IEEE VIS Publication Dataset

Visualization of topological information of a vector field can provide useful information on the structure of the field. However, in turbulent flows standard critical point visualization will result in a cluttered image which is difficult to interpret. This paper presents a technique for collapsing topologics. The governing idea is to classify the importance of the critical points in the topology. By only displaying the more important critical points, a simplified depiction of the topology can be provided. Flow consistency is maintained when collapsing the topology, resulting in a visualization which is consistent with the original topology. We apply the collapsing topology technique to a turbulent flow field.

Presents a method for the hierarchical representation of vector fields. Our approach is based on iterative refinement using clustering and principal component analysis. The input to our algorithm is a discrete set of points with associated vectors. The algorithm generates a top-down segmentation of the discrete field by splitting clusters of points. We measure the error of the various approximation levels by measuring the discrepancy between streamlines generated by the original discrete field and its approximations based on much smaller discrete data sets. Our method assumes no particular structure of the field, nor does it require any topological connectivity information. It is possible to generate multi-resolution representations of vector fields using this approach.

We present a novel approach to solving the cracking problem. The cracking problem arises in many contexts in scientific visualization and computer graphics modeling where there is need for an approximation based upon domain decomposition that is fine in certain regions and coarse in others. This includes surface rendering approximation of images and multiresolution terrain visualization. In general, algorithms based upon adaptive refinement strategies must deal with this problem. The approach presented here is simple and general. It is based upon the use of a triangular Coons patch. Both the basic idea of using a triangular Coons patch in this context and the particular Coons patch that is used constitute the novel contributions of the paper.

Perhaps the most effective instrument to simplify and to clarify the comprehension of any complex mathematical or scientific theory is through visualisation. Moreover using interactivity and 3D real time representations, one can easily explore and hence learn quickly in the virtual environments. The concept of virtual and safe laboratories has vast potentials in education. With the aid of computer simulations and 3D visualisations, many dangerous or cumbersome experiments may be implemented in the virtual environments, with rather small effort. Nonetheless visualisation alone is of little use if the respective simulation is not scientifically accurate. Hence a rigorous combination of precise computation as well as sophisticated visualisation, presented through some intuitive user interface is required to realise a virtual laboratory for education. We introduce Delta's Virtual Physics Laboratory, comprising a wide range of applications in the field of physics and astronomy, which can be implemented and used as an interactive learning tool on the World Wide Web.

Having a better way to represent and to interact with large geological models are topics of high interest in geoscience, and especially for oil and gas companies. We present the design and implementation of a visualization program that involves two main features. It is based on the central data model, in order to display in real time the modifications caused by the modeler. Furthermore, it benefits from the different immersive environments which give the user a much more accurate insight of the model than a regular computer screen. Then, we focus on the difficulties that come in the way of performance.

Microsatellite genotypes can have problems that are difficult to detect with existing tools. One such problem is null alleles. This paper presents a new visualization tool that helps to find and characterize these errors. The paper explains how the tool is used to analyze groups of genotypes and proposes other possible uses.

The detection of vortical phenomena in vector data is one of the key issues in many technical applications, in particular in flow visualization. Many existing approaches rely on purely local evaluation of the vector data. In order to overcome the limits of a local approach, we choose to combine a local method with a correlation of a pre-defined generic vortex with the data in a medium-scale region. Two different concepts of generic vortices were tested on various sets of flow velocity vector data. The approach is not limited to the two generic patterns suggested here. The method was found to successfully detect vortices in cases were other methods fail.

We present the design of a simulator for a prototype interventional magnetic resonance imaging scanner. This MRI scanner is integrated with an operating theater, enabling new techniques in minimally invasive surgery. The simulator is designed with a threefold purpose: to provide a rehearsal apparatus for practicing and modifying conventional procedures for use in the magnetic environment; to serve as a visualization workstation for procedure planning and previewing as well as a post-operative review; and to form the foundation of a laboratory workbench for the development of new surgical tools and procedures for minimally invasive surgery. The simulator incorporates pre-operative data, either MRI or CT exams, as well as data from commercial surgical planning systems. Dynamic control of the simulation and interactive display of pre-operative data in lieu of intra-operative data is handled via an opto-electronic tracking system. The resulting system is contributing insights into how best to perform visualization for this new surgical environment.

We present a method for compressing non-manifold polygonal meshes, i.e. polygonal meshes with singularities, which occur very frequently in the real-world. Most efficient polygonal compression methods currently available are restricted to a manifold mesh: they require a conversion process, and fail to retrieve the original model connectivity after decompression. The present method works by converting the original model to a manifold model, encoding the manifold model using an existing mesh compression technique, and clustering, or stitching together during the decompression process vertices that were duplicated earlier to faithfully recover the original connectivity. This paper focuses on efficiently encoding and decoding the stitching information. By separating connectivity from geometry and properties, the method avoids encoding vertices (and properties bound to vertices) multiple times; thus a reduction of the size of the bit-stream of about 10% is obtained compared with encoding the model as a manifold.

We present a new technique which enables direct volume rendering based on 3D texture mapping hardware, enabling shading as well as classification of the interpolated data. Our technique supports accurate lighting for a one directional light source, semi-transparent classification, and correct blending. To circumvent the limitations of one general classification, we introduce multiple classification spaces which are very valuable to understand the visualized data, and even mandatory to comprehensively grasp the 3D relationship of different materials present in the volumetric data. Furthermore, we illustrate how multiple classification spaces can be realized using existing graphics hardware. In contrast to previously reported algorithms, our technique is capable of performing all the above mentioned tasks within the graphics pipeline. Therefore, it is very efficient: The three dimensional texture needs to be stored only once and no load is put onto the CPU. Besides using standard OpenGL functionality, we exploit advanced per pixel operations and make use of available OpenGL extensions.

This paper describes tools and techniques for the exploration of gee-scientific data from the oil and gas domain in stereoscopic virtual environments. The two main sources of data in the exploration task are seismic volumes and multivariate well logs of physical properties down a bore hole. We have developed a props-based interaction device called the cubic mouse to allow more direct and intuitive interaction with a cubic seismic volume. This device effectively places the seismic cube in the user's hand. Geologists who have tried this device have been enthusiastic about the ease of use, and were adept only a few moments after picking it up. We have also developed a multi-modal, visualisation and sonification technique for the dense, multivariate well log data. The visualisation can show two well log variables mapped along the well geometry in a bivariate colour scheme, and another variable on a sliding lens. A sonification probe is attached to the lens so that other variables can be heard. The sonification is based on a Geiger-counter metaphor that is widely understood and which makes it easy to explain. The data is sonified at higher or lower resolutions depending on the speed of the lens. Sweeps can be made at slower rates and over smaller intervals to home in on peaks, boundaries or other features in the full resolution data set.

A method for comparing three-dimensional vector fields constructed from simple critical points is described. This method is a natural extension of the previous work [1] which defined a distance metric for comparing two-dimensional fields. The extension to three-dimensions follows the path of our previous work, rethinking the representation of a critical point signature and the distance measure between the points. Since the method relies on topologically based information, problems such as grid matching and vector alignment which often complicate other comparison techniques are avoided. In addition, since only feature information is used to represent, and therefore stored for each field, a significant amount of compression occurs.

We present a novel forward image mapping algorithm, which speeds up perspective warping, as in texture mapping. It processes the source image in a special scanline order instead of the normal raster scanline order. This special scanline has the property of preserving parallelism when projecting to the target image. The algorithm reduces the complexity of perspective-correct image warping by eliminating the division per pixel and replacing it with a division per scanline. The method also corrects the perspective distortion in Gouraud shading with negligible overhead. Furthermore, the special scanline order is suitable for antialiasing using a more accurate antialiasing conic filter, with minimum additional cost. The algorithm is highlighted by incremental calculations and optimized memory bandwidth by reading each source pixel only once, suggesting a potential hardware implementation.

Situational awareness applications require a highly detailed geospatial visualization covering a large geographic area. Conventional polygon based terrain modeling would exceed the capacity of current computer rendering. Terrain visualization techniques for a situational awareness application are described in this case study. Visualizing large amounts of terrain data has been achieved using very large texture maps. Sun shading is applied to the terrain texture map to enhance perception of relief features. Perception of submarine positions has been enhanced using a translucent, textured water surface.

Our ability to accumulate large, complex (multivariate) data sets has far exceeded our ability to effectively process them in searching for patterns, anomalies and other interesting features. Conventional multivariate visualization techniques generally do not scale well with respect to the size of the data set. The focus of this paper is on the interactive visualization of large multivariate data sets based on a number of novel extensions to the parallel coordinates display technique. We develop a multi-resolution view of the data via hierarchical clustering, and use a variation of parallel coordinates to convey aggregation information for the resulting clusters. Users can then navigate the resulting structure until the desired focus region and level of detail is reached, using our suite of navigational and filtering tools. We describe the design and implementation of our hierarchical parallel coordinates system which is based on extending the XmdvTool system. Lastly, we show examples of the tools and techniques applied to large (hundreds of thousands of records) multivariate data sets.

We present a novel presence acceleration for volumetric ray casting. A highly accurate estimation for object presence is obtained by projecting all grid cells associated with the object boundary on the image plane. Memory space and access time are reduced by run-length encoding of the boundary cells, while boundary cell projection time is reduced by exploiting projection templates and multiresolution volumes. Efforts have also been made towards a fast perspective projection as well as interactive classification. We further present task partitioning schemes for effective parallelization of both boundary cell projection and ray traversal procedures. Good load balancing has been reached by taking full advantage of both the optimizations in the serial rendering algorithm and shared-memory architecture. Our experimental results on a 16-processor SGI Power Challenge have shown interactive rendering rates for 256 3 volumetric data sets at 10-30 Hz. We describe the theory and implementation of our algorithm, and shows its superiority over the shear-warp factorization approach.

With the development of magnetic resonance imaging techniques for acquiring diffusion tensor data from biological tissue, visualization of tensor data has become a new research focus. The diffusion tensor describes the directional dependence of water molecules' diffusion and can be represented by a three-by-three symmetric matrix. Visualization of second-order tensor fields is difficult because the data values have many degrees of freedom. Existing visualization techniques are best at portraying the tensor's properties over a two-dimensional field, or over a small subset of locations within a three-dimensional field. A means of visualizing the global structure in measured diffusion tensor data is needed. We propose the use of direct volume rendering, with novel approaches for the tensors' coloring, lighting, and opacity assignment. Hue-balls use a two-dimensional colormap on the unit sphere to illustrate the tensor's action as a linear operator. Lit-tensors provide a lighting model for tensors which includes as special cases both lit-lines (from streamline vector visualization) and standard Phong surface lighting. Together with an opacity assignment based on a novel two-dimensional barycentric space of anisotropy, these methods are shown to produce informative renderings of measured diffusion tensor data from the human brain.

For types of data visualization where the cost of producing images is high, and the relationship between the rendering parameters and the image produced is less than obvious, a visual representation of the exploration process can make the process more efficient and effective. Image graphs represent not only the results but also the process of data visualization. Each node in an image graph consists of an image and the corresponding visualization parameters used to produce it. Each edge in a graph shows the change in rendering parameters between the two nodes it connects. Image graphs are not just static representations; users can interact with a graph to review a previous visualization session or to perform new rendering. Operations which cause changes in rendering parameters can propagate through the graph. The user can take advantage of the information in image graphs to understand how certain parameter changes affect visualization results. Users can also share image graphs to streamline the process of collaborative visualization. We have implemented a volume visualization system using the image graph interface, and the examples presented come from this application.

Irregular tetrahedral meshes, which are popular in many engineering and scientific applications, often contain a large number of vertices. A mesh of V vertices and T tetrahedra requires 48 V bits or less to store the vertex coordinates, 4·T·log 2(V) bits to store the tetrahedra-vertex incidence relations, also called connectivity information, and kV bits to store the k-bit value samples associated with the vertices. Given that T is 5 to 7 times larger than V and that V often exceeds 32 3, the storage space required for the connectivity is larger than 300 V bits and thus dominates the overall storage cost. Our "implants spray" compression approach introduced in the paper reduces this cost to about 30 V bits or less-a 10:1 compression ratio. Furthermore, implant spray supports the progressive refinement of a crude model through a series of vertex-splits operations.

We present a system for interactive explorations of extra- and intracranial blood vessels. Starting with a stack of images from 3D angiography, we use virtual clips to limit the segmentation of the vessel tree to the parts the neuroradiologists are interested in. Furthermore, methods of interactive virtual endoscopy are applied in order to provide an interior view of the blood vessels.