@inproceedings{birkeland10USpainting, title = {Ultrasound Painting of Vascular Tree}, author = {{\AA}smund Birkeland and Ivan Viola}, year = {2010}, booktitle = {Proceedings of Vision, Modeling, and Visualization (VMV 2010)}, abstract = {In treatment planning and surgical interventions, physicians and surgeons need information about the spatial extent of specific features and the surrounding structures. Previous techniques for extracting features, based on magnetic resonance imaging and computed tomography scans, can be slow and cumbersome and are rarely used by doctors. In this paper we will present a novel approach to extract features from tracked 2D ultrasound, in particular hypo-echoic regions such as blood vessels. Features are extracted during live examinations, removing the need for slow and cumbersome post-scan processes and interaction is based on the natural interaction techniques used by doctors during the examination. The ultrasound probe is utilized as a 3D brush, painting features in a 3D environment. The painting occurs during a regular examination, producing little extra interaction from the doctor. We will introduce a novel approach to extract hypo-echoic regions from an ultrasound image and track the regions from frame to frame. 3D models are then generated by storing the outline of the region as a 3D point cloud. Automatically detecting branching, this technique can handle complex structures, such as liver vessel trees, and track multiple regions simultaneously. During the examination, the point cloud is triangulated in real-time, enabling the doctor to examine the results live and discard areas which are unsatisfactory. To enable modifications of the extracted 3D models, we present how the ultrasound probe can be used as a interaction tool for fast point cloud editing.}, location = {Siegen, Germany}, pages = {163--170}, vid = {vids/birkeland10USpainting.html}, images = {images/birkeland10USpainting.jpg, images/birkeland10USpainting3.jpg, images/birkeland10USpainting1.jpg, images/birkeland10USpainting2.jpg}, thumbnails = {images/birkeland10USpainting_thumb.jpg, images/birkeland10USpainting3_thumb.jpg, images/birkeland10USpainting1_thumb.jpg, images/birkeland10USpainting2_thumb.jpg}, project = {illustrasound,medviz,illvis} } @inproceedings{lampe10differenceViews, title = {Visual Analysis of Multivariate Movement Data Using Interactive Difference Views}, author = {Ove Daae Lampe and Johannes Kehrer and Helwig Hauser}, year = {2010}, booktitle = {Proceedings of Vision, Modeling, and Visualization (VMV 2010)}, abstract = {Movement data consisting of a large number of spatio-temporal agent trajectories is challenging to visualize, especially when all trajectories are attributed with multiple variates. In this paper, we demonstrate the visual exploration of such movement data through the concept of interactive difference views. By reconfiguring the difference views in a fast and flexible way, we enable temporal trend discovery. We are able to analyze large amounts of such movement data through the use of a frequency-based visualization based on kernel density estimates (KDE), where it is also possible to quantify differences in terms of the units of the visualized data. Using the proposed techniques, we show how the user can produce quantifiable movement differences and compare different categorical attributes (such as weekdays, ship-type, or the general wind direction), or a range of a quantitative attribute (such as how two hours’ traffic compares to the average). We present results from the exploration of vessel movement data from the Norwegian Coastal Administration, collected by the Automatic Identification System (AIS) coastal tracking. There are many interacting patterns in such movement data, both temporal and other more intricate, such as weather conditions, wave heights, or sunlight. In this work we study these movement patterns, answering specific questions posed by Norwegian Coastal Administration on potential shipping lane optimizations.}, location = {Siegen, Germany}, pages = {315--322}, pdf = {pdfs/lampe10difference.pdf}, vid = {vids/lampe10difference.html}, pres = {pdfs/lampe10difference-presentation.pdf}, images = {images/lampe10difference1.jpg,images/lampe10difference3.jpg,images/lampe10difference2.jpg}, thumbnails = {images/lampe10difference1_thumb.jpg,images/lampe10difference3_thumb.jpg,images/lampe10difference2_thumb.jpg}, } @inproceedings{balabanian10a, title = {A}, author = {Jean-Paul Balabanian and M. Eduard Gr{\"o}ller}, year = {2010}, booktitle = {Scientific Visualization (Proc. Dagstuhl Seminar)}, series = {Dagstuhl Seminar Proceedings}, ISSN = {1862-4405}, abstract = {This paper describes the concept of A-space. A-space is the space where visualization algorithms reside. Every visualization algorithm is a unique point in A-space. Integrated visualizations can be interpreted as an interpolation between known algorithms. The void between algorithms can be considered as a visualization opportunity where a new point in A-space can be reconstructed and new integrated visualizations can be created.}, location = {Dagstuhl, Germany}, pages = {xx--xx}, images = {images/balabanian10a.jpg, images/balabanian10a2.jpg}, thumbnails = {images/balabanian10a_thumb.jpg, images/balabanian10a2_thumb.jpg}, URL = {http://www.cg.tuwien.ac.at/research/publications/2010/Balabanian-2010-PSDSV/} } @article{matkovic10modelView, title = {Interactive Visual Analysis of Multiple Simulation Runs using the Simulation Model View: Understanding and Tuning of an Electronic Unit Injector}, author = {Kresimir Matkovic and Denis Gracanin and Mario Jelovic and Andreas Ammer and Alan Lez and Helwig Hauser}, journal = {IEEE Transactions on Visualization and Computer Graphics}, volume = {16}, number = {6}, pages = {1449--1457 }, year = {2010}, event = {IEEE Visualization 2010}, location = {Salt Lake City, US}, abstract = {Multiple simulation runs using the same simulation model with different values of control parameters usually generate large data sets that capture the variational aspects of the behavior of the modeled and simulated phenomenon. We have identified a conceptual and visual gap between the simulation model behavior and the data set that makes data analysis more difficult than necessary. We propose a simulation model view that helps to bridge that gap by visually combining the simulation model description and the generated data. The simulation model view provides a visual outline of the simulation process and the corresponding simulation model. The view is integrated in a Coordinated Multiple Views (CMV) system. We use three levels of details to efficiently use the display area provided by the simulation model view. We collaborated with a domain expert and used the simulation model view on a problem in the automotive application domain, i.e., meeting the emission requirements for Diesel engines. One of the key components is a fuel injector unit so our goal was to understand and tune an electronic unit injector (EUI). We were mainly interested in understanding the model and how to tune it for three different operation modes: low emission, low consumption, and high power. Very positive feedback from the domain expert shows that the use of the simulation model view and the corresponding analysis procedures within a CMV system amount to an effective technique for interactive visual analysis of multiple simulation runs. We also developed new analysis procedures based on these results.}, URL = {http://dx.doi.org/10.1109/TVCG.2010.171}, images = {images/matkovic10model1.jpg, images/matkovic10model2.jpg, images/matkovic10model3.jpg}, thumbnails = {images/matkovic10model1_thumb.jpg, images/matkovic10model2_thumb.jpg, images/matkovic10model3_thumb.jpg}, } @article{bruckner10HVC, title = "Hybrid Visibility Compositing and Masking for Illustrative Rendering", author = "Stefan Bruckner and Peter Rautek and Ivan Viola and Mike Roberts and Mario Costa Sousa and M. Eduard Gr{\"o}ller", year = "2010", abstract = "In this paper, we introduce a novel framework for the compositing of interactively rendered 3D layers tailored to the needs of scientific illustration. Currently, traditional scientific illustrations are produced in a series of composition stages, combining different pictorial elements using 2D digital layering. Our approach extends the layer metaphor into 3D without giving up the advantages of 2D methods. The new compositing approach allows for effects such as selective transparency, occlusion overrides, and soft depth buffering. Furthermore, we show how common manipulation techniques such as masking can be integrated into this concept. These tools behave just like in 2D, but their influence extends beyond a single viewpoint. Since the presented approach makes no assumptions about the underlying rendering algorithms, layers can be generated based on polygonal geometry, volumetric data, point-based representations, or others. Our implementation exploits current graphics hardware and permits real-time interaction and rendering.", journal = {Computers \& Graphics - Special Issue on Illustrative Visualization}, pages = {361--369}, volume = {34}, number = {4}, images = {images/bruckner10hvc.jpg, images/bruckner10hvc2.jpg}, thumbnails = {images/bruckner10hvc_thumb.jpg, images/bruckner10hvc2-thumb.jpg}, URL = {http://dx.doi.org/10.1016/j.cag.2010.04.003}, project = {illustrasound,medviz,illvis} } @article{viola10editorial, title = {Editorial note for special section on illustrative visualization}, author = {Ivan Viola and Helwig Hauser and David Ebert}, year = {2010}, journal = {Computers \& Graphics}, pages = {335--336}, volume = {34}, number = {4}, images = {images/viola10editorial.jpg}, thumbnails = {images/viola10editorial_thumb.jpg}, URL = {http://dx.doi.org/10.1016/j.cag.2010.05.011}, project = {illvis} } @inproceedings{angelelli10guided, title = "Guided Visualization of Ultrasound Image Sequences", author = "Paolo Angelelli and Ivan Viola and Kim Nylund and Odd Helge Gilja and Helwig Hauser", year = "2010", booktitle = {Proceedings of Eurographics Workshop on Visual Computing for Biology and Medicine (VCBM)}, abstract = {Ultrasonography allows informative and expressive real time examinations of patients. Findings are usually reported as printouts, screen shots and video sequences. However, in certain scenarios, the amount of imaged ultrasound data is considerable or it is challenging to detect the anatomical features of interest. Post-examination access to the information present in the data is, therefore, cumbersome. The examiner must, in fact, review entire video sequences or risk to lose relevant information by reducing the examination to single screen shot and printouts. In this paper we propose a novel post-processing pipeline for guided visual exploration of ultrasound video sequences, to allow easier and richer exploration and analysis of the data. We demonstrate the usefulness of this approach by applying it to a liver examination case, showing easier and quicker ultrasound image selection and data exploration.}, pages = {125--132}, location = {Leipzig, Germany}, images = {images/angelelli10guided0.jpg, images/angelelli10guided3.jpg, images/angelelli10guided2.jpg, images/angelelli10guided4.jpg}, thumbnails = {images/angelelli10guided0_thumb.jpg, images/angelelli10guided3_thumb.jpg, images/angelelli10guided2_thumb.jpg, images/angelelli10guided4_thumb.jpg}, pdf = {pdfs/angelelli2010usvideovis.pdf}, project = {illustrasound,medviz,illvis}, vid = {http://www.ii.uib.no/vis/publications/vids/angelelli10DOISound.wmv}, } @article{kehrer10moments, author = {Johannes Kehrer and Peter Filzmoser and Helwig Hauser}, title = {Brushing Moments in Interactive Visual Analysis}, year = {2010}, month = {june}, abstract = {We present a systematic study of opportunities for the interactive visual analysis of multi-dimensional scientific data. This is based on the integration of statistical aggregations along selected data dimensions in a framework of coordinated multiple views (with linking and brushing). Traditional and robust estimates of the four statistical moments (mean, variance, skewness, and kurtosis) as well as measures of outlyingness are integrated in an iterative visual analysis process. Brushing particular statistics, the analyst can investigate data characteristics such as trends and outliers. We present a categorization of beneficial combinations of attributes in 2D scatterplots: (a) k-th vs. (k+1)-th statistical moment of a traditional or robust estimate, (b) traditional vs. robust version of the same moment, (c) two different robust estimates of the same moment. We propose selected view transformations to iteratively construct this multitude of informative views as well as to enhance the depiction of the statistical properties in the scatterplots. In the framework, we interrelate the original distributional data and the aggregated statistics, which allows the analyst to work with both data representations simultaneously. We demonstrate our approach in the context of two visual analysis scenarios of multi-run climate simulations.}, journal = {Computer Graphics Forum}, event = "EuroVis 2010", volume = {29}, number = {3}, pages = {813--822}, location = "Bordeaux, France", pres = {pdfs/kehrer10moments-presentation.pdf}, vid = {vids/kehrer10moments.html}, images = {images/kehrer10moments.jpg, images/kehrer10moments1.jpg, images/kehrer10moments2.jpg}, thumbnails = {images/kehrer10moments_thumb.jpg, images/kehrer10moments1_thumb.jpg, images/kehrer10moments2_thumb.jpg}, URL = {http://dx.doi.org/10.1111/j.1467-8659.2009.01697.x} } @article{solteszova10multidirectional, author = {Veronika \v{S}olt{\'e}szov{\'a} and Daniel Patel and Stefan Bruckner and Ivan Viola}, title = {A Multidirectional Occlusion Shading Model for Direct Volume Rendering}, year = {2010}, month = {june}, abstract = {In this paper, we present a novel technique which simulates directional light-scattering for more realistic interactive visualization of volume data. Our method extends the recent directional occlusion shading model by enabling light-source positioning with practically no performance penalty. Light transport is approximated using a tilted cone-shaped function which leaves elliptic footprints in the opacity buffer during slice-based volume rendering. We perform an incremental blurring operation on the opacity buffer for each slice in front-to-back order. This buffer is then used to define the degree of occlusion for the subsequent slice. Our method is capable of generating high-quality soft shadowing effects, allows interactive modification of all illumination and rendering parameters, and requires no pre-computation.}, journal = {Computer Graphics Forum}, event = "EuroVis 2010", volume = {29}, number = {3}, pages = {883--891}, location = "Bordeaux, France", vid = {vids/solteszova10multi.html}, images = {images/solteszova10multi.jpg, images/solteszova10multi1.jpg, images/solteszova10multi2.jpg, images/solteszova10multi3.jpg}, thumbnails = {images/solteszova10multi_thumb.jpg, images/solteszova10multi1_thumb.jpg, images/solteszova10multi2_thumb.jpg, images/solteszova10multi3_thumb.jpg}, URL = {http://dx.doi.org/10.1111/j.1467-8659.2009.01695.x}, project = {illustrasound,medviz,illvis} } @article{fuchs10lagrangian, author = {Raphael Fuchs and Jan Kemmler and Benjamin Schindler and Jürgen Waser and Filip Sadlo and Helwig Hauser and Ronald Peikert}, title = {Toward a Lagrangian Vector Field Topology}, year = {2010}, month = {june}, abstract = {In this paper we present an extended critical point concept which allows us to apply vector field topology in the case of unsteady flow. We propose a measure for unsteadiness which describes the rate of change of the velocities in a fluid element over time. This measure allows us to select particles for which topological properties remain intact inside a finite spatio-temporal neighborhood. One benefit of this approach is that the classification of critical points based on the eigenvalues of the Jacobian remains meaningful. In the steady case the proposed criterion reduces to the classical definition of critical points. As a first step we show that finding an optimal Galilean frame of reference can be obtained implicitly by analyzing the acceleration field. In a second step we show that this can be extended by switching to the Lagrangian frame of reference. This way the criterion can detect critical points moving along intricate trajectories. We analyze the behavior of the proposed criterion based on two analytical vector fields for which a correct solution is defined by their inherent symmetries and present results for numerical vector fields.}, journal = {Computer Graphics Forum}, event = "EuroVis 2010", volume = {29}, number = {3}, pages = {1163--1172}, location = "Bordeaux, France", images = {images/fuchs10lagrangian2.jpg, images/fuchs10lagrangian.jpg}, thumbnails = {images/fuchs10lagrangian2_thumb.jpg, images/fuchs10lagrangian_thumb.jpg}, URL = {http://dx.doi.org/10.1111/j.1467-8659.2009.01686.x}, project = {semseg} } @inproceedings{matkovic10car, title = "Interactive Visual Analysis of Families of Surfaces: An Application to Car Race and Car Setup", author = "Kresimir Matkovic and Denis Gracanin and R. Splechtna and Helwig Hauser", year = "2010", booktitle = {Proceedings of the Internat. Symp. on Visual Analytics Science and Technology (EuroVAST 2010)}, abstract = {Modern simulations often produce time series, or even functions of two variables as outputs for single attributes. Such complex data require carefully chosen and designed analysis procedures and the corresponding data model. The use of previously developed curve and surface views provides strong support for visual exploration and analysis of complex data. In this paper we describe how interactive visual analysis can support users in getting insight into complex data. The case study, based on TORCS 3D racing cars simulator, illustrates our approach and its successful application to a real world problem. The analysis of the car parameters and driving performances during races provides an insight and explanation for race results. That insight is then used to fine-tune car parameters to achieve better driving performance.}, pages = {--}, location = "Bordeaux, France", images = {images/matkovic10car.jpg, images/matkovic10car2.jpg}, thumbnails = {images/matkovic10car_thumb.jpg, images/matkovic10car2_thumb.jpg}, } @inproceedings{balabanian10hierarchical, title = "Interactive Illustrative Visualization of Hierarchical Volume Data", author = "Jean-Paul Balabanian and Ivan Viola and M. Eduard Gr{\"o}ller", year = "2010", booktitle = {Proceedings of Graphics Interface (best student paper award)}, abstract = {In scientific visualization the underlying data often has an inherent abstract and hierarchical structure. Therefore, the same dataset can simultaneously be studied with respect to its characteristics in the three-dimensional space and in the hierarchy space. Often both characteristics are equally important to convey. For such scenarios we explore the combination of hierarchy visualization and scientific visualization, where both data spaces are effectively integrated. We have been inspired by illustrations of species evolutions where hierarchical information is often present. Motivated by these traditional illustrations, we introduce integrated visualizations for hierarchically organized volumetric datasets. The hierarchy data is displayed as a graph, whose nodes are visually augmented to depict the corresponding 3D information. These augmentations include images due to volume raycasting, slicing of 3D structures, and indicators of structure visibility from occlusion testing. New interaction metaphors are presented that extend visualizations and interactions, typical for one visualization space, to control visualization parameters of the other space. Interaction on a node in the hierarchy influences visual representations of 3D structures and vice versa. We integrate both the abstract and the scientific visualizations into one view which avoids frequent refocusing typical for interaction with linked-view layouts. We demonstrate our approach on different volumetric datasets enhanced with hierarchical information.}, pages = {xx--xx}, location = {Ottawa, Canada}, vid = {vids/balabanian10hierarchical.html}, images = {images/balabanian09thesis1.jpg, images/balabanian10hierarchical2.jpg, images/balabanian10hierarchical1.jpg, images/balabanian10hierarchical3.jpg}, thumbnails = {images/balabanian09thesis1_thumb.jpg, images/balabanian10hierarchical2_thumb.jpg, images/balabanian10hierarchical1_thumb.jpg, images/balabanian10hierarchical3_thumb.jpg}, URL = {http://www.cg.tuwien.ac.at/research/publications/2010/Balabanian-2010-IIV/}, project = {medviz,illvis} } @proceedings{hauser10sccg, title = {Proceedings of the Spring Conference on Computer Graphics (SCCG 2010)}, editor = {Helwig Hauser and Reinhard Klein}, publisher = {Comenius University, Bratislava}, images = {images/hauser10sccg.jpg}, thumbnails = {images/hauser10sccg_thumb.jpg}, year = {2010} } @inproceedings{florek10kde, author = {Martin Florek and Helwig Hauser}, title ={Quantitative data visualization with interactive KDE surfaces}, year = {2010}, booktitle = {Proceedings of the Spring Conference on Computer Graphics (SCCG 2010)}, location = {Budmerice, Slovakia}, pages = {--}, images = {images/florek10kde.jpg}, thumbnails = {images/florek10kde_thumb.jpg}, abstract = {Kernel density estimation (KDE) is an established statistical concept for assessing the distributional characteristics of data that also has proven its usefulness for data visualization. In this work, we present several enhancements to such a KDE-based visualization that aim (a) at an improved specificity of the visualization with respect to the communication of quantitative information about the data and its distribution and (b) at an improved integration of such a KDE-based visualization in an interactive visualization setting, where, for example, linking and brushing is easily possible both from and to such a visualization. With our enhancements to KDE-based visualization, we can extend the utilization of this great statistical concept in the context of interactive visualization.}, month = {May}, url = {http://dx.doi.org/10.1145/1925059.1925068}, } @inproceedings{pobitzer10topology, title = "On the Way Towards Topology-Based Visualization of Unsteady Flow - the State of the Art", author = "Armin Pobitzer and Ronald Peikert and Raphael Fuchs and Benjamin Schindler and Alexander Kuhn and Holger Theisel and Kresimir Matkovic and Helwig Hauser", year = "2010", booktitle = {EuroGraphics 2010 State of the Art Reports (STARs)}, abstract = "Vector fields are a common concept for the representation of many different kinds of flow phenomena in science and engineering. Topology-based methods have shown their convenience for visualizing and analyzing steady flow but a counterpart for unsteady flow is still missing. However, a lot of good and relevant work has been done aiming at such a solution. We give an overview of the research done on the way towards topology-based visualization of unsteady flow, pointing out the different approaches and methodologies involved as well as their relation to each other, taking classical (i.e. steady) vector field topology as our starting point. Particularly, we focus on Lagrangian Methods, Space-Time Domain Approaches, Local Methods, and Stochastic and Multi-Field Approaches. Furthermore, we illustrated our review with practical examples for the different approaches.", pages = {137--154}, event = "EuroGraphics 2010", location = {Norrk{\"o}ping, Sweden}, pres = {pdfs/pobitzer10topology-presentation.pdf}, images = {images/pobitzer10topology.jpg,}, thumbnails = {images/pobitzer10topology_thumb.jpg}, project = {semseg}, } @article{ladstaedter10explorationClimateData, title = {Exploration of Climate Data Using Interactive Visualization}, author = {Florian Ladst{\"a}dter and Andrea K. Steiner and Bettina C. Lackner and Barbara Pirscher and Gottfried Kirchengast and Johannes Kehrer and Helwig Hauser and Philipp Muigg and Helmut Doleisch}, journal = {Journal of Atmospheric and Oceanic Technology}, volume = {27}, number = {4}, pages = {667--679}, month = {April}, year = {2010}, abstract = {In atmospheric and climate research, the increasing amount of data available from climate models and observations provides new challenges for data analysis. We present interactive visual exploration as an innovative approach to handle large datasets. Visual exploration does not require any previous knowledge about the data as is usually the case with classical statistics. It facilitates iterative and interactive browsing of the parameter space in order to quickly understand the data characteristics, to identify deficiencies, to easily focus on interesting features, and to come up with new hypotheses about the data. These properties extend the common statistical treatment of data, and provide a fundamentally different approach. We demonstrate the potential of this technology by exploring atmospheric climate data from different sources including reanalysis datasets, climate models, and radio occultation satellite data. Results are compared to those from classical statistics revealing the complementary advantages of visual exploration. Combining both, the analytical precision of classical statistics and the holistic power of interactive visual exploration, the usual work flow of studying climate data can be enhanced.}, images = {images/ladstaedter10exploration.jpg, images/ladstaedter10exploration1.jpg, images/ladstaedter10exploration3.jpg, images/ladstaedter10exploration2.jpg}, thumbnails = {images/ladstaedter10exploration_thumb.jpg, images/ladstaedter10exploration1_thumb.jpg, images/ladstaedter10exploration3_thumb.jpg, images/ladstaedter10exploration2_thumb.jpg}, URL = {http://dx.doi.org/10.1175/2009JTECHA1374.1} } @inproceedings{patel10horizontExtraction, title = "Seismic Volume Visualization for Horizon Extraction", author = "Daniel Patel and Stefan Bruckner and Ivan Viola and Meister Eduard Gr{\"o}ller", year = "2010", booktitle = {Proceedings of the IEEE Pacific Visualization Symposium 2010}, abstract = "Seismic horizons indicate change in rock properties and are central in geoscience interpretation. Traditional interpretation systems involve time consuming and repetitive manual volumetric seeding for horizon growing. We present a novel system for rapidly interpreting and visualizing seismic volumetric data. First we extract horizon surface-parts by preprocessing the seismic data. Then during interaction the user can assemble in realtime the horizon parts into horizons. Traditional interpretation systems use gradient-based illumination models in the rendering of the seismic volume and polygon rendering of horizon surfaces. We employ realtime gradientfree forward-scattering in the rendering of seismic volumes yielding results similar to high-quality global illumination. We use an implicit surface representation of horizons allowing for a seamless integration of horizon rendering and volume rendering. We present a collection of novel techniques constituting an interpretation and visualization system highly tailored to seismic data interpretation.", pages = {73--80}, month = {March}, location = {Taipei, Taiwan}, images = {images/patel10horizont.jpg, images/patel10horizont3.jpg, images/patel10horizont4.jpg, images/patel10horizont2.jpg}, thumbnails = {images/patel10horizont_thumb.jpg, images/patel10horizont3_thumb.jpg, images/patel10horizont4_thumb.jpg, images/patel10horizont2_thumb.jpg}, URL = {http://www.cg.tuwien.ac.at/research/publications/2010/patel-2010-SVV/}, project = {geoillustrator,illvis} } @inproceedings{haidacher10statisticalTF, title = "Volume Visualization based on Statistical Transfer-Function Spaces", author = "Martin Haidacher and Daniel Patel and Stefan Bruckner and Armin Kanitsar and Meister Eduard Gr{\"o}ller", year = "2010", booktitle = {Proceedings of the IEEE Pacific Visualization Symposium 2010}, abstract = "It is a difficult task to design transfer functions for noisy data. In traditional transfer-function spaces, data values of different materials overlap. In this paper we introduce a novel statistical transfer-function space which in the presence of noise, separates different materials in volume data sets. Our method adaptively estimates statistical properties, i.e. the mean value and the standard deviation, of the data values in the neighborhood of each sample point. These properties are used to define a transfer-function space which enables the distinction of different materials. Additionally, we present a novel approach for interacting with our new transfer-function space which enables the design of transfer functions based on statistical properties. Furthermore, we demonstrate that statistical information can be applied to enhance visual appearance in the rendering process. We compare the new method with 1D, 2D, and LH transfer functions to demonstrate its usefulness.", pages = {17--24}, month = {March}, location = {Taipei, Taiwan}, images = {images/haidacher10statTF.jpg, images/haidacher10statTF2.jpg, images/haidacher10statTF3.jpg}, thumbnails = {images/haidacher10statTF_thumb.jpg, images/haidacher10statTF2_thumb.jpg, images/haidacher10statTF3_thumb.jpg}, URL = {http://www.cg.tuwien.ac.at/research/publications/2010/haidacher_2010_statTF/}, } @article{piringer09hds, title = {Hierarchical Difference Scatterplots: Interactive Visual Analysis of Data Cubes}, author = {Harald Piringer and Matthias Buchetics and Helwig Hauser and M. Eduard Gr{\"o}ller}, year = {2010}, abstract = {Data cubes as employed by On-Line Analytical Processing (OLAP) play a key role in many application domains. The analysis typically involves to compare categories of different hierarchy levels with respect to size and pivoted values. Most existing visualization methods for pivoted values, however, are limited to single hierarchy levels. The main contribution of this paper is an approach called Hierarchical Difference Scatterplot (HDS). A HDS allows for relating multiple hierarchy levels and explicitly visualizes differences between them in the context of the absolute position of pivoted values. We discuss concepts of tightly coupling HDS to other types of tree visualizations and propose the integration in a setup of multiple views, which are linked by interactive queries on the data. We evaluate our approaches by analyzing social survey data in collaboration with a domain expert.}, pages = {49--58}, journal = {ACM SIGKDD Explorations Newsletter}, volume = {11}, number = {2}, images = {images/piringer09hds.jpg, images/piringer09hds2.jpg}, thumbnails = {images/piringer09hds_thumb.jpg, images/piringer09hds2_thumb.jpg}, URL = {http://dx.doi.org/10.1145/1809400.1809408}, } @misc{hauser10interactiveStoryTelling, author = {Helwig Hauser}, title ={Interactive Story Telling for Presentation with Visualization}, year = {2010}, month = {December 17}, howpublished = {Talk at CMR Forum}, location = {Christian Michelsen Research, Bergen, www.CMR.no}, images = {images/hauser10interactiveStoryTelling.png}, thumbnails = {images/hauser10interactiveStoryTelling_thumb.jpg}, pres = {pdfs/hauser10interactiveStoryTelling.pdf} } @misc{hauser10storyTelling, author = {Helwig Hauser}, title ={Story Telling for Visualization}, year = {2010}, month = {November 1}, howpublished = {Talk at Story Telling workshop 2010, UC Davis}, location = {Davis, CA}, images = {images/hauser10storyTelling.png}, thumbnails = {images/hauser10storyTelling_thumb.jpg}, pres = {pdfs/hauser10storyTelling.pdf}, } @misc{hauser10brainPerfusion, author = {Helwig Hauser and Sylvia Glaßer}, title ={Visualizing Statistics of Brain Perfusion Data}, year = {2010}, month = {October 8}, howpublished = {Talk in the MedViz Seminar Series}, location = {Bergen, Norway}, images = {images/hauser10brainPerf.png}, thumbnails = {images/hauser10brainPerf_thumb.jpg}, abstract = {Following up earlier cooperative research work with the University of Magdeburg in Germany (with Steffen Oeltze et al.), we are pursuing a new study of perfusion data (this time with Sylvia Glasser et al.) based on statistical tools (such as correlation analysis and principal component analysis) and interactive visual analysis. Shape parameters of concentration time curves are investigated (as well as other quantities that we derived from them) to analyze brain regions that are affected by tumors. Low and high grade tumors are compared. In this talk, a short update on the current state of this research is presented, more results are expected during the weeks and months to come.}, pres = {pdfs/hauser10brainPerfusion-pres.pdf} } @misc{hauser10levelsOfComplexity, author = {Helwig Hauser}, title = {Interactive Visual Analysis with different levels of complexity}, year = {2010}, month = {June 24}, howpublished = {Invited talk at TU Delft}, location = {Delft, The Netherlands}, images = {images/hauser10levelsOfComplexity.jpg}, thumbnails = {images/hauser10levelsOfComplexity_thumb.png}, abstract = {Interactive visual data exploration and analysis is a powerful methodology for enabling insight into complex and also large data. The iterative process of visualization and interaction (and back to visualization, aso.) can be seen as a visual dialog between the user and the data. Thereby, powerful data analysis schemes are enabled such as a step-by-step information drill-down, steered by the user’s perception, cognition, and knowledge. In this talk, we look at different levels of this methodology (in the sense of levels of complexity), starting at the first level of ``show \& brush'' continuing then via ``relational analysis'' to a third level that we call ``complex analysis.'' The hypothesis is stated that it indeed is useful to have these different levels of complexity for interactive visual data analysis: a large share of all addressed problems can be satisfyingly solved with the ``simple'' level of ``show & brush,'' while the more complex levels of this methodology are only paying off in special cases. Along with a characterization of these levels, we also take a look at a number of illustrative examples.}, pres = {pdfs/hauser10levelsOfComplexity-pres.pdf} } @misc{kehrer10edaVis, author = {Johannes Kehrer}, title ={Selected Opportunities for Integrating Statistics and Visualization in Multi-dimensional Data Exploration}, year = {2010}, month = {May 27}, howpublished = {Talk at EDAVis: Workshop on Exploratory Data Analysis and Visualisation}, location = {Vienna, Austria}, abstract = {Visualization and statistics both facilitate the understanding of complex data characteristics, and there is a long history of relations between the two fields. Traditional approaches for data analysis often consider passive visualizations of statistical data properties. Interactive visual analysis, however, as addressed in this talk, allows the iterative exploration and analysis of data in a guided human–computer dialog. Graphical representations of the data and well-proven interaction mechanisms are used to concurrently show, explore, and analyze complex (i.e., time-dependent, multi-variate, and/or multi-dimensional) data. Interesting subsets of the data are interactively selected (brushed) directly on the screen, the relations are investigated in other linked views (including 2D scatterplots, histograms, function graph views, parallel coordinates, but also 3D views of volumetric data). In recent work, we have studied the integration of large amounts of locally aggregated statistical data properties as well as measures of outlyingness in an interactive visual analysis process. The approach is demonstrated on the visual analysis of multi-dimensional climate data. A discussion of possibilities explains how a further combination of interactive statistical plots and proven interaction schemes from visualization research shows great potential for future research.}, images = {images/kehrer10edavis.jpg}, thumbnails = {images/kehrer10edavis_thumb.jpg}, } @misc{hauser10visualDialog, author = {Helwig Hauser}, title ={Interactive Visualization as a Visual Dialog for Data Investigation}, year = {2010}, month = {April 13}, howpublished = {Talk at Visualiseringsdag Stockholm}, location = {Stockholm, Sweden}, images = {images/hauser10visualDialog.png}, thumbnails = {images/hauser10visualDialog_thumb.jpg}, pres = {pdfs/hauser10visualDialog.pdf} }