We develop an approach to using microsaccade dynamics for the measurement of task difficulty/cognitive load imposed by a visual search task of a layered surface. Previous studies provide converging evidence that task difficulty/cognitive load can influence microsaccade activity. We corroborate this notion. Specifically, we explore this relationship during visual search for features embedded in a terrain-like surface, with the eyes allowed to move freely during the task. We make two relevant contributions. First, we validate an approach to distinguishing between the ambient and focal phases of visual search. We show that this spectrum of visual behavior can be quantified by a single previously reported estimator, known as Krejtz's K coefficient. Second, we use ambient/focal segments based on K as a moderating factor for microsaccade analysis in response to task difficulty. We find that during the focal phase of visual search (a) microsaccade magnitude increases significantly, and (b) microsaccade rate decreases significantly, with increased task difficulty. We conclude that the combined use of K and microsaccade analysis may be helpful in building effective tools that provide an indication of the level of cognitive activity within a task while the task is being performed.

A common approach to sampling the space of a prediction is the generation of an ensemble of potential outcomes, where the ensemble's distribution reveals the statistical structure of the prediction space. For example, the US National Hurricane Center generates multiple day predictions for a storm's path, size, and wind speed, and then uses a Monte Carlo approach to sample this prediction into a large ensemble of potential storm outcomes. Various forms of summary visualizations are generated from such an ensemble, often using spatial spread to indicate its statistical characteristics. However, studies have shown that changes in the size of such summary glyphs, representing changes in the uncertainty of the prediction, are frequently confounded with other attributes of the phenomenon, such as its size or strength. In addition, simulation ensembles typically encode multivariate information, which can be difficult or confusing to include in a summary display. This problem can be overcome by directly displaying the ensemble as a set of annotated trajectories, however this solution will not be effective if ensembles are densely overdrawn or structurally disorganized. We propose to overcome these difficulties by selectively sampling the original ensemble, constructing a smaller representative and spatially well organized ensemble. This can be drawn directly as a set of paths that implicitly reveals the underlying spatial uncertainty distribution of the prediction. Since this approach does not use a visual channel to encode uncertainty, additional information can more easily be encoded in the display without leading to visual confusion. To demonstrate our argument, we describe the development of a visualization for ensembles of tropical cyclone forecast tracks, explaining how their spatial and temporal predictions, as well as other crucial storm characteristics such as size and intensity, can be clearly revealed. We verify the effectiveness of this visualization approach through a cognitive study exploring how storm damage estimates are affected by the density of tracks drawn, and by the presence or absence of annotating information on storm size and intensity.

Data ensembles are often used to infer statistics to be used for a summary display of an uncertain prediction. In a spatial context, these summary displays have the drawback that when uncertainty is encoded via a spatial spread, display glyph area increases in size with prediction uncertainty. This increase can be easily confounded with an increase in the size, strength or other attribute of the phenomenon being presented. We argue that by directly displaying a carefully chosen subset of a prediction ensemble, so that uncertainty is conveyed implicitly, such misinterpretations can be avoided. Since such a display does not require uncertainty annotation, an information channel remains available for encoding additional information about the prediction. We demonstrate these points in the context of hurricane prediction visualizations, showing how we avoid occlusion of selected ensemble elements while preserving the spatial statistics of the original ensemble, and how an explicit encoding of uncertainty can also be constructed from such a selection. We conclude with the results of a cognitive experiment demonstrating that the approach can be used to construct storm prediction displays that significantly reduce the confounding of uncertainty with storm size, and thus improve viewers' ability to estimate potential for storm damage.

Abstract The U.S. National Hurricane Center (NHC) issues advisories every six hours during the life of a hurricane. These advisories describe the current state of the storm, and its predicted path, size, and wind speed over the next five days. However, from these data alone, the question “What is the likelihood that the storm will hit Houston with hurricane strength winds between 12:00 and 14:00 on Saturday?” cannot be directly answered. To address this issue, the NHC has recently begun making an ensemble of potential storm paths available as part of each storm advisory. Since each path is parameterized by time, predicted values such as wind speed associated with the path can be inferred for a specific time period by analyzing the statistics of the ensemble. This paper proposes an approach for generating smooth scalar fields from such a predicted storm path ensemble, allowing the user to examine the predicted state of the storm at any chosen time. As a demonstration task, we show how our approach can be used to support a visualization tool, allowing the user to display predicted storm position – including its uncertainty – at any time in the forecast. In our approach, we estimate the likelihood of hurricane risk for a fixed time at any geospatial location by interpolatingsimplicial depthvalues in the path ensemble. Adaptivelysizedradial basis functionsare used to carry out the interpolation. Finally, geometric fitting is used to produce a simple graphical visualization of this likelihood. We also employ a non‐linear filter, in time, to assure frame‐to‐frame coherency in the visualization as the prediction time is advanced. We explain the underlying algorithm and definitions, and give a number of examples of how our algorithm performs for several different storm predictions, and for two different sources of predicted path ensembles.

We report on an experiment testing gaze-contingent depth-of-field (DOF) for the reduction of visual discomfort when viewing stereoscopic displays. Subjective results are compelling, showing, we believe for the first time, that gaze-contingent depth-of-field significantly reduces visual discomfort. When individual stereoacuity is taken into account, objective measurements of gaze vergence corroborate previous reports, showing significant bias toward the zero depth plane, where error is smallest. As with earlier similar attempts, participants expressed a dislike toward gaze-contingent DOF. Although not statistically significant, this dislike is likely attributed to the eye tracker's spatial inaccuracy as well as the DOF simulation's noticeable temporal lag.

We show that the error in 3D gaze depth (vergence) estimated from binocularly-tracked gaze disparity is related to the viewing distance of the screen calibration plane at which 2D gaze is recorded. In a stereoscopic (virtual) environment, this relationship is evident in gaze to target depth error: vergence error behind the screen is greater than in front of the screen and is lowest at the screen depth. In a physical environment, with no accommodation-vergence conflict, the magnitude of vergence error in front of the 2D calibration plane appears reversed, increasing with distance from the viewer.

This article summarizes our previous work on developing an online system to allow the estimation of 3D gaze depth using eye tracking in a stereoscopic environment. We report on recent extensions allowing us to report the full 3D gaze position. Our system employs a 3D calibration process that determines the parameters of a mapping from a naive depth estimate, based simply on triangulation, to a refined 3D gaze point estimate tuned to a particular user. We show that our system is an improvement on the geometry-based 3D gaze estimation returned by a proprietary algorithm provided with our tracker. We also compare our approach with that of the Parameterized Self-Organizing Map (PSOM) method, due to Essig and colleagues, which also individually calibrates to each user. We argue that our method is superior in speed and ease of calibration, is easier to implement, and does not require an iterative solver to produce a gaze position, thus guaranteeing computation at the rate of tracker acquisition. In addition, we report on a user study that indicates that, compared with PSOM, our method more accurately estimates gaze depth, and is nearly as accurate in estimating horizontal and vertical position. Results are verified on two different 4D eye tracking systems, a high accuracy Wheatstone haploscope and a medium accuracy active stereo display. Thus, it is the recommended method for applications that primarily require gaze depth information, while its ease of use makes it suitable for many applications requiring full 3D gaze position.

A remote eye tracker is used to explore its utility for ocular vergence measurement. Subsequently, vergence measurements are compared in response to anaglyphic stereographic stimuli as well as in response to monoscopic stimulus presentation on a standard display. Results indicate a highly significant effect of anaglyphic stereoscopic display on ocular vergence when viewing a stereoscopic calibration video. Significant convergence measurements were obtained for stimuli fused in the anterior image plane.

The coloring of 3D models using 2D or 3D texture mapping has well-known intrinsic problems, such as mapping discontinuities and limitations to model editing after coloring. Workarounds for these problems often require adopting very complex approaches. Here we propose a new technique, called mesh colors, for associating color data directly with a polygonal mesh. The approach eliminates problems deriving from using a map from texture space to model space. Mesh colors is an extension of vertex colors where, in addition to keeping color values on each vertex, they are also kept on edges and faces. Like texture mapping, the approach allows higher texture resolution than model resolution, but at the same time it guarantees one-to-one correspondence between the model surface and the color data, and eliminates discontinuities. We show that mesh colors integrate well with the current graphics pipeline and can be used to generate very high-quality textures.

We provide an overview of the Master of Science in Visualization Sciences program offered at Texas A&M University. Central to this program is the intermixing of concepts, students, and faculty from art, computer science, architecture, and engineering. We explain the structure of the curriculum, and then focus on a detailed analysis of foundation courses that have been designed to make it possible for students from such diverse backgrounds to share the same advanced coursework. Illustrations are drawn from a number of examples of student project work in these foundation courses. We present practical guidelines for those seeking to implement similar courses in their programs.

We introduce a novel mechanism for creating line drawings from three-dimensional models, which captures the dynamic nature of the drawing process. The approach takes into account the interaction between the moving human hand and the drawing instrument. This is demonstrated as applied to the specific problem of making silhouette drawings from polygonal models. A control system drives a pen by tracking the contour of the polygonal model as projected onto the drawing surface, thus mimicking hand motion. The pen is treated as a physically-based object with momentum, giving the generated lines a smooth hand-drawn quality. Lines are rendered using a ribbon metaphor, where thickness is determined by the twist of the ribbon. The twist angle can be dependent upon various attributes such as perspective depth, the curvature of the line, and the lighting of the model. A number of examples are presented, ranging from tightly controlled drawings to expressive gestural drawings.

We present a new method for the real-time simulation of fluid surface waves and their interactions with floating objects. The method is based on the new concept of wave particles, which offers a simple, fast, and unconditionally stable approach to wave simulation. We show how graphics hardware can be used to convert wave particles to a height field surface, which is warped horizontally to account for local wave-induced flow. The method is appropriate for most fluid simulation situations that do not involve significant global flow. It is demonstrated to work well in constrained areas, including wave reflections off of boundaries, and in unconstrained areas, such as an ocean surface. Interactions with floating objects are easily integrated by including wave forces on the objects and wave generation due to object motion. Theoretical foundations and implementation details are provided, and experiments demonstrate that we achieve plausible realism. Timing studies show that the method is scalable to allow simulation of wave interaction with several hundreds of objects at real-time rates.

We present the results of two controlled studies comparing layered surface visualizations under various texture conditions. The task was to estimate surface normals, measured by accuracy of a hand-set surface normal probe. A single surface visualization was compared with the two-surfaces case under conditions of no texture and with projected grid textures. Variations in relative texture spacing on top and bottom surfaces were compared, as well as opacity of the top surface. Significant improvements are found for the textured cases over non-textured surfaces. Either larger or thinner top-surface textures, and lower top surface opacities are shown to give less bottom surface error. Top surface error appears to be highly resilient to changes in texture. Given the results we also present an example of how appropriate textures might be useful in volume visualization.

This paper is a contribution to the literature on perceptually optimal visualizations of layered three-dimensional surfaces. Specifically, we develop guidelines for generating texture patterns, which, when tiled on two overlapped surfaces, minimize confusion in depth-discrimination and maximize the ability to localize distinct features. We design a parameterized texture space and explore this texture space using a "human in the loop" experimental approach. Subjects are asked to rate their ability to identify Gaussian bumps on both upper and lower surfaces of noisy terrain fields. Their ratings direct a genetic algorithm, which selectively searches the texture parameter space to find fruitful areas. Data collected from these experiments are analyzed to determine what combinations of parameters work well and to develop texture generation guidelines. Data analysis methods include ANOVA, linear discriminant analysis, decision trees, and parallel coordinates. To confirm the guidelines, we conduct a post-analysis experiment, where subjects rate textures following our guidelines against textures violating the guidelines. Across all subjects, textures following the guidelines consistently produce high rated textures on an absolute scale, and are rated higher than those that did not follow the guidelines.

This paper proposes a new experimental framework within which evidence regarding the perceptual characteristics of a visualization method can be collected, and describes how this evidence can be explored to discover principles and insights to guide the design of perceptually near-optimal visualizations. We make the case that each of the current approaches for evaluating visualizations is limited in what it can tell us about optimal tuning and visual design. We go on to argue that our new approach is better suited to optimizing the kinds of complex visual displays that are commonly created in visualization. Our method uses human-in-the-loop experiments to selectively search through the parameter space of a visualization method, generating large databases of rated visualization solutions. Data mining is then used to extract results from the database, ranging from highly specific exemplar visualizations for a particular data set, to more broadly applicable guidelines for visualization design. We illustrate our approach using a recent study of optimal texturing for layered surfaces viewed in stereo and in motion. We show that a genetic algorithm is a valuable way of guiding the human-in-the-loop search through visualization parameter space. We also demonstrate several useful data mining methods including clustering, principal component analysis, neural networks, and statistical comparisons of functions of parameters.

The problem of perceptually optimizing complex visualizations is a difficult one, involving perceptual as well as aesthetic issues. In our experience, controlled experiments are quite limited in their ability to uncover interrelationships among visualization parameters, and thus may not be the most useful way to develop rules-of-thumb or theory to guide the production of high-quality visualizations. In this paper, we propose a new experimental approach to optimizing visualization quality that integrates some of the strong points of controlled experiments with methods more suited to investigating complex highly-coupled phenomena. We use human-in-the-loop experiments to search through visualization parameter space, generating large databases of rated visualization solutions. This is followed by data mining to extract results such as exemplar visualizations, guidelines for producing visualizations, and hypotheses about strategies leading to strong visualizations. The approach can easily address both perceptual and aesthetic concerns, and can handle complex parameter interactions. We suggest a genetic algorithm as a valuable way of guiding the human-in-the-loop search through visualization parameter space. We describe our methods for using clustering, histogramming, principal component analysis, and neural networks for data mining. The experimental approach is illustrated with a study of the problem of optimal texturing for viewing layered surfaces so that both surfaces are maximally observable.

A common problem in visualization applications is the display of one surface overlying another. Unfortunately, it is extremely difficult to do this clearly and effectively. Stereoscopic viewing can help, but in order for us to be able to see both surfaces simultaneously, they must be textured, and the top surface must be made partially transparent. There is also abundant evidence that all textures are not equal in helping to reveal surface shape, but there are no general guidelines describing the best set of textures to be used in this way. What makes the problem difficult to perceptually optimize is that there are a great many variables involved. Both foreground and background textures must be specified in terms of their component colors, texture element shapes, distributions, and sizes. Also to be specified is the degree of transparency for the foreground texture components. Here we report on a novel approach to creating perceptually optimal solutions to complex visualization problems and we apply it to the overlapping surface problem as a test case. Our approach is a three-stage process. In the first stage we create a parameterized method for specifying a foreground and background pair of textures. In the second stage a genetic algorithm is applied to a population of texture pairs using subject judgments as a selection criterion. Over many trials effective texture pairs evolve. The third stage involves characterizing and generalizing the examples of effective textures. We detail this process and present some early results.

A current trend in computer animation research is the use of motion capture techniques to generate libraries of basic motions. This paper presents an approach to the storage and reuse of motion capture data that integrates an adaptive filter for noise reduction with a functional representation that is highly efficient. Each motion sequence is represented as a set of non-uniform B-spline curves attached to a hierarchical model. Based on this representation a set of operations is developed that, when applied to these curves, facilitates the reuse and manipulation of pre-recorded motion. These operations include the generation of smooth transitions between motion clips, the addition of exaggerated movements to motion data, motion interpolation and motion warping.

We propose a new technique for gesture recognition that involves both physical and control models of gesture performance, and describe preliminary experiments done to validate the approach. The technique incorporates underlying dynamics and control models are used to augment a set of Kalman-filter-based recognizer modules so that each filters the input data under the a priori assumption that one of the gestures is being performed. The recognized gesture is the filter output that most closely matches the output of an unaugmented Kalman filter. In our preliminary experiments, we treated gestures made with simple motions of the right arm, done while tracking only hand position. We modeled the path that the hand traverses while performing a gesture as a point-mass moving through air. The control model for each specific gesture was simply an experimentally determined sequence of applied forces plus a proportional control based on spatial position. Our experiments showed that even using such a simple set of models we were able to obtain results reasonably comparable with a carefully hand-constructed feature-based discriminator on a limited set of spatially-distinct planar gestures.

The ability to express emotions is important for creating believable interactive characters. To simulate emotional expressions in an interactive environment, an intelligent agent needs both an adaptive model for generating believable responses, and a visualization model for mapping emotions into facial expressions. Recent advances in intelligent agents and in facial modeling have produced effective algorithms for these tasks independently. We describe a method for integrating these algorithms to create an interactive simulation of an agent that produces appropriate facial expressions in a dynamic environment. Our approach to combining a model of emotions with a facial model represents a first step towards developing the technology of a truly believable interactive agent which has a wide range of applications from designing intelligent training systems to video games and animation tools.

Area cartograms are used for visualizing geographically distributed data by attaching measurements to regions of a map and scaling the regions such that their areas are proportional to the measured quantities. A continuous area cartogram is a cartogram that is constructed without changing the underlying map topology. We present a new algorithm for the construction of continuous area cartograms that was developed by viewing their construction as a constrained optimization problem. The algorithm uses a relaxation method that exploits hierarchical resolution, constrained dynamics, and a scheme that alternates goals of achieving correct region areas and adjusting region shapes. It is compared favorably to existing methods in its ability to preserve region shape recognition cues, while still achieving high accuracy.

This paper describes a course in computer graphics which is designed for students with little or no background in computer science. The course is centered around custom software which was designed with the pedagogical goals in mind. The goals of the course, the software, and some experiences at two institutions are discussed.

We demonstrate a physically-based technique for predicting the drape of a wide variety of woven fabrics. The approach exploits a theoretical model that explicitly represents the microstructure of woven cloth with interacting particles, rather than utilizing a continuum approximation. By testing a cloth sample in a Kawabata fabric testing device, we obtain data that is used to tune the model's energy functions, so that it reproduces the draping behavior of the original material. Photographs, comparing the drape of actual cloth with visualizations of simulation results, show that we are able to reliably model the unique large-scale draping characteristics of distinctly different fabric types.