We consider the problem of calibrating a highly generic imaging model, that consists of a non-parametric association of a projection ray in 3D to every pixel in an image. Previous calibration approaches for this model do not seem to be directly applicable for cameras with large fields of view and non-central cameras. In this paper, we describe a complete calibration approach that should in principle be able to handle any camera that can be described by the generic imaging model. Initial calibration is performed using multiple-images of overlapping calibration grids simultaneously. This is then improved using pose estimation and bundle adjustment-type algorithms. The approach has been applied on a wide variety of central and non-central cameras including fisheye lens, catadioptric cameras with spherical and hyperbolic mirrors, and multi-camera setups. We also consider the question if non-central models are more appropriate for certain cameras than central models.

Location-aware mobile users need to access, query, and visualize, geographic information in a wide variety of applications including tourism, navigation, environmental management, and emergency response. Often they also need to communicate information back and forth with other mobile users or with a central agency. Depending upon the task, the user may have to share or switch between different databases or different views of the same database. We first describe our efforts to create a consistent visualization of different spatial geographic databases consisting of aerial imagery, AutoCAD drawings, schematic diagrams, and street maps. The location of a mobile user is obtained via GPS (global positioning system) and visualized synchronously across different visualizations. Our focus then is to support the mobile user in visually accessing and querying the spatial GIS databases. The mobile or the central user can interact with the database in real-time and communicate the information to each other over wireless networks. The system supports a variety of queries and tasks ("how far", "where", "closest" etc.) for many different types of geometric primitives (points, lines, polygons etc.) and objects (buildings, metro stops etc.). The output results can then be unobtrusively and consistently displayed to the user across multiple views of the scene.

We present a hierarchical top-down refinement algorithm for compressing 2D vector fields that preserves topology. Our approach is to reconstruct the data set using adaptive refinement that considers topology. The algorithms start with little data and subdivide regions that are most likely to reconstruct the original topology of the given data set. We use two different refinement techniques. The first technique uses bintree subdivision and linear interpolation. The second algorithm is driven by triangular quadtree subdivision with Coons patch quadratic interpolation. We employ local error metrics to measure the quality of compression and as a global metric we compute Earth Mover's Distance (EMD) to measure the deviation from the original topology. Experiments with both analytic and simulated data sets are presented. Results indicate that one can obtain significant compression with low errors without losing topological information. Advantages and disadvantages of different topology preserving compression algorithms are also discussed in the paper.

We present an algorithm for compressing 2D vector fields that preserves topology. Our approach is to simplify the given data set using constrained clustering. We employ different types of global and local error metrics including the earth mover's distance metric to measure the degradation in topology as well as weighted magnitude and angular errors. As a result, we obtain precise error bounds in the compressed vector fields. Experiments with both analytic and simulated data sets are presented. Results indicate that one can obtain significant compression with low errors without losing topology information.

Uncertainty or errors are introduced in fluid flow data as the data is acquired, transformed and rendered. Although researchers are aware of these uncertainties, little has been done to incorporate them in the existing visualization systems for fluid flow. In the absence of integrated presentation of data and its associated uncertainty, the analysis of the visualization is incomplete at best and may lead to inaccurate or incorrect conclusions. The article presents UFLOW-a system for visualizing uncertainty in fluid flow. Although there are several sources of uncertainties in fluid flow data, in this work, we focus on uncertainty arising from the use of different numerical algorithms for computing particle traces in a fluid flow. The techniques that we have employed to visualize uncertainty in fluid flow include uncertainty glyphs, flow envelopes, animations, priority sequences, twirling batons of trace viewpoints, and rakes. These techniques are effective in making the users aware of the effects of different integration methods and their sensitivity, especially near critical points in the flow field.

Integrated presentation of data with uncertainty is a worthy goal in scientific visualization. It allows researchers to make informed decisions based on imperfect data. It also allows users to visually compare and contrast different algorithms for performing the same task or different models for representing the same physical phenomenon. We present LISTEN-a data sonification system that has been incorporated into two visualization systems: a system for visualizing geometric uncertainty of surface interpolants; and a system for visualizing uncertainty in fluid flow. LISTEN is written in C++ for the SGI platform. It works with the SGI internal audio chip or a MIDI device or both. LISTEN is an object-oriented system that is modular, flexible, adaptable, portable, interactive and extensible. We demonstrate that sonification is very effective as an additional tool in visualizing geometric and fluid flow uncertainty.

Environmental data have inherent uncertainty which is often ignored in visualization. Meteorological stations and doppler radars, including their time series averages, have a wealth of uncertainty information that traditional vector visualization methods such as meteorological wind barbs and arrow glyphs simply ignore. We have developed a new vector glyph to visualize uncertainty in winds and ocean currents. Our approach is to include uncertainty in direction and magnitude, as well as the mean direction and length, in vector glyph plots. Our glyph shows the variation in uncertainty, and provides fair comparisons of data from instruments, models, and time averages of varying certainty. We also define visualizations that incorporate uncertainty in an unambiguous manner as verity visualization. We use both quantitative and qualitative methods to compare our glyphs to traditional ones. Subjective comparison tests with experts are provided, as well as objective tests, where the information density of our new glyphs and traditional glyphs are compared. The design of the glyph and numerous examples using environmental data are given. We show enhanced visualizations, data together with their uncertainty information, that may improve understanding of environmental vector field data quality.

An integrated computerized database and radiographic image analysis system for total knee and hip replacement is currently under development at the University of Texas Medical School. Measurements are made rapidly and accurately for a wide variety of prostheses. The results are automatically stored in a database for statistical comparisons. The system will prove valuable in postoperative care. However, the most significant contribution of the proposed system would be to compare the measured parameters from the radiographs of the patients with the predicted outcome based on the stress analysis of the preoperative configuration with the designed prosthesis in place. Such a study would provide necessary feedback to justify or modify the stress analysis models, which are utilized in designing the prostheses.>