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Tensor Field Visualization,
Topological Data Analysis
Epinome: A Visual-Analytics Workbench for Epidemiology Data|
Y. Livnat, T.-M. Rhyne, M. Samore. In IEEE Computer Graphics and Applications, Vol. 32, No. 2, pp. 89--95. 2012.
Effective detection of and response to infectious disease outbreaks depend on the ability to capture and analyze information and on how public health officials respond to this information. Researchers have developed various surveillance systems to automate data collection, analysis, and alert generation, yet the massive amount of collected data often leads to information overload. To improve decision-making in outbreak detection and response, it's important to understand how outbreak investigators seek relevant information. Studying their information-search strategies can provide insight into their cognitive biases and heuristics. Identifying the presence of such biases will enable the development of tools that counter balance them and help users develop alternative scenarios.
We implemented a large-scale high-fidelity simulation of scripted infectious-disease outbreaks to help us study public health practitioners' information- search strategies. We also developed Epinome, an integrated visual-analytics investigation system. Epinome caters to users' needs by providing a variety of investigation tools. It facilitates user studies by recording which tools they used, when, and how. (See the video demonstration of Epinome at www.sci.utah.edu/gallery2/v/ software/epinome.) Epinome provides a dynamic environment that seamlessly evolves and adapts to user tasks and needs. It introduces four userinteraction paradigms in public health:
• an evolving visual display,
Using Epinome, users can replay simulation scenarios, investigate an unfolding outbreak using a variety of visualization tools, and steer the simulation by implementing different public health policies at predefined decision points. Epinome records user actions, such as tool selection, interactions with each tool, and policy changes, and stores them in a database for postanalysis. A psychology team can then use that information to study users' search strategies.
From Quantification to Visualization: A Taxonomy of Uncertainty Visualization Approaches|
K. Potter, P. Rosen, C.R. Johnson. In Uncertainty Quantification in Scientific Computing, IFIP Advances in Information and Communication Technology Series, Vol. 377, Edited by Andrew Dienstfrey and Ronald Boisvert, Springer, pp. 226--249. 2012.
Quantifying uncertainty is an increasingly important topic across many domains. The uncertainties present in data come with many diverse representations having originated from a wide variety of domains. Communicating these uncertainties is a task often left to visualization without clear connection between the quantification and visualization. In this paper, we first identify frequently occurring types of uncertainty. Second, we connect those uncertainty representations to ones commonly used in visualization. We then look at various approaches to visualizing this uncertainty by partitioning the work based on the dimensionality of the data and the dimensionality of the uncertainty. We also discuss noteworthy exceptions to our taxonomy along with future research directions for the uncertainty visualization community.
Keywords: scidac, netl, uncertainty visualization
FluoRender: An Application of 2D Image Space Methods for 3D and 4D Confocal Microscopy Data Visualization in Neurobiology Research|
Y. Wan, H. Otsuna, C.-B. Chien, C.D. Hansen. In Proceedings of Pacific Vis 2012, Incheon, Korea, pp. 201--208. 2012.
2D image space methods are processing methods applied after the volumetric data are projected and rendered into the 2D image space, such as 2D filtering, tone mapping and compositing. In the application domain of volume visualization, most 2D image space methods can be carried out more efficiently than their 3D counterparts. Most importantly, 2D image space methods can be used to enhance volume visualization quality when applied together with volume rendering methods. In this paper, we present and discuss the applications of a series of 2D image space methods as enhancements to confocal microscopy visualizations, including 2D tone mapping, 2D compositing, and 2D color mapping. These methods are easily integrated with our existing confocal visualization tool, FluoRender, and the outcome is a full-featured visualization system that meets neurobiologists' demands for qualitative analysis of confocal microscopy data.
Output-Coherent Image-Space LIC for Surface Flow Visualization|
J. Huang, W. Pei, C. Wen, G. Chen, W. Chen, H. Bao. In Proceedings of the IEEE Pacific Visualization Symposium 2012, Korea, pp. 137--144. 2012.
Image-space line integral convolution (LIC) is a popular approach for visualizing surface vector fields due to its simplicity and high efficiency. To avoid inconsistencies or color blur during the user interactions in the image-space approach, some methods use surface parameterization or 3D volume texture for the effect of smooth transition, which often require expensive computational or memory cost. Furthermore, those methods cannot achieve consistent LIC results in both granularity and color distribution on different scales.
This paper introduces a novel image-space LIC for surface flows that preserves the texture coherence during user interactions. To make the noise textures under different viewpoints coherent, we propose a simple texture mapping technique that is local, robust and effective. Meanwhile, our approach pre-computes a sequence of mipmap noise textures in a coarse-to-fine manner, leading to consistent transition when the model is zoomed. Prior to perform LIC in the image space, the mipmap noise textures are mapped onto each triangle with randomly assigned texture coordinates. Then, a standard image-space LIC based on the projected vector fields is performed to generate the flow texture. The proposed approach is simple and very suitable for GPU acceleration. Our implementation demonstrates consistent and highly efficient LIC visualization on a variety of datasets.
Design of 2D Time-Varying Vector Fields|
G. Chen, V. Kwatra, L.-Y. Wei, C.D. Hansen, E. Zhang. In IEEE Transactions on Visualization and Computer Graphics TVCG, Vol. 18, No. 10, pp. 1717--1730. 2012.
Understanding Quasi-Periodic Fieldlines and Their Topology in Toroidal Magnetic Fields|
A.R. Sanderson, G. Chen, X. Tricoche, E. Cohen. In Topological Methods in Data Analysis and Visualization II, Edited by R. Peikert and H. Carr and H. Hauser and R. Fuchs, Springer, pp. 125--140. 2012.
Mesh-Driven Vector Field Clustering and Visualization: An Image-Based Approach|
Z. Peng, E. Grundy, R.S. Laramee, G. Chen, N. Croft. In IEEE Transactions on Visualization and Computer Graphics, 2011, Vol. 18, No. 2, pp. 283--298. February, 2012.
Vector field visualization techniques have evolved very rapidly over the last two decades, however, visualizing vector fields on complex boundary surfaces from computational flow dynamics (CFD) still remains a challenging task. In part, this is due to the large, unstructured, adaptive resolution characteristics of the meshes used in the modeling and simulation process. Out of the wide variety of existing flow field visualization techniques, vector field clustering algorithms offer the advantage of capturing a detailed picture of important areas of the domain while presenting a simplified view of areas of less importance. This paper presents a novel, robust, automatic vector field clustering algorithm that produces intuitive and insightful images of vector fields on large, unstructured, adaptive resolution boundary meshes from CFD. Our bottom-up, hierarchical approach is the first to combine the properties of the underlying vector field and mesh into a unified error-driven representation. The motivation behind the approach is the fact that CFD engineers may increase the resolution of model meshes according to importance. The algorithm has several advantages. Clusters are generated automatically, no surface parameterization is required, and large meshes are processed efficiently. The most suggestive and important information contained in the meshes and vector fields is preserved while less important areas are simplified in the visualization. Users can interactively control the level of detail by adjusting a range of clustering distance measure parameters. We describe two data structures to accelerate the clustering process. We also introduce novel visualizations of clusters inspired by statistical methods. We apply our method to a series of synthetic and complex, real-world CFD meshes to demonstrate the clustering algorithm results.
Keywords: Vector Field Visualization, Clustering, Feature-based, Surfaces
Simplification of Node Position Data for Interactive Visualization of Dynamic Datasets|
P. Rosen, V. Popescu. In IEEE Transactions on Visualization and Computer Graphics (IEEE Visweek 2012 TVCG Track), pp. 1537--1548. 2012.
PubMed ID: 22025753
PubMed Central ID: PMC3411892
We propose to aid the interactive visualization of time-varying spatial datasets by simplifying node position data over the entire simulation as opposed to over individual states. Our approach is based on two observations. The first observation is that the trajectory of some nodes can be approximated well without recording the position of the node for every state. The second observation is that there are groups of nodes whose motion from one state to the next can be approximated well with a single transformation. We present dataset simplification techniques that take advantage of this node data redundancy. Our techniques are general, supporting many types of simulations, they achieve good compression factors, and they allow rigorous control of the maximum node position approximation error. We demonstrate our approach in the context of finite element analysis data, of liquid flow simulation data, and of fusion simulation data.
VisBricks: Multiform Visualization of Large, Inhomogeneous Data|
A. Lex, H. Schulz, M. Streit, C. Partl, D. Schmalstieg. In IEEE Transactions on Visualization and Computer Graphics (InfoVis '11), Vol. 17, No. 12, 2011.
Large volumes of real-world data often exhibit inhomogeneities: vertically in the form of correlated or independent dimensions and horizontally in the form of clustered or scattered data items. In essence, these inhomogeneities form the patterns in the data that researchers are trying to find and understand. Sophisticated statistical methods are available to reveal these patterns, however, the visualization of their outcomes is mostly still performed in a one-view-fits-all manner, In contrast, our novel visualization approach, VisBricks, acknowledges the inhomogeneity of the data and the need for different visualizations that suit the individual characteristics of the different data subsets. The overall visualization of the entire data set is patched together from smaller visualizations, there is one VisBrick for each cluster in each group of interdependent dimensions. Whereas the total impression of all VisBricks together gives a comprehensive high-level overview of the different groups of data, each VisBrick independently shows the details of the group of data it represents, State-of-the-art brushing and visual linking between all VisBricks furthermore allows the comparison of the groupings and the distribution of data items among them. In this paper, we introduce the VisBricks visualization concept, discuss its design rationale and implementation, and demonstrate its usefulness by applying it to a use case from the field of biomedicine.
Context-Preserving Visual Links|
M. Steinberger, M. Waldner, M. Streit, A. Lex, D. Schmalstieg. In IEEE Transactions on Visualization and Computer Graphics (InfoVis '11), Vol. 17, No. 12, 2011.
Evaluating, comparing, and interpreting related pieces of information are tasks that are commonly performed during visual data analysis and in many kinds of information-intensive work. Synchronized visual highlighting of related elements is a well-known technique used to assist this task. An alternative approach, which is more invasive but also more expressive is visual linking in which line connections are rendered between related elements. In this work, we present context-preserving visual links as a new method for generating visual links. The method specifically aims to fulfill the following two goals: first, visual links should minimize the occlusion of important information; second, links should visually stand out from surrounding information by minimizing visual interference. We employ an image-based analysis of visual saliency to determine the important regions in the original representation. A consequence of the image-based approach is that our technique is application-independent and can be employed in a large number of visual data analysis scenarios in which the underlying content cannot or should not be altered. We conducted a controlled experiment that indicates that users can find linked elements in complex visualizations more quickly and with greater subjective satisfaction than in complex visualizations in which plain highlighting is used. Context-preserving visual links were perceived as visually more attractive than traditional visual links that do not account for the context information.
Visualizing the Effects of Logically Combined Filters|
T. Geymayer, A. Lex, M. Streit, D. Schmalstieg. In Proceedings of the Conference on Information Visualisation (IV '11), IEEE, pp. 47--52. 2011.
Filtering data is an essential process in a drill-down analysis of large data sets. Filtering can be necessary for several reasons. The main objective for filters is to uncover the relevant subsets of a dataset. Another, equally relevant goal is to reduce a dataset to dimensions to which either visualization or algorithmic analysis techniques scale. However, with multiple filters applied and possibly even logically combined, it becomes difficult for users to judge the effects of a filter chain. In this paper we present a simple, yet effective way to interactively visualize a sequence of filters and logical combinations of these. Such a visualized filter-pipeline allows analysts to easily judge the effect of every single filter and also their combination on the data set under investigation and therefore, leads to a faster and more efficient workflow.
R.S. MacLeod, J.J.E. Blauer. In Multimodal Cardiovascular Imaging: Principles and Clinical Applications, Ch. 25, Edited by O. Pahlm and G. Wagner, McGraw Hill, 2011.
Atrial fibrillation (AF) is the most common form of cardiac arrhythmia so that a review of the role imaging in AF is a natural topic to include in this book. Further motivation comes from the fact that the treatment of AF probably includes more different forms of imaging, often merged or combined in a variety of ways, than perhaps any other clinical intervention. A typical clinical electrophysiology lab for the treatment of AF usually contains no less than 6 and often more than 8 individual monitors, each rendering some form of image based information about the patient undergoing therapy. There is naturally great motivation to merge different images and different imaging modalities in the setting of AF but also very challenging because of a host of factors related to the small size, extremely thin walls, the large natural variation in atrial shape, and the fact that fibrillation is occurring so that atrial shape is changing rapidly and irregularly. Thus, the use of multimodal imaging has recently become a very active and challenging area of image processing and analysis research and development, driven by an enormous clinical need to understand and treat a disease that affects some 5 million Americans alone, a number that is predicted to increase to almost 16 million by 2050.
In this chapter we attempt to provide an overview of the large variety of imaging modalities and uses in the management and understanding of atrial fibrillation, with special emphasis on the most novel applications of magnetic resonance imaging (MRI) technology. To provide clinical and biomedical motivation, we outline the basics of the disease together with some contemporary hypotheses about its etiology and management. We then describe briefly the imaging modalities in common use in the management and research of AF, then focus on the use or MRI for all phases of the management of patients with AF and indicate some of the major engineering challenges that can motivate further progress.
Keywords: ablation, carma, cvrti, 5P41-RR012553-10
Scientific Discovery at the Exascale: Report from the (DOE) (ASCR) 2011 Workshop on Exascale Data Management, Analysis, and Visualization|
S. Ahern, A. Shoshani, K.L. Ma, A. Choudhary, T. Critchlow, S. Klasky, V. Pascucci. Note: Office of Scientific and Technical Information (OSTI), January, 2011.
Cyber Science and Engineering: A Report of the National Science Foundation Advisory Committee for Cyberinfrastructure Task Force on Grand Challenges|
J.T. Oden, O. Ghattas, J.L. King, B.I. Schneider, K. Bartschat, F. Darema, J. Drake, T. Dunning, D. Estep, S. Glotzer, M. Gurnis, C.R. Johnson, D.S. Katz, D. Keyes, S. Kiesler, S. Kim, J. Kinter, G. Klimeck, C.W. McCurdy, R. Moser, C. Ott, A. Patra, L. Petzold, T. Schlick, K. Schulten, V. Stodden, J. Tromp, M. Wheeler, S.J. Winter, C. Wu, K. Yelick. Note: NSF Report, 2011.
This document contains the findings and recommendations of the NSF – Advisory Committee for Cyberinfrastructure Task Force on Grand Challenges addressed by advances in Cyber Science and Engineering. The term Cyber Science and Engineering (CS&E) is introduced to describe the intellectual discipline that brings together core areas of science and engineering, computer science, and computational and applied mathematics in a concerted effort to use the cyberinfrastructure (CI) for scientific discovery and engineering innovations; CS&E is computational and data-based science and engineering enabled by CI. The report examines a host of broad issues faced in addressing the Grand Challenges of science and technology and explores how those can be met by advances in CI. Included in the report are recommendations for new programs and initiatives that will expand the portfolio of the Office of Cyberinfrastructure and that will be critical to advances in all areas of science and engineering that rely on the CI.
Advisory Committee for CyberInfrastructure Task Force on Software for Science and Engineering|
D. Keyes, V. Taylor, T. Hey, S. Feldman, G. Allen, P. Colella, P. Cummings, F. Darema, J. Dongarra, T. Dunning, M. Ellisman, I. Foster, W. Gropp, C.R. Johnson, C. Kamath, R. Madduri, M. Mascagni, S.G. Parker, P. Raghavan, A. Trefethen, S. Valcourt, A. Patra, F. Choudhury, C. Cooper, P. McCartney, M. Parashar, T. Russell, B. Schneider, J. Schopf, N. Sharp. Note: NSF Report, 2011.
The Software for Science and Engineering (SSE) Task Force commenced in June 2009 with a charge that consisted of the following three elements:Identify specific needs and opportunities across the spectrum of scientific software infrastructure. Characterize the specific needs and analyze technical gaps and opportunities for NSF to meet those needs through individual and systemic approaches. Design responsive approaches. Develop initiatives and programs led (or co-led) by NSF to grow, develop, and sustain the software infrastructure needed to support NSF’s mission of transformative research and innovation leading to scientific leadership and technological competitiveness. Address issues of institutional barriers. Anticipate, analyze and address both institutional and exogenous barriers to NSF’s promotion of such an infrastructure.
The SSE Task Force members participated in bi-weekly telecons to address the given charge. The telecons often included additional distinguished members of the scientific community beyond the task force membership engaged in software issues, as well as personnel from federal agencies outside of NSF who manage software programs. It was quickly acknowledged that a number of reports loosely and tightly related to SSE existed and should be leveraged. By September 2009, the task formed had formed three subcommittees focused on the following topics: (1) compute-intensive science, (2) data-intensive science, and (3) software evolution.
Visualization of Covariance and Cross-covariance Field|
C. Yang, D. Xiu, R.M. Kirby. In International Journal for Uncertainty Quantification, Vol. 3, No. 1, pp. 25--38. 2011.
We present a numerical technique to visualize covariance and cross-covariance fields of a stochastic simulation. The method is local in the sense that it demonstrates the covariance structure of the solution at a point with its neighboring locations. When coupled with an efficient stochastic simulation solver, our framework allows one to effectively concurrently visualize both the mean and (cross-)covariance information for two-dimensional (spatial) simulation results. Most importantly, the visualization provides the scientist a means to identify interesting correlation structure of the solution field. The mathematical setup is discussed, along with several examples to demonstrate the efficacy of this approach.
Consistent Approximation of Local Flow Behavior for 2D Vector Fields|
S. Jadhav, H. Bhatia, P.-T. Bremer, J.A. Levine, L.G. Nonato, V. Pascucci. In Mathematics and Visualization, Springer, pp. 141--159. Nov, 2011.
DOI: 10.1007/978-3-642-23175-9 10
Typically, vector fields are stored as a set of sample vectors at discrete locations. Vector values at unsampled points are defined by interpolating some subset of the known sample values. In this work, we consider two-dimensional domains represented as triangular meshes with samples at all vertices, and vector values on the interior of each triangle are computed by piecewise linear interpolation.
Many of the commonly used techniques for studying properties of the vector field require integration techniques that are prone to inconsistent results. Analysis based on such inconsistent results may lead to incorrect conclusions about the data. For example, vector field visualization techniques integrate the paths of massless particles (streamlines) in the flow or advect a texture using line integral convolution (LIC). Techniques like computation of the topological skeleton of a vector field, require integrating separatrices, which are streamlines that asymptotically bound regions where the flow behaves differently. Since these integrations may lead to compound numerical errors, the computed streamlines may intersect, violating some of their fundamental properties such as being pairwise disjoint. Detecting these computational artifacts to allow further analysis to proceed normally remains a significant challenge.
Direct Isosurface Visualization of Hex-Based High-Order Geometry and Attribute Representations|
T. Martin, E. Cohen, R.M. Kirby. In IEEE Transactions on Visualization and Computer Graphics (TVCG), Vol. PP, No. 99, pp. 1--14. 2011.
In this paper, we present a novel isosurface visualization technique that guarantees the accuarate visualization of isosurfaces with complex attribute data defined on (un-)structured (curvi-)linear hexahedral grids. Isosurfaces of high-order hexahedralbased finite element solutions on both uniform grids (including MRI and CT scans) and more complex geometry represent a domain of interest that can be rendered using our algorithm. Additionally, our technique can be used to directly visualize solutions and attributes in isogeometric analysis, an area based on trivariate high-order NURBS (Non-Uniform Rational B-splines) geometry and attribute representations for the analysis. Furthermore, our technique can be used to visualize isosurfaces of algebraic functions. Our approach combines subdivision and numerical root-finding to form a robust and efficient isosurface visualization algorithm that does not miss surface features, while finding all intersections between a view frustum and desired isosurfaces. This allows the use of view-independent transparency in the rendering process. We demonstrate our technique through a straightforward CPU implementation on both complexstructured and complex-unstructured geometry with high-order simulation solutions, isosurfaces of medical data sets, and isosurfaces of algebraic functions.
Topology Verfication for Isosurface Extraction|
T. Etiene, L.G. Nonato, C. Scheidegger, J. Tierny, T.J. Peters, V. Pascucci, R.M. Kirby, C.T. Silva. In IEEE Transactions on Visualization and Computer Graphics, pp. (accepted). 2011.
The broad goals of verifiable visualization rely on correct algorithmic implementations. We extend a framework for verification of isosurfacing implementations to check topological properties. Specifically, we use stratified Morse theory and digital topology to design algorithms which verify topological invariants. Our extended framework reveals unexpected behavior and coding mistakes in popular publicly-available isosurface codes.
A Conservered Developmental Patterning Network Produces Quantitatively Different Output in Multiple Species of Drosophila|
C. Fowlkes, K. Eckenrode, M. Bragdon, M.D. Meyer, Z. Wunderlich, L. Simirenko, C. Luengo, S. Keranen, C. Henriquez, D. Knowles, M. Biggin, M. Eisen, A. DePace. In PLoS Genetics, Vol. 7, No. 10:e1002346, pp. 17 pages. October, 2011.
Differences in the level, timing, or location of gene expression can contribute to alternative phenotypes at the molecular and organismal level. Understanding the origins of expression differences is complicated by the fact that organismal morphology and gene regulatory networks could potentially vary even between closely related species. To assess the scope of such changes, we used high-resolution imaging methods to measure mRNA expression in blastoderm embryos of Drosophila yakuba and Drosophila pseudoobscura and assembled these data into cellular resolution atlases, where expression levels for 13 genes in the segmentation network are averaged into species-specific, cellular resolution morphological frameworks. We demonstrate that the blastoderm embryos of these species differ in their morphology in terms of size, shape, and number of nuclei. We present an approach to compare cellular gene expression patterns between species, while accounting for varying embryo morphology, and apply it to our data and an equivalent dataset for Drosophila melanogaster. Our analysis reveals that all individual genes differ quantitatively in their spatio-temporal expression patterns between these species, primarily in terms of their relative position and dynamics. Despite many small quantitative differences, cellular gene expression profiles for the whole set of genes examined are largely similar. This suggests that cell types at this stage of development are conserved, though they can differ in their relative position by up to 3-4 cell widths and in their relative proportion between species by as much as 5-fold. Quantitative differences in the dynamics and relative level of a subset of genes between corresponding cell types may reflect altered regulatory functions between species. Our results emphasize that transcriptional networks can diverge over short evolutionary timescales and that even small changes can lead to distinct output in terms of the placement and number of equivalent cells.