Search Results

Now showing 1 - 10 of 32
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    Personalized Visual-Interactive Music Classification
    (The Eurographics Association, 2018) Ritter, Christian; Altenhofen, Christian; Zeppelzauer, Matthias; Kuijper, Arjan; Schreck, Tobias; Bernard, Jürgen; Christian Tominski and Tatiana von Landesberger
    We present an interactive visual music classification tool that will allow users to automatically structure music collections in a personalized way. With our approach, users play an active role in an iterative process of building classification models, using different interactive interfaces for labeling songs. The interactive tool conflates interfaces for the detailed analysis at different granularities, i.e., audio features, music songs, as well as classification results at a glance. Interactive labeling is provided with three complementary interfaces, combining model-centered and human-centered labeling-support principles. A clean visual design of the individual interfaces depicts complex model characteristics for experts, and indicates our work-inprogress towards the abilities of non-experts. The result of a preliminary usage scenario shows that, with our system, hardly any knowledge about machine learning is needed to create classification models of high accuracy with less than 50 labels.
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    Elastic Flattening of Painted Pottery Surfaces
    (The Eurographics Association, 2018) Preiner, Reinhold; Karl, Stephan; Bayer, Paul; Schreck, Tobias; Sablatnig, Robert and Wimmer, Michael
    Generating flat images from paintings on curved surfaces is an important task in Archaeological analysis of ancient pottery. It allows comparing styles and painting techniques, e.g, for style and workshop attribution, and serves as basis for domain publications which typically use 2d images. To obtain such flat images from scanned textured 3d models of the pottery objects, current practice is to perform so-called rollouts using approximating shape primitives like cones or spheres, onto which the mesh surfaces are projected. While this process provides in intuitive deformation metaphor for the users, it naturally introduces unwanted distortions in the mapping of the surface, especially for vessels with high-curvature profiles. In this work, we perform an elastic flattening of these projected meshes, where stretch energy is minimized by simulating a physical relaxation process on a damped elastic spring model. We propose an intuitive contraction-directed physical setup which allows for an efficient relaxation while ensuring a controlled convergence. Our work has shown to produce images of significantly improved suitability for domain experts' tasks like interpretation, documentation and attribution of ancient pottery.
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    Interactive Visual Exploration of Local Patterns in Large Scatterplot Spaces
    (The Eurographics Association and John Wiley & Sons Ltd., 2018) Chegini, Mohammad; Shao, Lin; Gregor, Robert; Lehmann, Dirk Joachim; Andrews, Keith; Schreck, Tobias; Jeffrey Heer and Heike Leitte and Timo Ropinski
    Analysts often use visualisation techniques like a scatterplot matrix (SPLOM) to explore multivariate datasets. The scatterplots of a SPLOM can help to identify and compare two-dimensional global patterns. However, local patterns which might only exist within subsets of records are typically much harder to identify and may go unnoticed among larger sets of plots in a SPLOM. This paper explores the notion of local patterns and presents a novel approach to visually select, search for, and compare local patterns in a multivariate dataset. Model-based and shape-based pattern descriptors are used to automatically compare local regions in scatterplots to assist in the discovery of similar local patterns. Mechanisms are provided to assess the level of similarity between local patterns and to rank similar patterns effectively. Moreover, a relevance feedback module is used to suggest potentially relevant local patterns to the user. The approach has been implemented in an interactive tool and demonstrated with two real-world datasets and use cases. It supports the discovery of potentially useful information such as clusters, functional dependencies between variables, and statistical relationships in subsets of data records and dimensions.
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    Visual Analysis of Aluminum Production Data with Tightly Linked Views
    (The Eurographics Association, 2019) Jekic, Nikolina; Mutlu, Belgin; Faschang, Mario; Neubert, Steffen; Thalmann, Stefan; Schreck, Tobias; Madeiras Pereira, João and Raidou, Renata Georgia
    Monitoring, analyzing and determining the production quality in a complex and long-running process such as in the aluminum production is a challenging task. We aim to support production data exploration in the aluminum industry. To this end, we developed the first version of the interactive visual analytics tool ADAM. The main aspect of concern is product quality, which is obtained from the quality inspection of aluminum plates at the end of the production process. A set of tightly linked views of production parameters with cross-filtering capability support the inspection of factors possibly influencing the product quality. ADAM allows highly responsive forward and backward search in the quality and production parameter space, leading to an understanding of important parameters, and supporting production planning and process improvement. Our approach was designed in an iterative development cycle guided by domain requirements from a major aluminum producer. We introduce the domain problem, propose a visual analytics design to support the problem, and demonstrate by application to real production data the usefulness and possible insights which can be obtained.
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    Visual Analysis of Urban Traffic Data based on High-Resolution and High-Dimensional Environmental Sensor Data
    (The Eurographics Association, 2018) Häußler, Johannes; Stein, Manuel; Seebacher, Daniel; Janetzko, Halldor; Schreck, Tobias; Keim, Daniel; Karsten Rink and Dirk Zeckzer and Roxana Bujack and Stefan Jänicke
    Urbanization is an increasing global trend resulting in a strong increase in public and individual transportation needs. Accordingly, a major challenge for traffic and urban planners is the design of sustainable mobility concepts to maintain and increase the long-term health of humans by reducing environmental pollution. Recent developments in sensor technology allow the precise tracking of vehicle sensor information, allowing a closer and more in-depth analysis of traffic data. We propose a visual analytics system for the exploration of environmental factors in these high-resolution and high-dimensional mobility sensor data. Additionally, we introduce an interactive visual logging approach to enable experts to cope with complex interactive analysis processes and the problem of the reproducibility of results. The usefulness of our approach is demonstrated via two expert studies with two domain experts from the field of environment-related projects and urban traffic planning.
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    MotionGlyphs: Visual Abstraction of Spatio-Temporal Networks in Collective Animal Behavior
    (The Eurographics Association and John Wiley & Sons Ltd., 2020) Cakmak, Eren; Schäfer, Hanna; Buchmüller, Juri; Fuchs, Johannes; Schreck, Tobias; Jordan, Alex; Keim, Daniel A.; Viola, Ivan and Gleicher, Michael and Landesberger von Antburg, Tatiana
    Domain experts for collective animal behavior analyze relationships between single animal movers and groups of animals over time and space to detect emergent group properties. A common way to interpret this type of data is to visualize it as a spatio-temporal network. Collective behavior data sets are often large, and may hence result in dense and highly connected node-link diagrams, resulting in issues of node-overlap and edge clutter. In this design study, in an iterative design process, we developed glyphs as a design for seamlessly encoding relationships and movement characteristics of a single mover or clusters of movers. Based on these glyph designs, we developed a visual exploration prototype, MotionGlyphs, that supports domain experts in interactively filtering, clustering, and animating spatio-temporal networks for collective animal behavior analysis. By means of an expert evaluation, we show how MotionGlyphs supports important tasks and analysis goals of our domain experts, and we give evidence of the usefulness for analyzing spatio-temporal networks of collective animal behavior.
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    EuroVis 2023 CGF 42-3: Frontmatter
    (The Eurographics Association and John Wiley & Sons Ltd., 2023) Bujack, Roxana; Archambault, Daniel; Schreck, Tobias; Bujack, Roxana; Archambault, Daniel; Schreck, Tobias
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    A Concept for Consensus-based Ordering of Views
    (The Eurographics Association, 2018) Jentner, Wolfgang; Jäckle, Dominik; Engelke, Ulrich; Keim, Daniel A.; Schreck, Tobias; Christian Tominski and Tatiana von Landesberger
    High-dimensional data poses a significant challenge for analysis, as patterns typically exist only in subsets of dimensions or records. A common approach to reveal patterns, such as meaningful structures or relationships, is to split the data and then to create a visual representation (views) for each data subset. This introduces the problem of ordering the views effectively because patterns can depend on the presented sequence. Existing methods provide metrics and heuristics to achieve an ordering of views based on their data characteristics. However, an effective ordering of subspace views is expected to rely on task- and data-dependent properties. Hence, heuristic-based ordering methods can be highly objective and not relevant to the task at hand, which is why the user involvement is key to find a meaningful ordering. We introduce a concept for a consensus-based ordering of views that learns to form sequences of subset views fitting the overall users' needs. This concept allows users to decide on the ordering freely and accumulates their preference into a global view that reflects the consensus. We showcase and discuss this concept based on ordering colored tiles from the controversially discussed rainbow color map.
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    Hierarchical Topic Maps for Visual Exploration and Comparison of Documents
    (The Eurographics Association, 2024) Tytarenko, Mariia; Shao, Lin; Rutar, Tobias Walter; Bedek, Michael A.; Krenn, Cornelia; Lengauer, Stefan; Schreck, Tobias; El-Assady, Mennatallah; Schulz, Hans-Jörg
    Information visualization nowadays provides a large amount of different text visualization techniques that help to summarize and present textual information in an intuitive and comprehensible manner. Despite many advancements, there remains a gap in effectively illustrating the thematic and structural distinction between similar documents in a hierarchical and interactive manner. We present the Hierarchical Topic Maps (HTM), an innovative approach, inspired by Tile Bars, that addresses this gap by illustrating the content distribution across a document hierarchically. Our model incorporates a multi-resolution display feature, enabling users, in particular curators of large document collections, with the need to quickly obtain text document structure, to delve deeper and draw more meaningful conclusions, to assess thematic similarities at multiple levels of detail, as well as facilitate nuanced comparison of textual documents. We demonstrate the effectiveness of both our approach's document exploration and document comparison potential by two exemplary use case scenarios. Our findings suggest that HTM not only simplifies the document overview process but also provides a practical solution for comparing thematic structures, thereby offering contributions to the field of text visualization and visualization analytics.
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    Augmenting Node-Link Diagrams with Topographic Attribute Maps
    (The Eurographics Association and John Wiley & Sons Ltd., 2020) Preiner, Reinhold; Schmidt, Johanna; Krösl, Katharina; Schreck, Tobias; Mistelbauer, Gabriel; Viola, Ivan and Gleicher, Michael and Landesberger von Antburg, Tatiana
    We propose a novel visualization technique for graphs that are attributed with scalar data. In many scenarios, these attributes (e.g., birth date in a family network) provide ambient context information for the graph structure, whose consideration is important for different visual graph analysis tasks. Graph attributes are usually conveyed using different visual representations (e.g., color, size, shape) or by reordering the graph structure according to the attribute domain (e.g., timelines). While visual encodings allow graphs to be arranged in a readable layout, assessing contextual information such as the relative similarities of attributes across the graph is often cumbersome. In contrast, attribute-based graph reordering serves the comparison task of attributes, but typically strongly impairs the readability of the structural information given by the graph's topology. In this work, we augment force-directed node-link diagrams with a continuous ambient representation of the attribute context. This way, we provide a consistent overview of the graph's topological structure as well as its attributes, supporting a wide range of graph-related analysis tasks. We resort to an intuitive height field metaphor, illustrated by a topographic map rendering using contour lines and suitable color maps. Contour lines visually connect nodes of similar attribute values, and depict their relative arrangement within the global context. Moreover, our contextual representation supports visualizing attribute value ranges associated with graph nodes (e.g., lifespans in a family network) as trajectories routed through this height field. We discuss how user interaction with both the structural and the contextual information fosters exploratory graph analysis tasks. The effectiveness and versatility of our technique is confirmed in a user study and case studies from various application domains.