Search Results

Now showing 1 - 10 of 51
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    Large Scale Comprehensive 3D Shape Retrieval
    (The Eurographics Association, 2014) Li, B.; Lu, Y.; Li, C.; Godil, A.; Schreck, Tobias; Aono, M.; Chen, Q.; Chowdhury, N. K.; Fang, B.; Furuya, T.; Johan, H.; Kosaka, R.; Koyanagi, H.; Ohbuchi, R.; Tatsuma, A.; Benjamin Bustos and Hedi Tabia and Jean-Philippe Vandeborre and Remco Veltkamp
    The objective of this track is to evaluate the performance of 3D shape retrieval approaches on a large-scale comprehensive 3D shape database that contains different types of models, such as generic, articulated, CAD and architecture models. The track is based on a new comprehensive 3D shape benchmark, which contains 8,987 triangle meshes that are classified into 171 categories. The benchmark was compiled as a superset of existing benchmarks and presents a new challenge to retrieval methods as it comprises generic models as well as domainspecific model types. In this track, 14 runs have been submitted by 5 groups and their retrieval accuracies were evaluated using 7 commonly used performance metrics.
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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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    Revisiting Perceptually Optimized Color Mapping for High-Dimensional Data Analysis
    (The Eurographics Association, 2014) Mittelstädt, Sebastian; Bernard, Jürgen; Schreck, Tobias; Steiger, Martin; Kohlhammer, Jörn; Keim, Daniel A.; N. Elmqvist and M. Hlawitschka and J. Kennedy
    Color is one of the most effective visual variables since it can be combined with other mappings and encodeinformation without using any additional space on the display. An important example where expressing additionalvisual dimensions is direly needed is the analysis of high-dimensional data. The property of perceptual linearity isdesirable in this application, because the user intuitively perceives clusters and relations among multi-dimensionaldata points. Many approaches use two-dimensional colormaps in their analysis, which are typically created byinterpolating in RGB, HSV or CIELAB color spaces. These approaches share the problem that the resulting colorsare either saturated and discriminative but not perceptual linear or vice versa. A solution that combines bothadvantages has been previously introduced by Kaski et al.; yet, this method is to date underutilized in InformationVisualization according to our literature analysis. The method maps high-dimensional data points into the CIELABcolor space by maintaining the relative perceived distances of data points and color discrimination. In this paper,we generalize and extend the method of Kaski et al. to provide perceptual uniform color mapping for visual analysisof high-dimensional data. Further, we evaluate the method and provide guidelines for different analysis tasks.
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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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    Automatic 3D Object Fracturing for Evaluation of Partial Retrieval and Object Restoration Tasks - Benchmark and Application to 3D Cultural Heritage Data
    (The Eurographics Association, 2015) Gregor, Robert; Bauer, Danny; Sipiran, Ivan; Perakis, Panagiotis; Schreck, Tobias; I. Pratikakis and M. Spagnuolo and T. Theoharis and L. Van Gool and R. Veltkamp
    Recently, 3D digitization and printing hardware have seen rapidly increasing adoption. High-quality digitization of real-world objects is becoming more and more efficient. In this context, growing amounts of data from the cultural heritage (CH) domain such as columns, tombstones or arches are being digitized and archived in 3D repositories. In many cases, these objects are not complete, but fragmented into several pieces and eroded over time. As manual restoration of fragmented objects is a tedious and error-prone process, recent work has addressed automatic reassembly and completion of fragmented 3D data sets. While a growing number of related techniques are being proposed by researchers, their evaluation currently is limited to smaller numbers of high-quality test fragment sets. We address this gap by contributing a methodology to automatically generate 3D fragment data based on synthetic fracturing of 3D input objects. Our methodology allows generating large-scale fragment test data sets from existing CH object models, complementing manual benchmark generation based on scanning of fragmented real objects. Besides being scalable, our approach also has the advantage to come with ground truth information (i.e. the input objects), which is often not available when scans of real fragments are used. We apply our approach to the Hampson collection of digitized pottery objects, creating and making available a first, larger restoration test data set that comes with ground truth. Furthermore, we illustrate the usefulness of our test data for evaluation of a recent 3D restoration method based on symmetry analysis and also outline how the applicability of 3D retrieval techniques could be evaluated with respect to 3D restoration tasks. Finally, we discuss first results of an ongoing extension of our methodology to include object erosion processes by means of a physiochemical model simulating weathering effects.
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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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    Interactive Regression Lens for Exploring Scatter Plots
    (The Eurographics Association and John Wiley & Sons Ltd., 2017) Shao, Lin; Mahajan, Aishwarya; Schreck, Tobias; Lehmann, Dirk J.; Heer, Jeffrey and Ropinski, Timo and van Wijk, Jarke
    Data analysis often involves finding models that can explain patterns in data, and reduce possibly large data sets to more compact model-based representations. In Statistics, many methods are available to compute model information. Among others, regression models are widely used to explain data. However, regression analysis typically searches for the best model based on the global distribution of data. On the other hand, a data set may be partitioned into subsets, each requiring individual models. While automatic data subsetting methods exist, these often require parameters or domain knowledge to work with. We propose a system for visual-interactive regression analysis for scatter plot data, supporting both global and local regression modeling. We introduce a novel regression lens concept, allowing a user to interactively select a portion of data, on which regression analysis is run in interactive time. The lens gives encompassing visual feedback on the quality of candidate models as it is interactively navigated across the input data. While our regression lens can be used for fully interactive modeling, we also provide user guidance suggesting appropriate models and data subsets, by means of regression quality scores. We show, by means of use cases, that our regression lens is an effective tool for user-driven regression modeling and supports model understanding.