IEEE VIS Publication Dataset

In this paper, we present two novel texture-based techniques to visualize uncertainty in time-dependent 2D flow fields. Both methods use semi-Lagrangian texture advection to show flow direction by streaklines and convey uncertainty by blurring these streaklines. The first approach applies a cross advection perpendicular to the flow direction. The second method employs isotropic diffusion that can be implemented by Gaussian filtering. Both methods are derived from a generic filtering process that is incorporated into the traditional texture advection pipeline. Our visualization methods allow for a continuous change of the density of flow representation by adapting the density of particle injection. All methods can be mapped to efficient GPU implementations. Therefore, the user can interactively control all important characteristics of the system like particle density, error influence, or dye injection to create meaningful illustrations of the underlying uncertainty. Even though there are many sources of uncertainties, we focus on uncertainty that occurs during data acquisition. We demonstrate the usefulness of our methods for the example of real-world fluid flow data measured with the particle image velocimetry (PIV) technique. Furthermore, we compare these techniques with an adapted multi-frequency noise approach.

In this paper we introduce GPU particle tracing for the visualization of 3D diffusion tensor fields. For about half a million particles, reconstruction of diffusion directions from the tensor field, time integration and rendering can be done at interactive rates. Different visualization options like oriented particles of diffusion-dependent shape, stream lines or stream tubes facilitate the use of particle tracing for diffusion tensor visualization. The proposed methods provide efficient and intuitive means to show the dynamics in diffusion tensor fields, and they accommodate the exploration of the diffusion properties of biological tissue.

The size and resolution of volume datasets in science and medicine are increasing at a rate much greater than the resolution of the screens used to view them. This limits the amount of data that can be viewed simultaneously, potentially leading to a loss of overall context of the data when the user views or zooms into a particular area of interest. We propose a focus+context framework that uses various standard and advanced magnification lens rendering techniques to magnify the features of interest, while compressing the remaining volume regions without clipping them away completely. Some of these lenses can be interactively configured by the user to specify the desired magnification patterns, while others are feature-adaptive. All our lenses are accelerated on the GPU. They allow the user to interactively manage the available screen area, dedicating more area to the more resolution-important features.

We have developed a real-time experiment-control and data-display system for a novel microscope, the 3D-force microscope (3DFM), which is designed for nanometer-scale and nanoNewton-force biophysical experiments. The 3DFM software suite synthesizes the several data sources from the 3DFM into a coherent view and provides control over data collection and specimen manipulation. Herein, we describe the system architecture designed to handle the several feedback loops and data flows present in the microscope and its control system. We describe the visualization techniques used in the 3DFM software suite, where used, and on which types of data. We present feedback from our scientist-users regarding the usefulness of these techniques, and we also present lessons learned from our successive implementations.

The field of visualization is getting mature. Many problems have been solved, and new directions are sought for. In order to make good choices, an understanding of the purpose and meaning of visualization is needed. Especially, it would be nice if we could assess what a good visualization is. In this paper an attempt is made to determine the value of visualization. A technological viewpoint is adopted, where the value of visualization is measured based on effectiveness and efficiency. An economic model of visualization is presented, and benefits and costs are established. Next, consequences (brand limitations of visualization are discussed (including the use of alternative methods, high initial costs, subjective/less, and the role of interaction), as well as examples of the use of the model for the judgement of existing classes of methods and understanding why they are or are not used in practice. Furthermore, two alternative views on visualization are presented and discussed: viewing visualization as an art or as a scientific discipline. Implications and future directions are identified.

In this case study, a data-oriented approach is used to visualize a complex digital signal processing pipeline. The pipeline implements a frequency modulated (FM) software-defined radio (SDR). SDR is an emerging technology where portions of the radio hardware, such as filtering and modulation, are replaced by software components. We discuss how an SDR implementation is instrumented to illustrate the processes involved in FM transmission and reception. By using audio-encoded images, we illustrate the processes involved in radio, such as how filters are used to reduce noise, the nature of a carrier wave, and how frequency modulation acts on a signal. The visualization approach used in this work is very effective in demonstrating advanced topics in digital signal processing and is a useful tool for experimenting with the software radio design.

Tensor topology is useful in providing a simplified and yet detailed representation of a tensor field. Recently the field of 3D tensor topology is advanced by the discovery that degenerate tensors usually form lines in their most basic configurations. These lines form the backbone for further topological analysis. A number of ways for extracting and tracing the degenerate tensor lines have also been proposed. In this paper, we complete the previous work by studying the behavior and extracting the separating surfaces emanating from these degenerate lines. First, we show that analysis of eigenvectors around a 3D degenerate tensor can be reduced to 2D. That is, in most instances, the 3D separating surfaces are just the trajectory of the individual 2D separatrices which includes trisectors and wedges. But the proof is by no means trivial since it is closely related to perturbation theory around a pair of singular slate. Such analysis naturally breaks down at the tangential points where the degenerate lines pass through the plane spanned by the eigenvectors associated with the repeated eigenvalues. Second, we show that the separatrices along a degenerate line may switch types (e.g. trisectors to wedges) exactly at the points where the eigenplane is tangential to the degenerate curve. This property leads to interesting and yet complicated configuration of surfaces around such transition points. Finally, we apply the technique to several common data sets to verify its correctness.

In this paper, we present a topological approach for simplifying continuous functions defined on volumetric domains. We introduce two atomic operations that remove pairs of critical points of the function and design a combinatorial algorithm that simplifies the Morse-Smale complex by repeated application of these operations. The Morse-Smale complex is a topological data structure that provides a compact representation of gradient flow between critical points of a function. Critical points paired by the Morse-Smale complex identify topological features and their importance. The simplification procedure leaves important critical points untouched, and is therefore useful for extracting desirable features. We also present a visualization of the simplified topology.

Topological concepts and techniques have been broadly applied in computer graphics and geometric modeling. However, the homotopy type of a mapping between two surfaces has not been addressed before. In this paper, we present a novel solution to the problem of computing continuous maps with different homotopy types between two arbitrary triangle meshes with the same topology. Inspired by the rich theory of topology as well as the existing body of work on surface mapping, our newly-developed mapping techniques are both fundamental and unique, offering many attractive advantages. First, our method allows the user to change the homotopy type or global structure of the mapping with minimal intervention. Moreover, to locally affect shape correspondence, we articulate a new technique that robustly satisfies hard feature constraints, without the use of heuristics to ensure validity. In addition to acting as a useful tool for computer graphics applications, our method can be used as a rigorous and practical mechanism for the visualization of abstract topological concepts such as homotopy type of surface mappings, homology basis, fundamental domain, and universal covering space. At the core of our algorithm is a procedure for computing the canonical homology basis and using it as a common cut graph for any surface with the same topology. We demonstrate our results by applying our algorithm to shape morphing in this paper.

Little is known about the cognitive abilities which influence the comprehension of scientific and information visualizations and what properties of the visualization affect comprehension. Our goal in this paper is to understand what makes visualizations difficult. We address this goal by examining the spatial ability differences in a diverse population selected for spatial ability variance. For example, how is, spatial ability related to visualization comprehension? What makes a particular visualization difficult or time intensive for specific groups of subjects? In this paper, we present the results of an experiment designed to answer these questions. Fifty-six subjects were tested on a basic visualization task and given standard paper tests of spatial abilities. An equal number of males and females were recruited in this study in order to increase spatial ability variance. Our results show that high spatial ability is correlated with accuracy on our three-dimensional visualization test, but not with time. High spatial ability subjects also had less difficulty with object complexity and the hidden properties of an object.

In a visualization of a three-dimensional dataset, the insights gained are dependent on what is occluded and what is not. Suggestion of interesting viewpoints can improve both the speed and efficiency of data understanding. This paper presents a view selection method designed for volume rendering. It can be used to find informative views for a given scene, or to find a minimal set of representative views which capture the entire scene. It becomes particularly useful when the visualization process is non-interactive - for example, when visualizing large datasets or time-varying sequences. We introduce a viewpoint "goodness" measure based on the formulation of entropy from information theory. The measure takes into account the transfer function, the data distribution and the visibility of the voxels. Combined with viewpoint properties like view-likelihood and view-stability, this technique can be used as a guide, which suggests "interesting" viewpoints for further exploration. Domain knowledge is incorporated into the algorithm via an importance transfer function or volume. This allows users to obtain view selection behaviors tailored to their specific situations. We generate a view space partitioning, and select one representative view for each partition. Together, this set of views encapsulates the "interesting" and distinct views of the data. Viewpoints in this set can be used as starting points for interactive exploration of the data, thus reducing the human effort in visualization. In non-interactive situations, such a set can be used as a representative visualization of the dataset from all directions.

Real-time rendering of massively textured 3D scenes usually involves two major problems: Large numbers of texture switches are a well-known performance bottleneck and the set of simultaneously visible textures is limited by the graphics memory. This paper presents a level-of-detail texturing technique that overcomes both problems. In a preprocessing step, the technique creates a hierarchical data structure for all textures used by scene objects, and it derives texture atlases at different resolutions. At runtime, our texturing technique requires only a small set of these texture atlases, which represent scene textures in an appropriate size depending on the current camera position and screen resolution. Independent of the number and total size of all simultaneously visible textures, the achieved frame rates are similar to that of rendering the scene without any texture switches. Since the approach includes dynamic texture loading, the total size of the textures is only limited by the hard disk capacity. The technique is applicable for any 3D scenes whose scene objects are primarily distributed in a plane, such as in the case of 3D city models or outdoor scenes in computer games. Our approach has been successfully applied to massively textured, large-scale 3D city models.

VisTrails is a new system that enables interactive multiple-view visualizations by simplifying the creation and maintenance of visualization pipelines, and by optimizing their execution. It provides a general infrastructure that can be combined with existing visualization systems and libraries. A key component of VisTrails is the visualization trail (vistrail), a formal specification of a pipeline. Unlike existing dataflow-based systems, in VisTrails there is a clear separation between the specification of a pipeline and its execution instances. This separation enables powerful scripting capabilities and provides a scalable mechanism for generating a large number of visualizations. VisTrails also leverages the vistrail specification to identify and avoid redundant operations. This optimization is especially useful while exploring multiple visualizations. When variations of the same pipeline need to be executed, substantial speedups can be obtained by caching the results of overlapping subsequences of the pipelines. In this paper, we describe the design and implementation of VisTrails, and show its effectiveness in different application scenarios.

We present a visual analysis and exploration of fluid flow through a cooling jacket. Engineers invest a large amount of time and serious effort to optimize the flow through this engine component because of its important role in transferring heat away from the engine block. In this study we examine the design goals that engineers apply in order to construct an ideal-as-possible cooling jacket geometry and use a broad range of visualization tools in order to analyze, explore, and present the results. We systematically employ direct, geometric, and texture-based flow visualization techniques as well as automatic feature extraction and interactive feature-based methodology. And we discuss the relative advantages and disadvantages of these approaches as well as the challenges, both technical and perceptual with this application. The result is a feature-rich state-of-the-art flow visualization analysis applied to an important and complex data set from real-world computational fluid dynamics simulations.

In this application paper, we report on over fifteen years of experience with relativistic and astrophysical visualization, which has been culminating in a substantial engagement for visualization in the Einstein Year 2005 - the 100th anniversary of Einstein's publications on special relativity, the photoelectric effect, and Brownian motion. This paper focuses on explanatory and illustrative visualizations used to communicate aspects of the difficult theories of special and general relativity, their geometric structure, and of the related fields of cosmology and astrophysics. We discuss visualization strategies, motivated by physics education and didactics of mathematics, and describe what kind of visualization methods have proven to be useful for different types of media, such as still images in popular-science magazines, film contributions to TV shows, oral presentations, or interactive museum installations. Although our visualization tools build upon existing methods and implementations, these techniques have been improved by several novel technical contributions like image-based special relativistic rendering on GPUs, an extension of general relativistic ray tracing to manifolds described by multiple charts, GPU-based interactive visualization of gravitational light deflection, as well as planetary terrain rendering. The usefulness and effectiveness of our visualizations are demonstrated by reporting on experiences with, and feedback from, recipients of visualizations and collaborators.

The genus of a knot or link can be defined via Seifert surfaces. A Seifert surface of a knot or link is an oriented surface whose boundary coincides with that, knot or link. Schematic images of these surfaces are shown in every text book on knot theory, but from these it is hard to understand their shape and structure. In this paper the visualization of such surfaces is discussed. A method is presented to produce different styles of surfaces for knots and links, starting from the so-called braid representation. Also, it is shown how closed oriented surfaces can be generated in which the knot is embedded, such that the knot subdivides the surface into two parts. These closed surfaces provide a direct visualization of the genus of a knot.

Analysis of phenomena that simultaneously occur on different spatial and temporal scales requires adaptive, hierarchical schemes to reduce computational and storage demands. Adaptive mesh refinement (AMR) schemes support both refinement in space that results in a time-dependent grid topology, as well as refinement in time that results in updates at higher rates for refined levels. Visualization of AMR data requires generating data for absent refinement levels at specific time steps. We describe a solution starting from a given set of "key frames" with potentially different grid topologies. The presented work was developed in a project involving several research institutes that collaborate in the field of cosmology and numerical relativity. AMR data results from simulations that are run on dedicated compute machines and is thus stored centrally, whereas the analysis of the data is performed on the local computers of the scientists. We built a distributed solution using remote procedure calls (RPC). To keep the application responsive, we split the bulk data transfer from the RPC response and deliver it asynchronously as a binary stream. The number of network round-trips is minimized by using high level operations. In summary, we provide an application for exploratory visualization of remotely stored AMR data.

Diffusion tensor imaging is a magnetic resonance imaging method which has gained increasing importance in neuroscience and especially in neurosurgery. It acquires diffusion properties represented by a symmetric 2nd order tensor for each voxel in the gathered dataset. From the medical point of view, the data is of special interest due lo different diffusion characteristics of varying brain tissue allowing conclusions about the underlying structures such as while matter tracts. An obvious way to visualize this data is to focus on the anisotropic areas using the major eigenvector for tractography and rendering lines for visualization of the simulation results. Our approach extends this technique to avoid line representation since lines lead 10 very complex illustrations and furthermore are mistakable. Instead, we generate surfaces wrapping bundles of lines. Thereby, a more intuitive representation of different tracts is achieved.

Line primitives are a very powerful visual attribute used for scientific visualization and in particular for 3D vector-field visualization. We extend the basic line primitives with additional visual attributes including color, line width, texture and orientation. To implement the visual attributes we represent the stylized line primitives as generalized cylinders. One important contribution of our work is an efficient rendering algorithm for stylized lines, which is hybrid in the sense that it uses both CPU and GPU based rendering. We improve the depth perception with a shadow algorithm. We present several applications for the visualization with stylized lines among which are the visualizations of 3D vector fields and molecular structures.

This paper describes an experimental study of three perceptual properties of motion: flicker, direction, and velocity. Our goal is to understand how to apply these properties to represent data in a visualization environment. Results from our experiments show that all three properties can encode multiple data values, but that minimum visual differences are needed to ensure rapid and accurate target detection: flicker must be coherent and must have a cycle length of 120 milliseconds or greater, direction must differ by at least 20┬░, and velocity must differ by at least 0.43┬░ of subtended visual angle. We conclude with an overview of how we are applying our results to real-world data, and then discuss future work we plan to pursue.