Browsing by Author "Telea, Alexandru"

Now showing 1 - 10 of 10
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    Enhanced Attribute-Based Explanations of Multidimensional Projections
    (The Eurographics Association, 2020) Driel, Daan van; Zhai, Xiaorui; Tian, Zonglin; Telea, Alexandru; Turkay, Cagatay and Vrotsou, Katerina
    Multidimensional projections (MPs) are established tools for exploring the structure of high-dimensional datasets to reveal groups of similar observations. For optimal usage, MPs can be augmented with mechanisms that explain what such points have in common that makes them similar. We extend the set of such explanatory instruments by two new techniques. First, we compute and encode the local dimensionality of the data in the projection, thereby showing areas where the MP can be well explained by a few latent variables. Secondly, we compute and display local attribute correlations, thereby helping the user to discover alternative explanations for the underlying phenomenon. We implement our explanatory tools using an image-based approach, which is efficient to compute, scales well visually for large and dense MP scatterplots, and can handle any projection technique. We demonstrate our approach using several datasets.
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    EUROGRAPHICS 2018: Tutorials Frontmatter
    (Eurographics Association, 2018) Ritschel, Tobias; Telea, Alexandru; Ritschel, Tobias; Telea, Alexandru
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    Feature Driven Combination of Animated Vector Field Visualizations
    (The Eurographics Association and John Wiley & Sons Ltd., 2020) Lobo, MarĂ­a JesĂşs; Telea, Alexandru; Hurter, Christophe; Viola, Ivan and Gleicher, Michael and Landesberger von Antburg, Tatiana
    Animated visualizations are one of the methods for finding and understanding complex structures of time-dependent vector fields. Many visualization designs can be used to this end, such as streamlines, vector glyphs, and image-based techniques. While all such designs can depict any vector field, their effectiveness in highlighting particular field aspects has not been fully explored. To fill this gap, we compare three animated vector field visualization techniques, OLIC, IBFV, and particles, for a critical point detection-and-classification task through a user study. Our results show that the effectiveness of the studied techniques depends on the nature of the critical points. We use these results to design a new flow visualization technique that combines all studied techniques in a single view by locally using the most effective technique for the patterns present in the flow data at that location. A second user study shows that our technique is more efficient and less error prone than the three other techniques used individually for the critical point detection task.
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    Identifying Cluttering Edges in Near-Planar Graphs
    (The Eurographics Association, 2023) Wageningen, Simon van; Mchedlidze, Tamara; Telea, Alexandru; Hoellt, Thomas; Aigner, Wolfgang; Wang, Bei
    Planar drawings of graphs tend to be favored over non-planar drawings. Testing planarity and creating a planar layout of a planar graph can be done in linear time. However, creating readable drawings of nearly planar graphs remains a challenge. We therefore seek to answer which edges of nearly planar graphs create clutter in their drawings generated by mainstream graph drawing algorithms. We present a heuristic to identify problematic edges in nearly planar graphs and adjust their weights in order to produce higher quality layouts with spring-based drawing algorithms. Our experiments show that our heuristic produces significantly higher quality drawings for augmented grid graphs, augmented triangulations, and deep triangulations.
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    Quantitative Comparison of Time-Dependent Treemaps
    (The Eurographics Association and John Wiley & Sons Ltd., 2020) Vernier, Eduardo; Sondag, Max; Comba, JoĂŁo; Speckmann, Bettina; Telea, Alexandru; Verbeek, Kevin; Viola, Ivan and Gleicher, Michael and Landesberger von Antburg, Tatiana
    Rectangular treemaps are often the method of choice to visualize large hierarchical datasets. Nowadays such datasets are available over time, hence there is a need for (a) treemaps that can handle time-dependent data, and (b) corresponding quality criteria that cover both a treemap's visual quality and its stability over time. In recent years a wide variety of (stable) treemapping algorithms has been proposed, with various advantages and limitations. We aim to provide insights to researchers and practitioners to allow them to make an informed choice when selecting a treemapping algorithm for specific applications and data. To this end, we perform an extensive quantitative evaluation of rectangular treemaps for time-dependent data. As part of this evaluation we propose a novel classification scheme for time-dependent datasets. Specifically, we observe that the performance of treemapping algorithms depends on the characteristics of the datasets used. We identify four potential representative features that characterize time-dependent hierarchical datasets and classify all datasets used in our experiments accordingly. We experimentally test the validity of this classification on more than 2000 datasets, and analyze the relative performance of 14 state-of-the-art rectangular treemapping algorithms across varying features. Finally, we visually summarize our results with respect to both visual quality and stability to aid users in making an informed choice among treemapping algorithms. All datasets, metrics, and algorithms are openly available to facilitate reuse and further comparative studies.
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    Route-Aware Edge Bundling for Visualizing Origin-Destination Trails in Urban Traffic
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Zeng, Wei; Shen, Qiaomu; Jiang, Yuzhe; Telea, Alexandru; Gleicher, Michael and Viola, Ivan and Leitte, Heike
    Origin-destination (OD) trails describe movements across space. Typical visualizations thereof use either straight lines or plot the actual trajectories. To reduce clutter inherent to visualizing large OD datasets, bundling methods can be used. Yet, bundling OD trails in urban traffic data remains challenging. Two specific reasons hereof are the constraints implied by the underlying road network and the difficulty of finding good bundling settings. To cope with these issues, we propose a new approach called Route Aware Edge Bundling (RAEB). To handle road constraints, we first generate a hierarchical model of the road-and-trajectory data. Next, we derive optimal bundling parameters, including kernel size and number of iterations, for a user-selected level of detail of this model, thereby allowing users to explicitly trade off simplification vs accuracy. We demonstrate the added value of RAEB compared to state-of-the-art trail bundling methods on both synthetic and real-world traffic data for tasks that include the preservation of road network topology and the support of multiscale exploration.
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    Scaling Up the Explanation of Multidimensional Projections
    (The Eurographics Association, 2023) Thijssen, Julian; Tian, Zonglin; Telea, Alexandru; Angelini, Marco; El-Assady, Mennatallah
    We present a set of interactive visual analysis techniques aiming at explaining data patterns in multidimensional projections. Our novel techniques include a global value-based encoding that highlights point groups having outlier values in any dimension as well as several local tools that provide details on the statistics of all dimensions for a user-selected projection area. Our techniques generically apply to any projection algorithm and scale computationally well to hundreds of thousands of points and hundreds of dimensions. We describe a user study that shows that our visual tools can be quickly learned and applied by users to obtain non-trivial insights in real-world multidimensional datasets.
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    ShaRP: Shape-Regularized Multidimensional Projections
    (The Eurographics Association, 2023) Machado, Alister; Telea, Alexandru; Behrisch, Michael; Angelini, Marco; El-Assady, Mennatallah
    Projections, or dimensionality reduction methods, are techniques of choice for the visual exploration of high-dimensional data. Many such techniques exist, each one of them having a distinct visual signature - i.e., a recognizable way to arrange points in the resulting scatterplot. Such signatures are implicit consequences of algorithm design, such as whether the method focuses on local vs global data pattern preservation; optimization techniques; and hyperparameter settings. We present a novel projection technique - ShaRP - that provides users explicit control over the visual signature of the created scatterplot, which can cater better to interactive visualization scenarios. ShaRP scales well with dimensionality and dataset size, generically handles any quantitative dataset, and provides this extended functionality of controlling projection shapes at a small, user-controllable cost in terms of quality metrics.
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    USTNet: Unsupervised Shape-to-Shape Translation via Disentangled Representations
    (The Eurographics Association and John Wiley & Sons Ltd., 2022) Wang, Haoran; Li, Jiaxin; Telea, Alexandru; Kosinka, JirĂ­; Wu, Zizhao; Umetani, Nobuyuki; Wojtan, Chris; Vouga, Etienne
    We propose USTNet, a novel deep learning approach designed for learning shape-to-shape translation from unpaired domains in an unsupervised manner. The core of our approach lies in disentangled representation learning that factors out the discriminative features of 3D shapes into content and style codes. Given input shapes from multiple domains, USTNet disentangles their representation into style codes that contain distinctive traits across domains and content codes that contain domaininvariant traits. By fusing the style and content codes of the target and source shapes, our method enables us to synthesize new shapes that resemble the target style and retain the content features of source shapes. Based on the shared style space, our method facilitates shape interpolation by manipulating the style attributes from different domains. Furthermore, by extending the basic building blocks of our network from two-class to multi-class classification, we adapt USTNet to tackle multi-domain shape-to-shape translation. Experimental results show that our approach can generate realistic and natural translated shapes and that our method leads to improved quantitative evaluation metric results compared to 3DSNet. Codes are available at https://Haoran226.github.io/USTNet.
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    Visual Exploration of Neural Network Projection Stability
    (The Eurographics Association, 2022) Bredius, Carlo; Tian, Zonglin; Telea, Alexandru; Archambault, Daniel; Nabney, Ian; Peltonen, Jaakko
    We present a method to visually assess the stability of deep learned projections. For this, we perturb the high-dimensional data by controlled sequences and visualize the resulting changes in the 2D projection. We apply our method to a recent deep learned projection framework on several training configurations (learned projections and real-world datasets). Our method, which is simple to implement, runs at interactive rates, sheds several novel insights on the stability of the explored method.