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Visualizing crossing probabilistic tracts 可视化交叉概率束
2015 IEEE Scientific Visualization Conference (SciVis) Pub Date : 2015-10-25 DOI: 10.1109/SciVis.2015.7429506
Mathias Goldau, A. Reichenbach, M. Hlawitschka
{"title":"Visualizing crossing probabilistic tracts","authors":"Mathias Goldau, A. Reichenbach, M. Hlawitschka","doi":"10.1109/SciVis.2015.7429506","DOIUrl":"https://doi.org/10.1109/SciVis.2015.7429506","url":null,"abstract":"Diffusion weighted magnetic resonance imaging (dMRI) together with tractography algorithms allow to probe for principal white matter tracts in the living human brain. Specifically, probabilistic tractography quantifies the existence of physical connections to a given seed region as a 3D scalar map of confidence scores. Fiber-Stippling is a visualization for probabilistic tracts that effectively communicates the diffusion pattern, connectivity score, and anatomical context. Unfortunately, it cannot handle multiple diffusion orientations per voxel, which exist in high angular resolution diffusion imaging (HARDI) data. Such data is needed to resolve tracts in complex configurations, such as crossings. In this work, we suggest a visualization based on Fiber-Stippling but sensible to multiple diffusion orientations from HARDI-based diffusion models. With such a technique, it is now possible to visualize probabilistic tracts from HARDI-based tractography algorithms. This implies that tract crossings may now be visualized as crossing stipples, which is an essential step towards an accurate visualization of the neuroanatomy, as crossing tracts are widespread phenomena in the brain.","PeriodicalId":123718,"journal":{"name":"2015 IEEE Scientific Visualization Conference (SciVis)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129306920","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}
引用次数: 1
Visualize the universe: Interactive exploration of cosmological dark matter simulation data 可视化宇宙:宇宙暗物质模拟数据的交互式探索
2015 IEEE Scientific Visualization Conference (SciVis) Pub Date : 2015-10-25 DOI: 10.1109/SciVis.2015.7429500
A. Scherzinger, T. Brix, Dominik Drees, Andreas Völker, Kiril Radkov, Niko Santalidis, A. Fieguth, K. Hinrichs
{"title":"Visualize the universe: Interactive exploration of cosmological dark matter simulation data","authors":"A. Scherzinger, T. Brix, Dominik Drees, Andreas Völker, Kiril Radkov, Niko Santalidis, A. Fieguth, K. Hinrichs","doi":"10.1109/SciVis.2015.7429500","DOIUrl":"https://doi.org/10.1109/SciVis.2015.7429500","url":null,"abstract":"We propose a unified visualization tool for cosmological data resulting from dark matter simulations. Our system comprises both established and novel visualization approaches for the dark matter tracer particles and halo structures to allow interactive exploration of the data in 3D and 2D as well as tracking the evolution of the data over time using multiple views. Due to our scalable volume rendering approach, properties of the particle data such as the distribution of dark matter can be visualized at interactive frame rates even for large-scale data after a one-time pre-processing conversion step.","PeriodicalId":123718,"journal":{"name":"2015 IEEE Scientific Visualization Conference (SciVis)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126605745","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}
引用次数: 1
Correlation analysis in multidimensional multivariate time-varying datasets 多维多元时变数据集的相关分析
2015 IEEE Scientific Visualization Conference (SciVis) Pub Date : 2015-10-25 DOI: 10.1109/SciVis.2015.7429502
N. Abedzadeh
{"title":"Correlation analysis in multidimensional multivariate time-varying datasets","authors":"N. Abedzadeh","doi":"10.1109/SciVis.2015.7429502","DOIUrl":"https://doi.org/10.1109/SciVis.2015.7429502","url":null,"abstract":"One of the most vital challenges for weather forecasters is the correlation between two geographical phenomena that are distributed continuously in multidimensional multivariate time-varying datasets. In this research, we have visualized the correlation between Pressure and Temperature in the climate datasets. Pearson correlation is used in this study to measure the major linear relationship between two variables in the dataset. Using glyphs in the spatial location, we highlighted the significant association between variables. Based on the positive or negative slope of correlation lines, we can conclude how much they are correlated. The principal of this research is visualizing the local trend of variables versus each other in multidimensional multivariate time-varying datasets, which needs to be visualized with their spatial locations in meteorological datasets. Using glyphs, not only can we visualize the correlation between two variables in the coordinate system, but we can also discern whether any of these variables is separately increasing or decreasing. Moreover, we can visualize the background color as another variable and see the correlation lines around of a particular zone such as storm area.","PeriodicalId":123718,"journal":{"name":"2015 IEEE Scientific Visualization Conference (SciVis)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130248409","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}
引用次数: 1
Feature-based tensor field visualization for fiber reinforced polymers 基于特征的纤维增强聚合物张量场可视化
2015 IEEE Scientific Visualization Conference (SciVis) Pub Date : 2015-10-25 DOI: 10.1109/SciVis.2015.7429491
Valentin Zobel, M. Stommel, G. Scheuermann
{"title":"Feature-based tensor field visualization for fiber reinforced polymers","authors":"Valentin Zobel, M. Stommel, G. Scheuermann","doi":"10.1109/SciVis.2015.7429491","DOIUrl":"https://doi.org/10.1109/SciVis.2015.7429491","url":null,"abstract":"Virtual testing is an integral part of modern product development in mechanical engineering. Numerical structure simulations allow the computation of local stresses which are given as tensor fields. For homogeneous materials, the tensor information is usually reduced to a scalar field like the von Mises stress. A material-dependent threshold defines the material failure answering the key question of engineers. This leads to a rather simple feature-based visualisation. For composite materials like short fiber reinforced polymers, the situation is much more complex. The material property is determined by the fiber distribution at every position, often described as fiber orientation tensor field. Essentially, the material's ability to cope with stress becomes anisotropic and inhomogeneous. We show how to combine the stress field and the fiber orientation field in such cases, leading to a feature-based visualization of tensor fields for composite materials. The resulting features inform the engineer about potential improvements in the product development.","PeriodicalId":123718,"journal":{"name":"2015 IEEE Scientific Visualization Conference (SciVis)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130692048","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}
引用次数: 9
Explicit frequency control for high-quality texture-based flow visualization 明确的频率控制，高质量的基于纹理的流可视化
2015 IEEE Scientific Visualization Conference (SciVis) Pub Date : 2015-10-25 DOI: 10.1109/SciVis.2015.7429490
V. Matvienko, J. Krüger
{"title":"Explicit frequency control for high-quality texture-based flow visualization","authors":"V. Matvienko, J. Krüger","doi":"10.1109/SciVis.2015.7429490","DOIUrl":"https://doi.org/10.1109/SciVis.2015.7429490","url":null,"abstract":"In this work we propose an effective method for frequency-controlled dense flow visualization derived from a generalization of the Line Integral Convolution (LIC) technique. Our approach consists in considering the spectral properties of the dense flow visualization process as an integral operator defined in a local curvilinear coordinate system aligned with the flow. Exploring LIC from this point of view, we suggest a systematic way to design a flow visualization process with particular local spatial frequency properties of the resulting image. Our method is efficient, intuitive, and based on a long-standing model developed as a result of numerous perception studies. The method can be described as an iterative application of line integral convolution, followed by a one-dimensional Gabor filtering orthogonal to the flow. To demonstrate the utility of the technique, we generated novel adaptive multi-frequency flow visualizations, that according to our evaluation, feature a higher level of frequency control and higher quality scores than traditional approaches in texture-based flow visualization.","PeriodicalId":123718,"journal":{"name":"2015 IEEE Scientific Visualization Conference (SciVis)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128099558","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}
引用次数: 3
A classification of user tasks in visual analysis of volume data 卷数据可视化分析中用户任务的分类
2015 IEEE Scientific Visualization Conference (SciVis) Pub Date : 2015-10-25 DOI: 10.1109/SciVis.2015.7429485
B. Laha, D. Bowman, D. Laidlaw, J. Socha
{"title":"A classification of user tasks in visual analysis of volume data","authors":"B. Laha, D. Bowman, D. Laidlaw, J. Socha","doi":"10.1109/SciVis.2015.7429485","DOIUrl":"https://doi.org/10.1109/SciVis.2015.7429485","url":null,"abstract":"Empirical findings from studies in one scientific domain have very limited applicability to other domains, unless we formally establish deeper insights on the generalizability of task types. We present a domain-independent classification of visual analysis tasks with volume visualizations. This taxonomy will help researchers design experiments, ensure coverage, and generate hypotheses in empirical studies with volume datasets. To develop our taxonomy, we first interviewed scientists working with spatial data in disparate domains. We then ran a survey to evaluate the design participants in which were scientists and professionals from around the world, working with volume data in various scientific domains. Respondents agreed substantially with our taxonomy design, but also suggested important refinements. We report the results in the form of a goal-based generic categorization of visual analysis tasks with volume visualizations. Our taxonomy covers tasks performed with a wide variety of volume datasets.","PeriodicalId":123718,"journal":{"name":"2015 IEEE Scientific Visualization Conference (SciVis)","volume":"103 39","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131914094","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}
引用次数: 15
A proposed multivariate visualization taxonomy from user data 一种基于用户数据的多变量可视化分类法
2015 IEEE Scientific Visualization Conference (SciVis) Pub Date : 2015-10-25 DOI: 10.1109/SciVis.2015.7429511
M. Livingston, Jonathan W. Decker, Zhuming Ai
{"title":"A proposed multivariate visualization taxonomy from user data","authors":"M. Livingston, Jonathan W. Decker, Zhuming Ai","doi":"10.1109/SciVis.2015.7429511","DOIUrl":"https://doi.org/10.1109/SciVis.2015.7429511","url":null,"abstract":"We revisited past user study data on multivariate visualizations, looking at whether image processing measures offer any insight into user performance. While we find statistically significant correlations, some of the greatest insights into user performance came from variables that have strong ties to two key properties of multivariate representations. We discuss our analysis and propose a taxonomy of multivariate visualizations that arises.","PeriodicalId":123718,"journal":{"name":"2015 IEEE Scientific Visualization Conference (SciVis)","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134417600","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}
引用次数: 0
Multiresolution visualization of digital earth data via hexagonal box-spline wavelets 基于六边形盒样条小波的数字地球数据多分辨率可视化
2015 IEEE Scientific Visualization Conference (SciVis) Pub Date : 2015-10-25 DOI: 10.1109/SciVis.2015.7429508
M. Jubair, U. Alim, N. Röber, J. Clyne, Ali Mahdavi-Amiri, F. Samavati
{"title":"Multiresolution visualization of digital earth data via hexagonal box-spline wavelets","authors":"M. Jubair, U. Alim, N. Röber, J. Clyne, Ali Mahdavi-Amiri, F. Samavati","doi":"10.1109/SciVis.2015.7429508","DOIUrl":"https://doi.org/10.1109/SciVis.2015.7429508","url":null,"abstract":"Multiresolution analysis is an important tool for exploring large-scale data sets. Such analysis provides facilities to visualize data at different levels of detail while providing the advantages of efficient data compression and transmission. In this work, an approach is presented to apply multiresolution analysis to digital Earth data where each resolution describes data at a specific level of detail. Geospatial data at a fine level is taken as the input and a hierarchy of approximation and detail coefficients is built by applying a hexagonal discrete wavelet transform. Multiresolution filters are designed for hexagonal cells based on the three directional linear box spline which is natively supported by modern GPUs.","PeriodicalId":123718,"journal":{"name":"2015 IEEE Scientific Visualization Conference (SciVis)","volume":"147 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115177770","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}
引用次数: 3
PathlinesExplorer - Image-based exploration of large-scale pathline fields PathlinesExplorer -基于图像的大规模路径域探索
2015 IEEE Scientific Visualization Conference (SciVis) Pub Date : 2014-05-27 DOI: 10.1109/SciVis.2015.7429512
Omniah H. Nagoor, M. Hadwiger, Madhusudhanan Srinivasan
{"title":"PathlinesExplorer - Image-based exploration of large-scale pathline fields","authors":"Omniah H. Nagoor, M. Hadwiger, Madhusudhanan Srinivasan","doi":"10.1109/SciVis.2015.7429512","DOIUrl":"https://doi.org/10.1109/SciVis.2015.7429512","url":null,"abstract":"PathlinesExplorer is a novel image-based tool, which has been designed to visualize large scale pathline fields on a single computer [7]. PathlinesExplorer integrates explorable images (EI) technique [4] with order-independent transparency (OIT) method [2]. What makes this method different is that it allows users to handle large data on a single workstation. Although it is a view-dependent method, PathlinesExplorer combines both exploration and modification of visual aspects without re-accessing the original huge data. Our approach is based on constructing a per-pixel linked list data structure in which each pixel contains a list of pathline segments. With this view-dependent method, it is possible to filter, color-code, and explore large-scale flow data in real-time. In addition, optimization techniques such as early-ray termination and deferred shading are applied, which further improves the performance and scalability of our approach.","PeriodicalId":123718,"journal":{"name":"2015 IEEE Scientific Visualization Conference (SciVis)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129849196","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}
引用次数: 0
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