Pub Date : 2011-03-01DOI: 10.1109/PACIFICVIS.2011.5742387
O. D. Lampe, H. Hauser
In this paper, we discuss the extension and integration of the statistical concept of Kernel Density Estimation (KDE) in a scatterplot-like visualization for dynamic data at interactive rates. We present a line kernel for representing streaming data, we discuss how the concept of KDE can be adapted to enable a continuous representation of the distribution of a dependent variable of a 2D domain. We propose to automatically adapt the kernel bandwith of KDE to the viewport settings, in an interactive visualization environment that allows zooming and panning. We also present a GPU-based realization of KDE that leads to interactive frame rates, even for comparably large datasets. Finally, we demonstrate the usefulness of our approach in the context of three application scenarios - one studying streaming ship traffic data, another one from the oil & gas domain, where process data from the operation of an oil rig is streaming in to an on-shore operational center, and a third one studying commercial air traffic in the US spanning 1987 to 2008.
{"title":"Interactive visualization of streaming data with Kernel Density Estimation","authors":"O. D. Lampe, H. Hauser","doi":"10.1109/PACIFICVIS.2011.5742387","DOIUrl":"https://doi.org/10.1109/PACIFICVIS.2011.5742387","url":null,"abstract":"In this paper, we discuss the extension and integration of the statistical concept of Kernel Density Estimation (KDE) in a scatterplot-like visualization for dynamic data at interactive rates. We present a line kernel for representing streaming data, we discuss how the concept of KDE can be adapted to enable a continuous representation of the distribution of a dependent variable of a 2D domain. We propose to automatically adapt the kernel bandwith of KDE to the viewport settings, in an interactive visualization environment that allows zooming and panning. We also present a GPU-based realization of KDE that leads to interactive frame rates, even for comparably large datasets. Finally, we demonstrate the usefulness of our approach in the context of three application scenarios - one studying streaming ship traffic data, another one from the oil & gas domain, where process data from the operation of an oil rig is streaming in to an on-shore operational center, and a third one studying commercial air traffic in the US spanning 1987 to 2008.","PeriodicalId":127522,"journal":{"name":"2011 IEEE Pacific Visualization Symposium","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133290409","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}
Pub Date : 2011-03-01DOI: 10.1109/PACIFICVIS.2011.5742382
J. Alsakran, Yang Chen, Ye Zhao, Jing Yang, Dongning Luo
Text streams demand an effective, interactive, and on-the-fly method to explore the dynamic and massive data sets, and meanwhile extract valuable information for visual analysis. In this paper, we propose such an interactive visualization system that enables users to explore streaming-in text documents without prior knowledge of the data. The system can constantly incorporate incoming documents from a continuous source into existing visualization context, which is “physically” achieved by minimizing a potential energy defined from similarities between documents. Unlike most existing methods, our system uses dynamic keyword vectors to incorporate newly-introduced keywords from data streams. Furthermore, we propose a special keyword importance that makes it possible for users to adjust the similarity on-the-fly, and hence achieve their preferred visual effects in accordance to varying interests, which also helps to identify hot spots and outliers. We optimize the system performance through a similarity grid and with parallel implementation on graphics hardware (GPU), which achieves instantaneous animated visualization even for a very large data collection. Moreover, our system implements a powerful user interface enabling various user interactions for in-depth data analysis. Experiments and case studies are presented to illustrate our dynamic system for text stream exploration.
{"title":"STREAMIT: Dynamic visualization and interactive exploration of text streams","authors":"J. Alsakran, Yang Chen, Ye Zhao, Jing Yang, Dongning Luo","doi":"10.1109/PACIFICVIS.2011.5742382","DOIUrl":"https://doi.org/10.1109/PACIFICVIS.2011.5742382","url":null,"abstract":"Text streams demand an effective, interactive, and on-the-fly method to explore the dynamic and massive data sets, and meanwhile extract valuable information for visual analysis. In this paper, we propose such an interactive visualization system that enables users to explore streaming-in text documents without prior knowledge of the data. The system can constantly incorporate incoming documents from a continuous source into existing visualization context, which is “physically” achieved by minimizing a potential energy defined from similarities between documents. Unlike most existing methods, our system uses dynamic keyword vectors to incorporate newly-introduced keywords from data streams. Furthermore, we propose a special keyword importance that makes it possible for users to adjust the similarity on-the-fly, and hence achieve their preferred visual effects in accordance to varying interests, which also helps to identify hot spots and outliers. We optimize the system performance through a similarity grid and with parallel implementation on graphics hardware (GPU), which achieves instantaneous animated visualization even for a very large data collection. Moreover, our system implements a powerful user interface enabling various user interactions for in-depth data analysis. Experiments and case studies are presented to illustrate our dynamic system for text stream exploration.","PeriodicalId":127522,"journal":{"name":"2011 IEEE Pacific Visualization Symposium","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132291053","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}
Pub Date : 2011-03-01DOI: 10.1109/PACIFICVIS.2011.5742365
K. Ma
Advanced computing and imaging technologies enable scientists to study natural and physical phenomena at unprecedented precision, resulting in an explosive growth of data. Furthermore, the size of the collected information about the Internet and mobile device users is expected to be even greater, a daunting challenge we must address in order to make sense and maximize utilization of all the available information for decision making and knowledge discovery. I will introduce a few new approaches to large data visualization for revealing hidden structures and gleaning insights from large, complex data found in many areas of study.
{"title":"Keynote address: New approaches to large data visualization","authors":"K. Ma","doi":"10.1109/PACIFICVIS.2011.5742365","DOIUrl":"https://doi.org/10.1109/PACIFICVIS.2011.5742365","url":null,"abstract":"Advanced computing and imaging technologies enable scientists to study natural and physical phenomena at unprecedented precision, resulting in an explosive growth of data. Furthermore, the size of the collected information about the Internet and mobile device users is expected to be even greater, a daunting challenge we must address in order to make sense and maximize utilization of all the available information for decision making and knowledge discovery. I will introduce a few new approaches to large data visualization for revealing hidden structures and gleaning insights from large, complex data found in many areas of study.","PeriodicalId":127522,"journal":{"name":"2011 IEEE Pacific Visualization Symposium","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125184144","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}
Pub Date : 2011-03-01DOI: 10.1109/PACIFICVIS.2011.5742380
Manuela Waldner, D. Schmalstieg
Information exploration processes are often conducted in teams of experts, family members, or colleagues. These teams have to retrieve information from different sources, verify it, and finally compare and discuss their findings to find consensus. Today, support for these collaborative processes is limited and users often end up sharing either a single PC with one user taking control or using separate workstations, where support for tight collaboration is limited. In this paper, we present collaborative information linking which visually connects information across private and shared application windows to bridge knowledge gaps between users. We present the technical infrastructure for multi-user interaction and personalized meta-visualizations on large multi-projector displays, and demonstrate how personalized visual links connect information across existing applications modified in a minimally invasive manner. An observational experiment showed that information linking helps individuals to deal with large display space and teams to switch between individual information retrieval and joint verification and discussion.
{"title":"Collaborative information linking: Bridging knowledge gaps between users by linking across applications","authors":"Manuela Waldner, D. Schmalstieg","doi":"10.1109/PACIFICVIS.2011.5742380","DOIUrl":"https://doi.org/10.1109/PACIFICVIS.2011.5742380","url":null,"abstract":"Information exploration processes are often conducted in teams of experts, family members, or colleagues. These teams have to retrieve information from different sources, verify it, and finally compare and discuss their findings to find consensus. Today, support for these collaborative processes is limited and users often end up sharing either a single PC with one user taking control or using separate workstations, where support for tight collaboration is limited. In this paper, we present collaborative information linking which visually connects information across private and shared application windows to bridge knowledge gaps between users. We present the technical infrastructure for multi-user interaction and personalized meta-visualizations on large multi-projector displays, and demonstrate how personalized visual links connect information across existing applications modified in a minimally invasive manner. An observational experiment showed that information linking helps individuals to deal with large display space and teams to switch between individual information retrieval and joint verification and discussion.","PeriodicalId":127522,"journal":{"name":"2011 IEEE Pacific Visualization Symposium","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130414704","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}
Pub Date : 2011-03-01DOI: 10.1109/PACIFICVIS.2011.5742384
Roeland Scheepens, N. Willems, H. V. D. Wetering, J. V. Wijk
We present a method to interactively explore multiple attributes in trajectory data using density maps, i.e., images that show an aggregate overview of massive amounts of data. So far, density maps have mainly been used to visualize single attributes. Density maps are created in a two-way procedure; first smoothed trajectories are aggregated in a density field, and then the density field is visualized. In our approach, the user can explore attributes along trajectories by calculating a density field for multiple subsets of the data. These density fields are then either combined into a new density field or first visualized and then combined. Using a widget, called a distribution map, the user can interactively define subsets in an effective and intuitive way, and, supported by high-end graphics hardware the user gets fast feedback for these computationally expensive density field calculations. We show the versatility of our method with use cases in the maritime domain: to distinguish between periods in the temporal aggregation, to find anomalously behaving vessels, to solve ambiguities in density maps via drill down in the data, and for risk assessments. Given the generic framework and the lack of domain-specific assumptions, we expect our concept to be applicable for trajectories in other domains as well.
{"title":"Interactive visualization of multivariate trajectory data with density maps","authors":"Roeland Scheepens, N. Willems, H. V. D. Wetering, J. V. Wijk","doi":"10.1109/PACIFICVIS.2011.5742384","DOIUrl":"https://doi.org/10.1109/PACIFICVIS.2011.5742384","url":null,"abstract":"We present a method to interactively explore multiple attributes in trajectory data using density maps, i.e., images that show an aggregate overview of massive amounts of data. So far, density maps have mainly been used to visualize single attributes. Density maps are created in a two-way procedure; first smoothed trajectories are aggregated in a density field, and then the density field is visualized. In our approach, the user can explore attributes along trajectories by calculating a density field for multiple subsets of the data. These density fields are then either combined into a new density field or first visualized and then combined. Using a widget, called a distribution map, the user can interactively define subsets in an effective and intuitive way, and, supported by high-end graphics hardware the user gets fast feedback for these computationally expensive density field calculations. We show the versatility of our method with use cases in the maritime domain: to distinguish between periods in the temporal aggregation, to find anomalously behaving vessels, to solve ambiguities in density maps via drill down in the data, and for risk assessments. Given the generic framework and the lack of domain-specific assumptions, we expect our concept to be applicable for trajectories in other domains as well.","PeriodicalId":127522,"journal":{"name":"2011 IEEE Pacific Visualization Symposium","volume":"183 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127316863","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}
Pub Date : 2011-03-01DOI: 10.1109/PACIFICVIS.2011.5742370
Ching-Yao Lin, Kuen-Long Tsai, Sheng-Chuan Wang, C. Hsieh, Hsiu-Ming Chang, A. Chiang
Recent advances in microscopic imaging technology have enabled neuroscientists to obtain unprecedentedly clear images of neurons. To extract additional knowledge from the tangled neurons, for example, their connective relationships, is key to understanding how information is processed and transmitted within the brain. In this paper, we will introduce our recent endeavor, the Neuron Navigator (NNG), which integrates a 3D neuron image database into an easy-to-use visual interface. Via a flexible and user-friendly interface, NNG is designed to help researchers analyze and observe the connectivity within the neural maze and discover possible pathways. With NNG's 3D neuron image database, researchers can perform volumetric searches using the location of neural terminals, or the occupation of neuron volumes within the 3D brain space. Also, the presence of the neurons under a combination of spatial restrictions can be shown as well. NNG is a result of a multi-discipline collaboration between neuroscientists and computer scientists, and NNG has now been implemented on a coordinated brain space, that being, the Drosophila (fruit fly) brain. NNG is accessible through: http://211.73.64.34/NNG.
{"title":"The Neuron Navigator: Exploring the information pathway through the neural maze","authors":"Ching-Yao Lin, Kuen-Long Tsai, Sheng-Chuan Wang, C. Hsieh, Hsiu-Ming Chang, A. Chiang","doi":"10.1109/PACIFICVIS.2011.5742370","DOIUrl":"https://doi.org/10.1109/PACIFICVIS.2011.5742370","url":null,"abstract":"Recent advances in microscopic imaging technology have enabled neuroscientists to obtain unprecedentedly clear images of neurons. To extract additional knowledge from the tangled neurons, for example, their connective relationships, is key to understanding how information is processed and transmitted within the brain. In this paper, we will introduce our recent endeavor, the Neuron Navigator (NNG), which integrates a 3D neuron image database into an easy-to-use visual interface. Via a flexible and user-friendly interface, NNG is designed to help researchers analyze and observe the connectivity within the neural maze and discover possible pathways. With NNG's 3D neuron image database, researchers can perform volumetric searches using the location of neural terminals, or the occupation of neuron volumes within the 3D brain space. Also, the presence of the neurons under a combination of spatial restrictions can be shown as well. NNG is a result of a multi-discipline collaboration between neuroscientists and computer scientists, and NNG has now been implemented on a coordinated brain space, that being, the Drosophila (fruit fly) brain. NNG is accessible through: http://211.73.64.34/NNG.","PeriodicalId":127522,"journal":{"name":"2011 IEEE Pacific Visualization Symposium","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130688413","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}
Pub Date : 2011-03-01DOI: 10.1109/PACIFICVIS.2011.5742355
A. Knoll, S. Thelen, I. Wald, C. Hansen, H. Hagen, M. Papka
We present an efficient method for volume rendering by raycasting on the CPU. We employ coherent packet traversal of an implicit bounding volume hierarchy, heuristically pruned using preintegrated transfer functions, to exploit empty or homogeneous space. We also detail SIMD optimizations for volumetric integration, trilinear interpolation, and gradient lighting. The resulting system performs well on low-end and laptop hardware, and can outperform out-of-core GPU methods by orders of magnitude when rendering large volumes without level-of-detail (LOD) on a workstation. We show that, while slower than GPU methods for low-resolution volumes, an optimized CPU renderer does not require LOD to achieve interactive performance on large data sets.
{"title":"Full-resolution interactive CPU volume rendering with coherent BVH traversal","authors":"A. Knoll, S. Thelen, I. Wald, C. Hansen, H. Hagen, M. Papka","doi":"10.1109/PACIFICVIS.2011.5742355","DOIUrl":"https://doi.org/10.1109/PACIFICVIS.2011.5742355","url":null,"abstract":"We present an efficient method for volume rendering by raycasting on the CPU. We employ coherent packet traversal of an implicit bounding volume hierarchy, heuristically pruned using preintegrated transfer functions, to exploit empty or homogeneous space. We also detail SIMD optimizations for volumetric integration, trilinear interpolation, and gradient lighting. The resulting system performs well on low-end and laptop hardware, and can outperform out-of-core GPU methods by orders of magnitude when rendering large volumes without level-of-detail (LOD) on a workstation. We show that, while slower than GPU methods for low-resolution volumes, an optimized CPU renderer does not require LOD to achieve interactive performance on large data sets.","PeriodicalId":127522,"journal":{"name":"2011 IEEE Pacific Visualization Symposium","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132493553","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}
Pub Date : 2011-03-01DOI: 10.1109/PACIFICVIS.2011.5742377
Jishang Wei, Hongfeng Yu, R. Grout, Jacqueline H. Chen, K. Ma
Current simulations of turbulent flames are instrumented with particles to capture the dynamic behavior of combustion in next-generation engines. Categorizing the set of many millions of particles, each of which is featured with a history of its movement positions and changing thermo-chemical states, helps understand the turbulence mechanism. We introduce a dual-space method to analyze such data, starting by clustering the time series curves in the phase space of the data, and then visualizing the corresponding trajectories of each cluster in the physical space. To cluster time series curves, we adopt a model-based clustering technique in a two-stage scheme. In the first stage, the characteristics of shape and relative position are particularly concerned in classifying the time series curves, and in the second stage, within each group of curves, clustering is further conducted based on how the curves change over time. In our work, we perform the model-based clustering in a semi-supervised manner. Users' domain knowledge is integrated through intuitive interaction tools to steer the clustering process. Our dual-space method has been used to analyze particle data in combustion simulations and can also be applied to other scientific simulations involving particle trajectory analysis work.
{"title":"Dual space analysis of turbulent combustion particle data","authors":"Jishang Wei, Hongfeng Yu, R. Grout, Jacqueline H. Chen, K. Ma","doi":"10.1109/PACIFICVIS.2011.5742377","DOIUrl":"https://doi.org/10.1109/PACIFICVIS.2011.5742377","url":null,"abstract":"Current simulations of turbulent flames are instrumented with particles to capture the dynamic behavior of combustion in next-generation engines. Categorizing the set of many millions of particles, each of which is featured with a history of its movement positions and changing thermo-chemical states, helps understand the turbulence mechanism. We introduce a dual-space method to analyze such data, starting by clustering the time series curves in the phase space of the data, and then visualizing the corresponding trajectories of each cluster in the physical space. To cluster time series curves, we adopt a model-based clustering technique in a two-stage scheme. In the first stage, the characteristics of shape and relative position are particularly concerned in classifying the time series curves, and in the second stage, within each group of curves, clustering is further conducted based on how the curves change over time. In our work, we perform the model-based clustering in a semi-supervised manner. Users' domain knowledge is integrated through intuitive interaction tools to steer the clustering process. Our dual-space method has been used to analyze particle data in combustion simulations and can also be applied to other scientific simulations involving particle trajectory analysis work.","PeriodicalId":127522,"journal":{"name":"2011 IEEE Pacific Visualization Symposium","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122443044","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}
Pub Date : 2011-03-01DOI: 10.1109/PACIFICVIS.2011.5742391
W. Didimo, G. Liotta, Fabrizio Montecchiani, P. Palladino
We present a new system, VISFAN, for the visual analysis of financial activity networks. It supports the analyst with effective tools to discover financial crimes, like money laundering and frauds. If compared with other existing systems and methodologies for the analysis of criminal networks, VISFAN presents the following main novelties: (i) It combines bottom-up and top-down interaction paradigms for the visual exploration of complex networks; (ii) It makes it possible to mix automatic and manual clustering; (iii) It allows the analyst to interactively customize the dimensions of each cluster region and to apply different geometric constraints on the layout. VISFAN also implements several tools for social network analysis other than clustering. For example, it computes several indices to measure the centrality of each actor in the network.
{"title":"An advanced network visualization system for financial crime detection","authors":"W. Didimo, G. Liotta, Fabrizio Montecchiani, P. Palladino","doi":"10.1109/PACIFICVIS.2011.5742391","DOIUrl":"https://doi.org/10.1109/PACIFICVIS.2011.5742391","url":null,"abstract":"We present a new system, VISFAN, for the visual analysis of financial activity networks. It supports the analyst with effective tools to discover financial crimes, like money laundering and frauds. If compared with other existing systems and methodologies for the analysis of criminal networks, VISFAN presents the following main novelties: (i) It combines bottom-up and top-down interaction paradigms for the visual exploration of complex networks; (ii) It makes it possible to mix automatic and manual clustering; (iii) It allows the analyst to interactively customize the dimensions of each cluster region and to apply different geometric constraints on the layout. VISFAN also implements several tools for social network analysis other than clustering. For example, it computes several indices to measure the centrality of each actor in the network.","PeriodicalId":127522,"journal":{"name":"2011 IEEE Pacific Visualization Symposium","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114388728","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}
Pub Date : 2011-03-01DOI: 10.1109/PACIFICVIS.2011.5742374
Mathias Otto, T. Germer, H. Theisel
We present a technique to visualize global uncertainty in stationary 3D vector fields by a topological approach. We start from an existing approach for 2D uncertain vector field topology and extend this into 3D space. For this a number of conceptional and technical challenges in performance and visual representation arise. In order to solve them, we develop an acceleration for finding sink and source distributions. Having these distributions we use overlaps of their corresponding volumes to find separating structures and saddles. As part of the approach, we introduce uncertain saddle and boundary switch connectors and provide algorithms to extract them. For the visual representation, we use multiple direct volume renderings. We test our method on a number of synthetic and real data sets.
{"title":"Uncertain topology of 3D vector fields","authors":"Mathias Otto, T. Germer, H. Theisel","doi":"10.1109/PACIFICVIS.2011.5742374","DOIUrl":"https://doi.org/10.1109/PACIFICVIS.2011.5742374","url":null,"abstract":"We present a technique to visualize global uncertainty in stationary 3D vector fields by a topological approach. We start from an existing approach for 2D uncertain vector field topology and extend this into 3D space. For this a number of conceptional and technical challenges in performance and visual representation arise. In order to solve them, we develop an acceleration for finding sink and source distributions. Having these distributions we use overlaps of their corresponding volumes to find separating structures and saddles. As part of the approach, we introduce uncertain saddle and boundary switch connectors and provide algorithms to extract them. For the visual representation, we use multiple direct volume renderings. We test our method on a number of synthetic and real data sets.","PeriodicalId":127522,"journal":{"name":"2011 IEEE Pacific Visualization Symposium","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134163969","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}
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