@article{patel08seismicAnalyzer, author = {Daniel Patel and Christopher Giertsen and John Thurmond and John Gjelberg and M. Eduard Gr{\"o}ller}, title = {The Seismic Analyzer: Interpreting and Illustrating 2D Seismic Data}, year = {2008}, abstract = {We present a toolbox for quickly interpreting and illustrating 2D slices of seismic volumetric reflection data. Searching for oil and gas involves creating a structural overview of seismic reflection data to identify hydrocarbon reservoirs. We improve the search of seismic structures by precalculating the horizon structures of the seismic data prior to interpretation. We improve the annotation of seismic structures by applying novel illustrative rendering algorithms tailored to seismic data, such as deformed texturing and line and texture transfer functions. The illustrative rendering results in multi-attribute and scale invariant visualizations where features are represented clearly in both highly zoomed in and zoomed out views. Thumbnail views in combination with interactive appearance control allows for a quick overview of the data before detailed interpretation takes place. These techniques help reduce the work of seismic illustrators and interpreters.}, month = {Oct}, journal = {IEEE Transactions on Visualization and Computer Graphics (IEEE TVCG)}, event = {IEEE Visualization 2008}, location = {Columbus, Ohio, USA}, volume = {14}, number = {6}, pages = {1571--1578}, URL = {http://dx.doi.org/10.1109/TVCG.2008.170}, pres = {pdfs/patel08vis-presentation.pdf}, vid = {vids/patel08vis.html}, images = {images/patel08seismic.png, images/patel08seismic4.jpg, images/patel08seismic3.jpg, images/patel08seismic1.png}, thumbnails = {images/patel08seismic_thumb.png, images/patel08seismic4_thumb.jpg, images/patel08seismic3_thumb.jpg, images/patel08seismic1_thumb.png}, } @article{kehrer08hypothesisGeneration, author = {Johannes Kehrer and Florian Ladst{\"a}dter and Philipp Muigg and Helmut Doleisch and Andrea Steiner and Helwig Hauser}, title = {Hypothesis Generation in Climate Research with Interactive Visual Data Exploration}, year = {2008}, abstract = {One of the most prominent topics in climate research is the investigation, detection, and allocation of climate change. In this paper, we aim at identifying regions in the atmosphere (e.g., certain height layers) which can act as sensitive and robust indicators for climate change. We demonstrate how interactive visual data exploration of large amounts of multi-variate and time-dependent climate data enables the steered generation of promising hypotheses for subsequent statistical evaluation. The use of new visualization and interaction technology -- in the context of a coordinated multiple views framework -- allows not only to identify these promising hypotheses, but also to efficiently narrow down parameters that are required in the process of computational data analysis. Two datasets, namely an ECHAM5 climate model run and the ERA-40 reanalysis incorporating observational data, are investigated. Higher-order information such as linear trends or signal-to-noise ratio is derived and interactively explored in order to detect and explore those regions which react most sensitively to climate change. As one conclusion from this study, we identify an excellent potential for usefully generalizing our approach to other, similar application cases, as well.}, month = {Oct}, journal = {IEEE Transactions on Visualization and Computer Graphics (IEEE TVCG)}, event = {IEEE Visualization 2008}, location = {Columbus, Ohio, USA}, volume = {14}, number = {6}, pages = {1579--1586}, URL = {http://dx.doi.org/10.1109/TVCG.2008.139}, vid = {vids/kehrer08hypothesis.html}, pres = {pdfs/kehrer08vis-presentation.pdf}, images = {images/kehrer08vis01.jpg, images/kehrer08vis03.jpg, images/kehrer08vis04.png}, thumbnails = {images/kehrer08vis01_thumb.jpg, images/kehrer08vis03_thumb.jpg, images/kehrer08vis04_thumb.png}, } @article{matkovic08visualSteering, author = {Kresimir Matkovic and Denis Gracanin and Mario Jelovic and Helwig Hauser}, title = {Interactive Visual Steering –- Rapid Visual Prototyping of a Common Rail Injection System}, year = {2008}, abstract = {Interactive steering with visualization has been a common goal of the visualization research community for twenty years, but it is rarely ever realized in practice. In this paper we describe a successful realization of a tightly coupled steering loop, integrating new simulation technology and interactive visual analysis in a prototyping environment for automotive industry system design. Due to increasing pressure on car manufacturers to meet new emission regulations, to improve efficiency, and to reduce noise, both simulation and visualization are pushed to their limits. Automotive system components, such as the powertrain system or the injection system, have an increasing number of parameters, and new design approaches are required. It is no longer possible to optimize such a system solely based on experience or forward optimization. By coupling interactive visualization with the simulation back-end (computational steering), it is now possible to quickly prototype a new system, starting from a non-optimized initial prototype and the corresponding simulation model. The prototyping continues through the refinement of the simulation model, of the simulation parameters and through trial-and-error attempts to an optimized solution. The ability to early see the first results from a multidimensional simulation space -- thousands of simulations are run for a multidimensional variety of input parameters -- and to quickly go back into the simulation and request more runs in particular parameter regions of interest significantly improves the prototyping process and provides a deeper understanding of the system behavior. The excellent results which we achieved for the common rail injection system strongly suggest that our approach has a great potential of being generalized to other, similar scenarios.}, month = {Oct}, journal = {IEEE Transactions on Visualization and Computer Graphics (IEEE TVCG)}, event = {IEEE Visualization 2008}, location = {Columbus, Ohio, USA}, volume = {14}, number = {6}, pages = {1699--1706}, URL = {http://dx.doi.org/10.1109/TVCG.2008.145}, images = {images/matkovic08vis.png, images/matkovic08vis1.png, images/matkovic08vis3.png, images/matkovic08vis4.png}, thumbnails = {images/matkovic08vis_thumb.png, images/matkovic08vis1_thumb.png, images/matkovic08vis3_thumb.png, images/matkovic08vis4_thumb.png}, } @article{freiler08setTyped, author = {Wolfgang Freiler and Kresimir Matkovic and Helwig Hauser}, title = {Interactive Visual Analysis of Set-Typed Data}, year = {2008}, abstract = {While it is quite typical to deal with attributes of different data types in the visualization of heterogeneous and multivariate datasets, most existing techniques still focus on the most usual data types such as numerical attributes or strings. In this paper we present a new approach to the interactive visual exploration and analysis of data that contains attributes which are of set type. A set-typed attribute of a data item -- like one cell in a table -- has a list of n>=0 elements as its value. We present the set’o’gram as a new visualization approach to represent data of set type and to enable interactive visual exploration and analysis. We also demonstrate how this approach is capable to help in dealing with datasets that have a larger number of dimensions (more than a dozen or more), especially also in the context of categorical data. To illustrate the effectiveness of our approach, we present the interactive visual analysis of a CRM dataset with data from a questionnaire on the education and shopping habits of about 90000 people.}, month = {Oct}, journal = {IEEE Transactions on Visualization and Computer Graphics (IEEE TVCG)}, event = {IEEE Information Visualization 2008}, location = {Columbus, Ohio, USA}, volume = {14}, number = {6}, pages = {1340--1347}, URL = {http://dx.doi.org/10.1109/TVCG.2008.144}, images = {images/freiler08setTyped.png, images/freiler08setTyped1.png, images/freiler08setTyped2.png}, thumbnails = {images/freiler08setTyped_thumb.png, images/freiler08setTyped1_thumb.png, images/freiler08setTyped2_thumb.png}, } @inproceedings{viola08illustrasound, author = {Ivan Viola and Kim Nylund and Ola Kristoffer {\O}ye and Dag Magne Ulvang and Odd Helge Gilja and Helwig Hauser}, title = {Illustrated Ultrasound for Multimodal Data Interpretation of Liver Examinations}, year = {2008}, month = {Oct}, booktitle = {Proceedings of Eurographics Workshop on Visual Computing in Biomedicine}, pages = {125--133}, vid = {vids/viola08illustrasound.avi}, images = {images/viola08illustrasound.jpg, images/viola08illustrasound1.jpg, images/viola08illustrasound2.jpg, images/viola08illustrasound3.jpg}, thumbnails = {images/viola08illustrasound_thumb.jpg, images/viola08illustrasound1_thumb.jpg, images/viola08illustrasound2_thumb.jpg, images/viola08illustrasound3_thumb.jpg}, location = {Delft, The Netherlands}, abstract = {Traditional visualization of real-time 2D ultrasound data is difficult to interpret, even for experienced medical personnel. To make the interpretation during the education phase easier, we enhance the visualization during liver examinations with an abstracted depiction of relevant anatomical structures, here denoted as illustrated ultrasound. The specifics of enhancing structures are available through an interactively co-registered computed tomography, which has been enhanced by semantic information. To assist the orientation in the liver, we partition the liver into Couinaud segments. They are defined in a rapid segmentation process based on linked 2D slice views and 3D exploded views. The semantics are interactively related from the co-registered modality to the real-time ultrasound via co-registration. During the illustrated ultrasound examination training we provide visual enhancements that depict which liver segments are intersected by the ultrasound slice.}, URL = {http://www.ii.uib.no/vis/team/viola/_pdfs/viola_2008_vcbm.pdf}, project = {illvis,illustrasound,medviz}, } @inproceedings{balabanian08hierarchical, title = "Hierarchical Volume Visualization of Brain Anatomy", author = "Jean-Paul Balabanian and Martin Ystad and Ivan Viola and Arvid Lundervold and Helwig Hauser and M. Eduard Gr{\"o}ller", year = "2008", pages = "313--322", month = {oct}, booktitle = "Proceeding of Vision, Modeling and Visualization (VMV 2008)", isbn = "978-3-89838-609-8", location = "Konstanz, Germany", abstract = {Scientific data-sets often come with an inherent hierarchical structure such as functional substructures within organs. In this work we propose a new visualization approach for volume data which is augmented by the explicit representation of hierarchically structured data. The volumetric structures are organized in an interactive hierarchy view. Seamless zooming between data visualization, with volume rendering, and map viewing, for orientation and navigation within the hierarchy, facilitates deeper insight on multiple levels. The map shows all structures, organized in multiple hierarchy levels. Focusing on a selected node allows a visual analysis of a substructure as well as identifying its location in the hierarchy. The visual style of the node in focus, its parent and child nodes are automatically adapted during interaction to emphasize the embedding in the hierarchy. The hierarchy view is linked to a traditional tree view. The value of this new visualization approach is demonstrated on segmented MRI brain data consisting of hundreds of cortical and sub-cortical structures.}, images = {images/balabanian08hierarchical1.jpg, images/balabanian08hierarchical2.jpg, images/balabanian08hierarchical3.jpg, vids/balabanian08hierarchical.wmv}, thumbnails = {images/balabanian08hierarchical1_thumb.jpg, images/balabanian08hierarchical2_thumb.jpg, images/balabanian08hierarchical3_thumb.jpg, images/wmv_thumb.png}, URL = {http://www.cg.tuwien.ac.at/research/publications/2008/balabanian-2008-hvv/}, vid = {vids/balabanian08hierarchical.wmv} project = {illvis,medviz}, } @article{rautek08illustrative, author = {Peter Rautek and Stefan Bruckner and M. Eduard Gr{\"o}ller and Ivan Viola}, title = {Illustrative Visualization: New Technology or Useless Tautology?}, journal = {SIGGRAPH Comput. Graph.}, volume = {42}, number = {3}, year = {2008}, doi = {http://doi.acm.org/10.1145/1408626.1408633}, URL = {http://doi.acm.org/10.1145/1408626.1408633}, publisher = {ACM}, address = {New York, NY, USA}, images = {images/rautek08illustrative.jpg}, thumbnails = {images/rautek08illustrative_thumb.jpg}, project = {illvis}, } @inproceedings{ruiz08similarity, title = {Similarity-based Exploded Views}, author = {Marc Ruiz and Ivan Viola and Imma Boada and Stefan Bruckner and Miquel Feixas and Mateu Sbert}, year = {2008}, pages = {154--165}, month = {Aug}, booktitle = {Proceedings of 8th International Symposium on Smart Graphics}, location = {Rennes, France}, abstract = {Exploded views are often used in illustration to overcome the problem of occlusion when depicting complex structures. In this paper, we propose a volume visualization technique inspired by exploded views that partitions the volume into a number of parallel slabs and shows them apart from each other. The thickness of slabs is driven by the similarity between partitions. We use an information-theoretic technique for the generation of exploded views. First, the algorithm identifies the viewpoint from which the structure is the highest. Then, the partition of the volume into the most informative slabs for exploding is obtained using two complementary similarity-based strategies. The number of slabs and the similarity parameter are freely adjustable by the user.}, URL = {http://www.cg.tuwien.ac.at/research/publications/2008/ruiz-2008-SEV/}, images = {images/ruiz08similarity.jpg, images/ruiz08similarity1.jpg}, thumbnails = {images/ruiz08similarity_thumb.jpg, images/ruiz08similarity1_thumb.jpg}, project = {illvis,medviz}, } @inproceedings{ruiz08obscurance, title = {Obscurance-based Volume Rendering Framework}, author = {Marc Ruiz and Imma Boada and Ivan Viola and Stefan Bruckner and Miquel Feixas and Mateu Sbert}, year = {2008}, pages = {113--120}, month = {Aug}, booktitle = {Proceedings of IEEE/EG International Symposium on Volume and Point-Based Graphics}, location = {Los Angeles, CA, USA}, abstract = {Obscurances, from which ambient occlusion is a particular case, is a technology that produces natural-looking lighting effects in a faster way than global illumination. Its application in volume visualization is of special interest since it permits us to generate a high quality rendering at a low cost. In this paper, we propose an obscurance-based framework that allows us to obtain realistic and illustrative volume visualizations in an interactive manner. Obscurances can include color bleeding effects without additional cost. Moreover, we obtain a saliency map from the gradient of obscurances and we show its application to enhance volume visualization and to select the most salient views.}, URL = {http://www.cg.tuwien.ac.at/research/publications/2008/ruiz-2008-OVR/}, images = {images/ruiz08obscurance.jpg, images/ruiz08obscurance1.jpg}, thumbnails = {images/ruiz08obscurance_thumb.jpg, images/ruiz08obscurance1_thumb.jpg}, project = {illvis,medviz}, } @inproceedings{piringer08comparing, author = {Harald Piringer and Wolfgang Berger and Helwig Hauser}, title = {Quantifying and Comparing Features in High-Dimensional Datasets}, booktitle = {Proceedings of the International Conference on Information Visualisation (IV 2008)}, abstract = {Linking and brushing is a proven approach to analyzing multi-dimensional datasets in the context of multiple coordinated views. Nevertheless, most of the respective visualization techniques only offer qualitative visual results. Many user tasks, however, also require precise quantitative results as, for example, offered by statistical analysis. In succession of the useful Rank-by-Feature Framework, this paper describes a joint visual and statistical approach for guiding the user through a high-dimensional dataset by ranking dimensions (1D case) and pairs of dimensions (2D case) according to statistical summaries. While the original Rank-by-Feature Framework is limited to global features, the most important novelty here is the concept to consider local features, i.e., data subsets defined by brushing in linked views. The ability to compare subsets to other subsets and subsets to the whole dataset in the context of a large number of dimensions significantly extends the benefits of the approach especially in later stages of an exploratory data analysis. A case study illustrates the workflow by analyzing counts of keywords for classifying e-mails as spam or no-spam.}, location = {London, UK}, year = {2008}, pages = {240--245}, month = 7, URL = {http://dx.doi.org/10.1109/IV.2008.17}, publisher = {IEEE Computer Society}, address = {Washington, DC, USA}, images = {images/piringer08comparing1.png, images/piringer08comparing2.png, images/piringer08comparing3.png}, thumbnails = {images/piringer08comparing1_thumb.png, images/piringer08comparing2_thumb.jpg, images/piringer08comparing3_thumb.png}, } @inproceedings{matkovic08comVis, title = {ComVis: a Coordinated Multiple Views System for Prototyping New Visualization Technology}, author = {Kresimir Matkovic and Wolfgang Freiler and Denis Gracanin and Helwig Hauser}, year = {2008}, abstract = {There is a large number of interactive visualization tools, however no universal tool exists that covers all relevant aspects for all possible application domains. We have developed a tool, ComVIs, which was intended to be used as a research prototype for new visualization techniques. We have identified some interesting aspects from developers and users point of view during tool development. In this paper we describe lessons learned during the process, and share our findings with visualization research community. Examples at the end prove the usefulness of the developed tool. One particular example, the concept of families of function graphs and application to analysis of fuel injection concludes the paper.}, pages = {215--220}, month = 7, URL = {http://dx.doi.org/10.1109/IV.2008.87}, booktitle = {Proceedings of the International Conference on Information Visualisation (IV 2008)}, location = {London, UK}, publisher = {IEEE Computer Society}, address = {Washington, DC, USA}, images = {images/matkovic08comvis.jpg}, thumbnails = {images/matkovic08comvis_thumb.jpg}, } @INPROCEEDINGS{Balabanian2008_3DPVT, author = {Jean-Paul Balabanian and Ivan Viola and Torsten M{\"o}ller and Eduard Gr{\"o}ller}, title = {Temporal Styles for Time-Varying Volume Data}, booktitle = {Proceedings of 3DPVT'08 - the Fourth International Symposium on 3D Data Processing, Visualization and Transmission}, year = {2008}, editor = {Stephan Gumhold and Jana Kosecka and Oliver Staadt}, pages = {81--89}, month = {June}, abstract = {This paper introduces interaction mechanisms for conveying temporal characteristics of time-varying volume data based on temporal styles. We demonstrate the flexibility of the new concept through different temporal style transfer function types and we define a set of temporal compositors as operators on them. The data is rendered by a multi-volume GPU raycaster that does not require any grid alignment over the individual time-steps of our data nor a rectilinear grid structure. The paper presents the applicability of the new concept on different data sets from partial to full voxel alignment with rectilinear and curvilinear grid layout.}, location = {Atlanta, USA}, event = {3D Data Processing, Visualization and Transmission 2008}, url = {http://www.cc.gatech.edu/research/reports/GT-IC-08-05}, pdf = {pdfs/Balabanian2008_3DPVT.pdf}, thumbnails = {images/balabanian2008_3dpvt_isabel_thumb.jpg, images/balabanian2008_3dpvt_overview_thumb.jpg, images/balabanian2008_3dpvt_poster_thumb.jpg}, images = {images/balabanian2008_3dpvt_isabel.jpg, images/balabanian2008_3dpvt_overview.jpg, images/balabanian2008_3dpvt_poster.jpg}, project = {illvis}, } @article{fuchs08parallel, title = "Parallel Vectors Criteria for Unsteady Flow Vortices", author = "Raphael Fuchs and Ronald Peikert and Helwig Hauser and Filip Sadlo and Philipp Muigg", year = "2008", abstract = "Feature-based flow visualization is naturally dependent on feature extraction. To extract flow features, often higher-order properties of the flow data are used such as the Jacobian or curvature properties, implicitly describing the flow features in terms of their inherent flow characteristics (e.g., collinear flow and vorticity vectors). In this paper we present recent research which leads to the (not really surprising) conclusion that feature extraction algorithms need to be extended to a time-dependent analysis framework (in terms of time derivatives) when dealing with unsteady flow data. Accordingly, we present two extensions of the parallel vectors based vortex extraction criteria to the time-dependent domain and show the improvements of feature-based flow visualization in comparison to the steady versions of this extraction algorithm both in the context of a high-resolution dataset, i.e., a simulation specifically designed to evaluate our new approach, as well as for a real-world dataset from a concrete application.", pages = "615--626", month = "May", journal = {IEEE Transactions on Visualization and Computer Graphics (IEEE TVCG)}, number = "3", volume = "14", keywords = "Time-Varying Data Visualization, Vortex Feature Detection", URL = "http://www.cg.tuwien.ac.at/research/publications/2008/fuchs_raphael_2007_par/", pdf = {http://dx.doi.org10.1109/TVCG.2007.70633}, images = {images/fuchs08parallel.jpg, images/fuchs08parallel1.jpg}, thumbnails = {images/fuchs08parallel_thumb.jpg, images/fuchs08parallel1_thumb.jpg}, } @article{Muigg08four, author = {Philipp Muigg and Johannes Kehrer and Steffen Oeltze and Harald Piringer and Helmut Doleisch and Bernhard Preim and Helwig Hauser}, title = {A Four-level Focus+Context Approach to Interactive Visual Analysis of Temporal Features in Large Scientific Data}, year = {2008}, month = {may}, abstract = {In this paper we present a new approach to the interactive visual analysis of time-dependent scientific data – both from measurements as well as from computational simulation – by visualizing a scalar function over time for each of tenthousands or even millions of sample points. In order to cope with overdrawing and cluttering, we introduce a new four-level method of focus+context visualization. Based on a setting of coordinated, multiple views (with linking and brushing), we integrate three different kinds of focus and also the context in every single view. Per data item we use three values (from the unit interval each) to represent to which degree the data item is part of the respective focus level. We present a color compositing scheme which is capable of expressing all three values in a meaningful way, taking semantics and their relations amongst each other (in the context of our multiple linked view setup) into account. Furthermore, we present additional image-based postprocessing methods to enhance the visualization of large sets of function graphs, including a texture-based technique based on line integral convolution (LIC). We also propose advanced brushing techniques which are specific to the timedependent nature of the data (in order to brush patterns over time more efficiently). We demonstrate the usefulness of the new approach in the context of medical perfusion data.}, journal = {Computer Graphics Forum}, event = "EuroVis 2008", volume = {27}, number = {3}, pages = {775--782}, location = "Eindhooven, Netherlands", URL = {http://dx.doi.org/10.1111/j.1467-8659.2008.01207.x}, images = {images/muigg08_eurovis3.jpg, images/muigg08_eurovis1.jpg, images/muigg08_eurovis2.jpg}, thumbnails = {images/muigg08_eurovis3_thumb.jpg, images/muigg08_eurovis1_thumb.jpg, images/muigg08_eurovis2_thumb.jpg}, }