporal Correlation Analysis for Ensemble Data Based on Bag-of-Features

Visualization of WiFi Signals Using Programmable Transfer Functions

Making Sense of Scientific Simulation Ensembles With Semantic Interaction

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