By Topic

Pacific Visualization Symposium (PacificVis), 2011 IEEE

Date 1-4 March 2011

Filter Results

Displaying Results 1 - 25 of 41
  • [Title page]

    Page(s): i
    Save to Project icon | Request Permissions | PDF file iconPDF (141 KB)  
    Freely Available from IEEE
  • [Copyright notice]

    Page(s): ii
    Save to Project icon | Request Permissions | PDF file iconPDF (22 KB)  
    Freely Available from IEEE
  • Contents

    Page(s): iii - iv
    Save to Project icon | Request Permissions | PDF file iconPDF (71 KB)  
    Freely Available from IEEE
  • Supporting organizations

    Page(s): v
    Save to Project icon | Request Permissions | PDF file iconPDF (416 KB)  
    Freely Available from IEEE
  • Preface

    Page(s): vi
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (30 KB)  

    Welcome to the proceedings of the IEEE Pacific Visualization Symposium 2011 which took place in Hong Kong, China, on March 1–4, 2011. After a very successful event in Kyoto in 2008, Beijing in 2009, and Taipei in 2010, this is the fourth PacificVis sponsored by the IEEE Visualization and Graphics Technical Committee (VGTC). View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • IEEE Visualization and Graphics Technical Committee (VGTC)

    Page(s): vii
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (72 KB)  

    The IEEE Visualization and Graphics Technical Committee (VGTC) is a formal subcommittee of the Technical Activities Board (TAB) of the IEEE Computer Society. The VGTC provides technical leadership and organizes technical activities in the areas of visualization, computer graphics, virtual and augmented reality, and interaction. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Symposium Committee

    Page(s): viii
    Save to Project icon | Request Permissions | PDF file iconPDF (51 KB)  
    Freely Available from IEEE
  • Reviewers

    Page(s): ix
    Save to Project icon | Request Permissions | PDF file iconPDF (38 KB)  
    Freely Available from IEEE
  • Keynote address: Immersive exploration of large datasets

    Page(s): x
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (137 KB)  

    Scientists, engineers and physicians are now confronted with a fire hose of data. Immersive visualization environments provide these users with a novel way of interacting and reasoning with large datasets. They allow them to utilize the entirety of their visual bandwidth, effectively engulfing the user in the data and enabling collaborative interaction. We present a custom-built 5-wall Cave environment, called the Immersive Cabin (IC). It is driven by a GPU cluster for both computation and 3D stereo rendering. We also propose a conformal deformation rendering pipeline for the visualization of datasets on partially-immersive platforms. Combined with a range of interaction and navigation tools, our system can support numerous interactive applications of large datasets. Several demonstrations include architectural visualization, urban planning, medical visualization, simulation and rendering of physical phenomena, and entertainment. Current visualization displays, however, have not kept up with the explosive growth in data size and resolution, which is beginning to match the resolution of the visuals that surround us in daily life. To ameliorate this challenge, we have developed a life-like, realistic immersion into the petascale data to be explored, appropriately called The RealityDeck. It is a one-of-a kind pioneering G-pixel immersive and collaborative display system - a unique assembly of high-res display panels, GPU cluster, sensors, networking, computer vision, and human-computer interaction technologies. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Keynote address: New approaches to large data visualization

    Page(s): xi
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (80 KB)  

    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. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Keynote address: Why everyone seems to be using spring embedders for network visualization, and should not

    Page(s): xii
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (265 KB)  

    The main algorithmic challenge in network visualization is the placement of nodes. While plenty of layout algorithms have been proposed, the vast majority of information visualization tools appears to utilize (sometimes a variant of) one of two algorithms: the approach of Fruchterman and Reingold or that of Kamada and Kawai. Both are often referred to as force-directed methods, or spring embedders, and praised for their general applicability, high adaptability, and simplicity. I will argue that commonly used implementations and even the approaches themselves are outdated and, in fact, have always been. They should be replaced by variants of multidimensional scaling that display superior results and scalability, and are just as flexible and easy to implement. Some of these statements may actually be backed by evidence. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Full-resolution interactive CPU volume rendering with coherent BVH traversal

    Page(s): 3 - 10
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (16920 KB) |  | HTML iconHTML  

    We present an efficient method for volume rendering by ray casting 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. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Context-aware volume navigation

    Page(s): 11 - 18
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4101 KB) |  | HTML iconHTML  

    The trackball metaphor is exploited in many applications where volumetric data needs to be explored. Although it provides an intuitive way to inspect the overall structure of objects of interest, an in-detail inspection can be tedious - or when cavities occur even impossible. Therefore we propose a context-aware navigation technique for the exploration of volumetric data. While navigation techniques for polygonal data require information about the rendered geometry, this strategy is not sufficient in the area of volume rendering. Since rendering parameters, e.g., the transfer function, have a strong influence on the visualized structures, they also affect the features to be explored. To compensate for this effect we propose a novel image-based navigation approach for volumetric data. While being intuitive to use, the proposed technique allows the user to perform complex navigation tasks, in particular to get an overview as well as to perform an in-detail inspection without any navigation mode switches. The technique can be easily integrated into raycasting based volume renderers, needs no extra data structures and is independent of the data set as well as the rendering parameters. We will discuss the underlying concepts, explain how to enable the navigation at interactive frame rates using OpenCL, and evaluate its usability as well as its performance. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Multi-dimensional transfer function design based on flexible dimension projection embedded in parallel coordinates

    Page(s): 19 - 26
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (9040 KB) |  | HTML iconHTML  

    In this paper, we present an effective transfer function (TF) design for multivariate volume, providing tightly coupled views of parallel coordinates plot (PCP), MDS-based dimension projection plots, and volume rendered image space. In our design, the PCP showing the data distribution of each variate dimension and the MDS showing reduced dimensional features are integrated seamlessly to provide flexible feature classification for the user without context switching between different data presentations. Our proposed interface enables users to identify interested clusters and assign optical properties with lassos, magic wand and other tools. Furthermore, sketching directly on the volume rendered images has been implemented to probe and edit features. To achieve interactivity, octree partitioning with Gaussian Mixture Model (GMM), and other data reduction techniques are applied. Our experiments show that the proposed method is effective for multidimensional TF design and data exploration. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Static correlation visualization for large time-varying volume data

    Page(s): 27 - 34
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (8936 KB) |  | HTML iconHTML  

    Finding correlations among data is one of the most essential tasks in many scientific investigations and discoveries. This paper addresses the issue of creating a static volume classification that summarizes the correlation connection in time-varying multivariate data sets. In practice, computing all temporal and spatial correlations for large 3D time-varying multivariate data sets is prohibitively expensive. We present a sampling-based approach to classifying correlation patterns. Our sampling scheme consists of three steps: selecting important samples from the volume, prioritizing distance computation for sample pairs, and approximating volume-based correlation with sample-based correlation. We classify sample voxels to produce static visualization that succinctly summarize the connection among all correlation volumes with respect to various reference locations. We also investigate the error introduced by each step of our sampling scheme in terms of classification accuracy. Domain scientists participated in this work and helped us select samples and evaluate results. Our approach is generally applicable to the analysis of other scientific data where correlation study is relevant. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • The Neuron Navigator: Exploring the information pathway through the neural maze

    Page(s): 35 - 42
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (11935 KB) |  | HTML iconHTML  

    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. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • CareCruiser: Exploring and visualizing plans, events, and effects interactively

    Page(s): 43 - 50
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (6727 KB) |  | HTML iconHTML  

    In recent years, sophisticated visualization methods have been developed to support both, the logical structure and the time-oriented aspects of computer-executable clinical treatment plans. However, visualizing the effects of applying treatment plans as well as supporting the exploration of effects on the patient's condition are still largely unresolved tasks. To fill this gap, we have developed a prototype that enhances known visualization methods to communicate the processes of treatment plan application together with their effects on a patient's condition in an easily understandable way. Our prototype combines the advantages of enhanced visual recognition of patterns with traditional information of parameters' development. Thus, it provides means (1) to assess success or failure of previously applied treatment plans, (2) to explore the effects of each applied clinical action on the patient's condition, and (3) to identify sub-optimal treatment choices. These means help physicians to optimize their treatment choices and enable developers of clinical practice guidelines (CPGs) to investigate and readjust these treatment plans. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Loose capacity-constrained representatives for the qualitative visual analysis in molecular dynamics

    Page(s): 51 - 58
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (24397 KB) |  | HTML iconHTML  

    Molecular dynamics is a widely used simulation technique to investigate material properties and structural changes under external forces. The availability of more powerful clusters and algorithms continues to increase the spatial and temporal extents of the simulation domain. This poses a particular challenge for the visualization of the underlying processes which might consist of millions of particles and thousands of time steps. Some application domains have developed special visual metaphors to only represent the relevant information of such data sets but these approaches typically require detailed domain knowledge that might not always be available or applicable. We propose a general technique that replaces the huge amount of simulated particles by a smaller set of representatives that are used for the visualization instead. The representatives capture the characteristics of the underlying particle density and exhibit coherency over time. We introduce loose capacity-constrained Voronoi diagrams for the generation of these representatives by means of a GPU-friendly, parallel algorithm. This way we achieve visualizations that reflect the particle distribution and geometric structure of the original data very faithfully. We evaluate our approach using real-world data sets from the application domains of material science, thermodynamics and dynamical systems theory. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Interactive seismic interpretation with piecewise global energy minimization

    Page(s): 59 - 66
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (6228 KB) |  | HTML iconHTML  

    Increasing demands in world-wide energy consumption and oil depletion of large reservoirs have resulted in the need for exploring smaller and more complex oil reservoirs. Planning of the reservoir valorization usually starts with creating a model of the subsurface structures, including seismic faults and horizons. However, seismic interpretation and horizon tracing is a difficult and error-prone task, often resulting in hours of work needing to be manually repeated. In this paper, we propose a novel, interactive workflow for horizon interpretation based on well positions, which include additional geological and geophysical data captured by actual drillings. Instead of interpreting the volume slice-by-slice in 2D, we propose 3D seismic interpretation based on well positions. We introduce a combination of 2D and 3D minimal cost path and minimal cost surface tracing for extracting horizons with very little user input. By processing the volume based on well positions rather than slice-based, we are able to create a piecewise optimal horizon surface at interactive rates. We have integrated our system into a visual analysis platform which supports multiple linked views for fast verification, exploration and analysis of the extracted horizons. The system is currently being evaluated by our collaborating domain experts. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Uncertain topology of 3D vector fields

    Page(s): 67 - 74
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (10176 KB) |  | HTML iconHTML  

    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. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Edge maps: Representing flow with bounded error

    Page(s): 75 - 82
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2268 KB) |  | HTML iconHTML  

    Robust analysis of vector fields has been established as an important tool for deriving insights from the complex systems these fields model. Many analysis techniques rely on computing streamlines, a task often hampered by numerical instabilities. Approaches that ignore the resulting errors can lead to inconsistencies that may produce unreliable visualizations and ultimately prevent in-depth analysis. We propose a new representation for vector fields on surfaces that replaces numerical integration through triangles with linear maps defined on its boundary. This representation, called edge maps, is equivalent to computing all possible streamlines at a user defined error threshold. In spite of this error, all the streamlines computed using edge maps will be pairwise disjoint. Furthermore, our representation stores the error explicitly, and thus can be used to produce more informative visualizations. Given a piecewise-linear interpolated vector field, a recent result [15] shows that there are only 23 possible map classes for a triangle, permitting a concise description of flow behaviors. This work describes the details of computing edge maps, provides techniques to quantify and refine edge map error, and gives qualitative and visual comparisons to more traditional techniques. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • View point evaluation and streamline filtering for flow visualization

    Page(s): 83 - 90
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2810 KB) |  | HTML iconHTML  

    Visualization of flow fields with geometric primitives is often challenging due to occlusion that is inevitably introduced by 3D streamlines. In this paper, we present a novel view-dependent algorithm that can minimize occlusion and reveal important flow features for three dimensional flow fields. To analyze regions of higher importance, we utilize Shannon's entropy as a measure of vector complexity. An entropy field in the form of a three dimensional volume is extracted from the input vector field. To utilize this view-independent complexity measure for view-dependent calculations, we introduce the notion of a maximal entropy projection (MEP) framebuffer, which stores maximal entropy values as well as the corresponding depth values for a given viewpoint. With this information, we develop a view-dependent streamline selection algorithm that can evaluate and choose streamlines that will cause minimum occlusion to regions of higher importance. Based on a similar concept, we also propose a viewpoint selection algorithm that works hand-in-hand with our streamline selection algorithm to maximize the visibility of high complexity regions in the flow field. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Dual space analysis of turbulent combustion particle data

    Page(s): 91 - 98
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (8254 KB) |  | HTML iconHTML  

    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. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Analyzing information transfer in time-varying multivariate data

    Page(s): 99 - 106
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2124 KB) |  | HTML iconHTML  

    Effective analysis and visualization of time-varying multivariate data is crucial for understanding complex and dynamic variable interaction and temporal evolution. Advances made in this area are mainly on query-driven visualization and correlation exploration. Solutions and techniques that investigate the important aspect of causal relationships among variables have not been sought. In this paper, we present a new approach to analyzing and visualizing time-varying multivariate volumetric and particle data sets through the study of information flow using the information-theoretic concept of transfer entropy. We employ time plot and circular graph to show information transfer for an overview of relations among all pairs of variables. To intuitively illustrate the influence relation between a pair of variables in the visualization, we modulate the color saturation and opacity for volumetric data sets and present three different visual representations, namely, ellipse, smoke, and metaball, for particle data sets. We demonstrate this information-theoretic approach and present our findings with three time-varying multivariate data sets produced from scientific simulations. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Impact of group size on spatial structure understanding tasks

    Page(s): 107 - 114
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (984 KB) |  | HTML iconHTML  

    Co-located collaborative tasks allow teams to leverage the skills of each individual member. While numerous guidelines exist to develop visualizations for individuals working on desktops, very little is known about how groups of individuals interpret and comprehend diverse types of visual constructs on larger displays. To study whether group size impacts the collective understanding of relationships in three-dimensional (3D) spatial structures when using different types of presentation, we carried out three experiments. We compared individual performance at structure understanding tasks to performance of groups containing two or four members. We consider two alternate visualization techniques for extracting 3D structure information: a 3D view with animated rotations and a combination of one static 3D plus three static two-dimensional (2D) projection views. In general our studies suggest that as group size increases, so does accuracy but with a cost in efficiency. Our results also suggest that beyond a threshold limit in group size, performance on certain tasks begins to degrade. Regardless of group size, participants performed better when the display was presented in the animation condition instead of the multiple static views, except when large groups needed to relate the visualization to a physical counterpart. We summarize our results in terms of Steiner's model for explaining the effects of group size and task characteristics on group performance. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.