{"title":"See What You Know: Analyzing Data Distribution to Improve Density Map Visualization","authors":"E. Bertini, A. D. Girolamo, G. Santucci","doi":"10.2312/VisSym/EuroVis07/163-170","DOIUrl":"https://doi.org/10.2312/VisSym/EuroVis07/163-170","url":null,"abstract":"Density maps allow for visually rendering density differences, usually mapping density values to a grey or color scale. The paper analyzes the drawbacks arising from the commonly used strategies and introduces a novel technique able to improve the overall mapping process. The technique is driven by statistical knowledge about the density distribution and a set of quality metrics allows for validating, in an objective way, its effectiveness.","PeriodicalId":224719,"journal":{"name":"Eurographics Conference on Visualization","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114882048","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Segmentation of DT-MRI Anisotropy Isosurfaces","authors":"T. Schultz, H. Theisel, H. Seidel","doi":"10.2312/VisSym/EuroVis07/187-194","DOIUrl":"https://doi.org/10.2312/VisSym/EuroVis07/187-194","url":null,"abstract":"While isosurfaces of anisotropy measures for data from diffusion tensor magnetic resonance imaging (DT-MRI) are known to depict major anatomical structures, the anisotropy metric reduces the rich tensor data to a simple scalar field. In this work, we suggest that the part of the data which has been ignored by the metric can be used to segment anisotropy isosurfaces into anatomically meaningful regions. For the implementation, we propose an edge-based watershed method that adapts and extends a method from curvature-based mesh segmentation [MW99]. Finally, we use the segmentation results to enhance visualization of the data.","PeriodicalId":224719,"journal":{"name":"Eurographics Conference on Visualization","volume":"132 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127794419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Schedl, Peter Knees, Klaus Seyerlehner, Tim Pohle
{"title":"The CoMIRVA Toolkit for Visualizing Music-Related Data","authors":"M. Schedl, Peter Knees, Klaus Seyerlehner, Tim Pohle","doi":"10.2312/VisSym/EuroVis07/147-154","DOIUrl":"https://doi.org/10.2312/VisSym/EuroVis07/147-154","url":null,"abstract":"We present CoMIRVA, which is an abbreviation for Collection of Music Information Retrieval and Visualization Applications. CoMIRVA is a Java framework and toolkit for information retrieval and visualization. It is licensed under the GNU GPL and can be downloaded from http://www.cp.jku.at/comirva/. At the moment, the main functionalities include music information retrieval, web retrieval, and visualization of the extracted information. In this paper, we focus on the visualization aspects of CoMIRVA. Since many of the information retrieval functions are intended to be applied to problems of the field of music information retrieval (MIR), we demonstrate the functions using data like similarity matrices of music artists gained by analyzing artist-related web pages. CoMIRVA is continuously being extended. Currently, it supports the following visualization techniques: Self-Organizing Map, Smoothed Data Histogram, Circled Bars, Circled Fans, Probabilistic Network, Continuous Similarity Ring, Sunburst, and Music Description Map. Since space is limited, we can only present a selected number of these in this paper. As one key feature of CoMIRVA is its easy extensibility, we further elaborate on how CoMIRVA was used for creating a novel user interface to digital music repositories.","PeriodicalId":224719,"journal":{"name":"Eurographics Conference on Visualization","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128101495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Design of Multi-dimensional Transfer Functions Using Dimensional Reduction","authors":"F. Pinto, C. Freitas","doi":"10.2312/VisSym/EuroVis07/131-138","DOIUrl":"https://doi.org/10.2312/VisSym/EuroVis07/131-138","url":null,"abstract":"Direct volume rendering techniques allow visualization of volume data without extracting intermediate geometry. The mapping from voxel attributes to optical properties is performed by transfer functions which, consequently, play a crucial role in building informative images from the data. One-dimensional transfer functions, which are based only on a scalar value per voxel, often do not provide proper visualizations. On the other hand, multidimensional transfer functions can perform more sophisticated data classification, based on vectorial voxel signatures. The transfer function design is a non-trivial and unintuitive task, especially in the multi-dimensional case. In this paper we propose a multi-dimensional transfer function design technique that uses self-organizing maps to perform dimensional reduction. Our approach gives uniform treatment to volume data containing voxel signatures of arbitrary dimension, and allows the use of any type of voxel attribute as part of the voxel signatures.","PeriodicalId":224719,"journal":{"name":"Eurographics Conference on Visualization","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123827801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
John C. Anderson, Luke J. Gosink, M. Duchaineau, K. Joy
{"title":"Feature Identification and Extraction in Function Fields","authors":"John C. Anderson, Luke J. Gosink, M. Duchaineau, K. Joy","doi":"10.2312/VisSym/EuroVis07/195-201","DOIUrl":"https://doi.org/10.2312/VisSym/EuroVis07/195-201","url":null,"abstract":"We present interactive techniques for identifying and extracting features in function fields. Function fields map points in n-dimensional Euclidean space to 1-dimensional scalar functions. Visual feature identification is ac- complished by interactively rendering scalar distance fields, constructed by applying a function-space distance metric over the function field. Combining visual exploration with feature extraction queries, formulated as a set of function-space constraints, facilitates quantitative analysis and annotation. Numerous application domains give rise to function fields. We present results for two-dimensional hyperspectral images, and a simulated time-varying, three-dimensional air quality dataset.","PeriodicalId":224719,"journal":{"name":"Eurographics Conference on Visualization","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125208211","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Depth Cues and Density in Temporal Parallel Coordinates","authors":"J. Johansson, P. Ljung, M. Cooper","doi":"10.2312/VisSym/EuroVis07/035-042","DOIUrl":"https://doi.org/10.2312/VisSym/EuroVis07/035-042","url":null,"abstract":"This paper introduces Temporal Density Parallel Coordinates (TDPC) and Depth Cue Parallel Coordinates (DCPC) which extend the standard 2D parallel coordinates technique to capture time-varying dynamics. The proposed techniques can be used to analyse temporal positions of data items as well as temporal positions of changes occurring using 2D displays. To represent temporal changes, polygons (instead of traditional lines) are rendered in parallel coordinates. The results presented show that rendering polygons is superior at revealing large temporal changes. Both TDPC and DCPC have been efficiently implemented on the GPU allowing the visualization of thousands of data items over thousands of time steps at interactive frame rates.","PeriodicalId":224719,"journal":{"name":"Eurographics Conference on Visualization","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117119937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Multiscale Visualization of Dynamic Software Logs","authors":"Sergio Moreta, A. Telea","doi":"10.2312/VisSym/EuroVis07/011-018","DOIUrl":"https://doi.org/10.2312/VisSym/EuroVis07/011-018","url":null,"abstract":"We present a set of techniques and design principles for the visualization of large dynamic software logs consisting of attributed change events, such as obtained from instrumenting programs or mining software repositories. We enhance the visualization scalability with importance-based antialiasing techniques that guarantee visibility of several types of events. We present a hierarchical clustering method that uncovers several patterns of interest in the event logs, such as same-lifetime memory allocations and software releases. We visualize the clusters using a new type of technique called interleaved cushions. We demonstrate our methods on two real-world problems: the monitoring of a dynamic memory allocator and the analysis of a software repository.","PeriodicalId":224719,"journal":{"name":"Eurographics Conference on Visualization","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125587784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Multiresolution MIP Rendering of Large Volumetric Data Accelerated on Graphics Hardware","authors":"W. J. V. D. Laan, A. Jalba, J. Roerdink","doi":"10.2312/VisSym/EuroVis07/243-250","DOIUrl":"https://doi.org/10.2312/VisSym/EuroVis07/243-250","url":null,"abstract":"This paper is concerned with a multiresolution representation for maximum intensity projection (MIP) volume rendering based on morphological pyramids which allows progressive refinement. We consider two algorithms for progressive rendering from the morphological pyramid: one which projects detail coefficients level by level, and a second one, called streaming MIP, which resorts the detail coefficients of all levels simultaneously with respect to decreasing magnitude of a suitable error measure. The latter method outperforms the level-by-level method, both with respect to image quality with a fixed amount of detail data, and in terms of flexibility of controlling approximation error or computation time. We improve the streaming MIP algorithm, present a GPU implementation for both methods, and perform a comparison with existing CPU and GPU implementations.","PeriodicalId":224719,"journal":{"name":"Eurographics Conference on Visualization","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129177993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
R. Buerger, P. Muigg, M. Ilcík, H. Doleisch, H. Hauser
{"title":"Integrating Local Feature Detectors in the Interactive Visual Analysis of Flow Simulation Data","authors":"R. Buerger, P. Muigg, M. Ilcík, H. Doleisch, H. Hauser","doi":"10.2312/VisSym/EuroVis07/171-178","DOIUrl":"https://doi.org/10.2312/VisSym/EuroVis07/171-178","url":null,"abstract":"We present smooth formulations of common vortex detectors that allow a seamless integration into the concept of interactive visual analysis of flow simulation data. We express the originally binary feature detectors as fuzzy-sets that can be combined using the linking and brushing concepts of interactive visual analysis. Both interaction and visualization gain from having multiple detectors concurrently available and from the ability to combine them. An application study on automotive data reveals how these vortex detectors combine and perform in praxis.","PeriodicalId":224719,"journal":{"name":"Eurographics Conference on Visualization","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126853538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
K. T. McDonnell, N. Neophytou, K. Mueller, Hong Qin
{"title":"Subdivision Volume Splatting","authors":"K. T. McDonnell, N. Neophytou, K. Mueller, Hong Qin","doi":"10.2312/VisSym/EuroVis07/139-146","DOIUrl":"https://doi.org/10.2312/VisSym/EuroVis07/139-146","url":null,"abstract":"Volumetric Subdivision (VS) is a powerful paradigm that enables volumetric sculpting and realistic volume deformations that give rise to the concept of \"virtual clay\". In VS, volumes are commonly represented as a space-filling set of deformed polyhedra, which can be further decomposed into a mesh of tetrahedra for rendering. Images can then be generated via tetrahedral projection or raycasting. A current shortcoming in VS-based operations is the need for a very high level of subdivision to represent fine detail in the mesh and to obtain a high-fidelity visualization. However, we have discovered that the subdivision process itself can be closely simulated with radial basis functions (RBFs), making it possible to replace the finer subdivision levels by a coarser aggregation of RBF kernels. This reduction to a simplified assembly of RBFs subsequently enables interactive rendering of volumetric subdivision shapes within a GPU-based volume splatting framework.","PeriodicalId":224719,"journal":{"name":"Eurographics Conference on Visualization","volume":"317 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130813496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}