IEEE VIS Publication Dataset

A new method for the visualization of state transition graphs is presented. Visual information is reduced by clustering nodes, forming a tree structure of related clusters. This structure is visualized in three dimensions with concepts from cone trees and emphasis on symmetry. The resulting visualization makes it easier to relate features in the visualization of the state transition graph to semantic concepts in the corresponding process and vice versa.

In this paper, we present a new approach for the visualization of time-series data based on spirals. Different to classical bar charts and line graphs, the spiral is suited to visualize large data sets and supports much better the identification of periodic structures in the data. Moreover, it supports both the visualization of nominal and quantitative data based on a similar visualization metaphor. The extension of the spiral visualization to 3D gives access to concepts for zooming and focusing and linking in the data set. As such, spirals complement other visualization techniques for time series and specifically enhance the identication of periodic patterns.

We present the problem of visualizing time-varying medical data. Two medical imaging modalities are compared-MRI and dynamic SPECT. For each modality, we examine several derived scalar and vector quantities such as the change in intensity over time, the spatial gradient, and the change of the gradient over time. We compare several methods for presenting the data, including isosurfaces, direct volume rendering, and vector visualization using glyphs. These techniques may provide more information and context than methods currently used in practice; thus it is easier to discover temporal changes and abnormalities in a data set.

In this paper, we address the problem of historical data visualization. We describe the data acquisition, preparation, and visualization. Since the data contain four dimensions, the standard 3D exploration techniques have to be extended or appropriately adapted in order to enable interactive exploration. We discuss in detail two interaction concepts: (1) navigation with one fixed dimension, and (2) quasi 4D navigation allowing to simultaneously explore the four-dimensional space. In addition, we also present a picture-in-picture display mode, enabling the user to interactively view the data, while "flying with" a particular event, tracking its motion in time and space. Finally, we present a technique for guided exploration and animation generation, allowing for a vivid gain of insight into the historical data.

Distance fields are an important volume representation. A high quality distance field facilitates accurate surface characterization and gradient estimation. However, due to Nyquist's law, no existing volumetric methods based on the linear sampling theory can fully capture surface details, such as comers and edges, in 3D space. We propose a novel complete distance field representation (CDFR) that does not rely on Nyquist's sampling theory. To accomplish this, we construct a volume where each voxel has a complete description of all portions of surface that affect the local distance field. For any desired distance, we are able to extract a surface contour in true Euclidean distance, at any level of accuracy, from the same CDFR representation. Such point-based iso-distance contours have faithful per-point gradients and can be interactively visualized using splatting, providing per-point shaded image quality. We also demonstrate applying CDFR to a cutting edge design for manufacturing application involving high-complexity parts at unprecedented accuracy using only commonly available computational resources.

The authors propose three simple, but significant improvements to the OoCS (Out-of-Core Simplification) algorithm of P. Lindstrom (2000) which increase the quality of approximations and extend the applicability of the algorithm to an even larger class of compute systems. The original OoCS algorithm has memory complexity that depends on the size of the output mesh, but no dependency on the size of the input mesh. That is, it can be used to simplify meshes of arbitrarily large size, but the complexity of the output mesh is limited by the amount of memory available. Our first contribution is a version of OoCS that removes the dependency of having enough memory to hold (even) the simplified mesh. With our new algorithm, the whole process is made essentially independent of the available memory on the host computer. Our new technique uses disk instead of main memory, but it is carefully designed to avoid costly random accesses. Our two other contributions improve the quality of the approximations generated by OoCS. We propose a scheme for preserving surface boundaries which does not use connectivity information, and a scheme for constraining the position of the "representative vertex" of a grid cell to an optimal position inside the cell.

We present a simple denoising technique for geometric data represented as a semiregular mesh, based on locally adaptive Wiener filtering. The degree of denoising is controlled by a single parameter (an estimate of the relative noise level) and the time required for denoising is independent of the magnitude of the estimate. The performance of the algorithm is sufficiently fast to allow interactive local denoising.

This paper presents a new algorithm for the calculation of stream surfaces for tetrahedral grids. It propagates the surface through the tetrahedra, one at a time, calculating the intersections with the tetrahedral faces. The method allows us to incorporate topological information from the cells, e.g. critical points. The calculations are based on barycentric coordinates, since this simplifies the theory and the algorithm. The stream surfaces are ruled surfaces inside each cell, and their construction starts with line segments on the faces. Our method supports the analysis of velocity fields resulting from computational fluid dynamics (CFD) simulations.

Animal dissection for the scientific examination of organ subsystems is a delicate procedure. Performing this procedure under the complex environment of microgravity presents additional challenges because of the limited training opportunities available that can recreate the altered gravity environment. Traditional astronaut crew training often occurs several months in advance of experimentation, provides limited realism, and involves complicated logistics. We have developed an interactive virtual environment that can simulate several common tasks performed during animal dissection. In this paper, we describe the imaging modality used to reconstruct the rat, provide an overview of the simulation environment and briefly discuss some of the techniques used to manipulate the virtual rat.

Acceleration techniques for volume ray-casting are primarily based on pre-computed data structures that allow one to efficiently traverse empty or homogeneous regions. In order to display volume data that successively undergoes color lookups, however, the data structures have to be re-built continuously. In this paper we propose a technique that circumvents this drawback using hardware accelerated texture mapping. In a first rendering pass we employ graphics hardware to interactively determine for each ray where the material is hit. In a second pass ray-casting is performed, but ray traversal starts right in front of the previously determined regions. The algorithm enables interactive classification and it considerably accelerates the view dependent display of selected materials and surfaces from volume data. In contrast to other techniques that are solely based on texture mapping our approach requires less memory and accurately performs the composition of material contributions along the ray.

We describe a virtual reality environment for visualizing tensor-valued volumetric datasets acquired with diffusion tensor magnetic resonance imaging (DT-MRI). We have prototyped a virtual environment that displays geometric representations of the volumetric second-order diffusion tensor data and are developing interaction and visualization techniques for two application areas: studying changes in white-matter structures after gamma-knife capsulotomy and pre-operative planning for brain tumor surgery. Our feedback shows that compared to desktop displays, our system helps the user better interpret the large and complex geometric models, and facilitates communication among a group of users.

Presents a method to estimate illumination dependent properties in image synthesis prior to rendering. A preprocessing step is described in which a linear image basis is developed and a lighting-independent formulation defined. A reflection function, similar to hemispherical reflectance, approximates normal Lambertian shading. Intensity errors resulting from this approximation are reduced by use of a polynomial gamma correction function and scaling to a normalized display range. This produces images that are similar to normal Lambertian shading without employing the maximum (max) function. For a single object view, images can then be expressed in a linear form so that lighting direction can be factored out. During normal rendering, image quantities for arbitrary light directions can be found without rendering. This method is demonstrated for estimating image intensity and level-of-detail error prior to rendering an object.

We present the results of an evaluation of the ARCHAVE system, an immersive virtual reality environment for archaeological research. ARCHAVE is implemented in a Cave. The evaluation studied re- searchers analyzing lamp and coin finds throughout the excavation trenches at the Petra Great Temple site in Jordan. Experienced ar- chaeologists used our system to study excavation data, confirming existing hypotheses and postulating new theories they had not been able to discover without the system. ARCHAVE provided access to the excavation database, and researchers were able to examine the data in the context of a life-size representation of the present day architectural ruins of the temple. They also had access to a minia- ture model for site-wide analysis. Because users quickly became comfortable with the interface, they concentrated their efforts on examining the data being retrieved and displayed. The immersive VR visualization of the recovered information gave them the op- portunity to explore it in a new and dynamic way and, in several cases, enabled them to make discoveries that opened new lines of investigation about the excavation.

The paper describes a novel application of feature preserving mesh simplification to the problem of managing large, multidimensional datasets during scientific visualization. To allow this, we view a scientific dataset as a triangulated mesh of data elements, where the attributes embedded in each element form a set of properties arrayed across the surface of the mesh. Existing simplification techniques were not designed to address the high dimensionality that exists in these types of datasets. In addition, vertex operations that relocate, insert, or remove data elements may need to be modified or restricted. Principal component analysis provides an algorithm-independent method for compressing a dataset's dimensionality during simplification. Vertex locking forces certain data elements to maintain their spatial locations; this technique is also used to guarantee a minimum density in the simplified dataset. The result is a visualization that significantly reduces the number of data elements to display, while at the same time ensuring that high-variance regions of potential interest remain intact. We apply our techniques to a number of well-known feature preserving algorithms, and demonstrate their applicability in a real-world context by simplifying a multidimensional weather dataset. Our results show a significant improvement in execution time with only a small reduction in accuracy; even when the dataset was simplified to 10% of its original size, average per attribute error was less than 1%.

For psychophysical studies in spatial cognition a virtual model of the picturesque old town of Tubingen has been constructed. In order to perform psychophysical experiments in highly realistic virtual environments the model is based on high quality texture maps adding up to several hundreds of MBytes. To accomplish the required real-time frame updates, view frustum and occlusion culling without visibility pre-processing, levels of detail, and texture compression are applied in an interleaved manner. Shared memory communication and a standard PC with two commodity graphics cards is used to enable the powerful combination of those techniques because this combination is not yet available on a single graphics card.

We describe a visualization system designed for interactive study of proteins in the field of computational biology. Our system incorporates multiple, custom, three-dimensional and two-dimensional linked views of the proteins. We take advantage of modem commodity graphics cards, which are typically designed for games rather than scientific visualization applications, to provide instantaneous linking between views and three-dimensional interactivity on standard personal computers. Furthermore, we anticipate the usefulness of game techniques such as bump maps and skinning for scientific applications.

The focus of this paper is to evaluate the usefulness of some basic feature tracking algorithms as analysis tools for combustion datasets by application to a dataset modeling autoignition. Features defined as areas of high intermediate concentrations were examined to explore the initial phases in the autoignition process.

The complex geometry of the human brain contains many folds and fissures, making it impossible to view the entire surface at once. Since most of the cortical activity occurs on these folds, it is desirable to be able to view the entire surface of the brain in a single view. This can be achieved using quasi-conformal flat maps of the cortical surface. Computational and visualization tools are now needed to be able to interact with these flat maps of the brain to gain information about spatial and functional relationships that might not otherwise be apparent. Such information can contribute to earlier diagnostic tools for diseases and improved treatment. Our group is developing visualization and analysis tools that will help elucidate new information about the human brain through the interaction between a cortical surface and its corresponding quasiconformal flat map.

The case study presents a medical Web service for the automatic analysis of CTA (computer tomography angiography) datasets. It aims at the detection and evaluation of intracranial aneurysms which are malformations of cerebral blood vessels. To obtain a standardized 3D visualization, digital videos are automatically generated. The time-consuming video production caused by the manual delineation of structures, software based volume rendering, and the interactive definition of an optimized camera path is considerably improved with a fully automatic strategy. Therefore, a previously suggested approach (C. Rezk-Salama, 2000) is applied which uses an optimized transfer function as a template and automatically adapts it to an individual dataset. Furthermore, we introduce hardware-accelerated morphologic filtering in order to detect the location of mid-size and giant aneurysms. The actual generation of the video is finally integrated into a hardware accelerated off-screen rendering process based on 3D texture mapping, ensuring fast visualization of high quality. Overall, clinical routine can be considerably assisted by providing a Web based service combining automatic detection and standardized visualization.

It is of significant interest for neurological studies to determine and visualize neuronal fiber pathways in the human brain. By exploiting the capability of diffusion tensor magnetic resonance imaging to detect local orientations of neuronal fibers, we have developed a system of algorithms to reconstruct, visualize and quantify neuronal fiber pathways in vivo. Illustrative results show that the system is a promising tool for visual analysis of fiber connectivity and quantitative studies of neuronal fibers.