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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.

Uncertainty represented in visualizations is often ignored or misunderstood by the non-expert user. The NationalHurricane Center displays hurricane forecasts using a track forecast cone, depicting the expected track of the storm and the uncertaintyin the forecast. Our goal was to test whether different graphical displays of a hurricane forecast containing uncertaintywould influence a decision about storm characteristics.Participants viewed one of five different visualization types. Three varied the currently used forecast cone, one presenteda track with no uncertainty, and one presented an ensemble of multiple possible hurricane tracks. Results show that individualsmake different decisions using uncertainty visualizations with different visual properties, demonstrating that basic visualproperties must be considered in visualization design and communication.

Three experiments examined perceived absolute distance in a head-mounted display virtual environment (HMD-VE) and a matched real-world environment, as a function of the type and orientation of the distance viewed. In Experiment 1, participants turned and walked, without vision, a distance to match the viewed interval for both egocentric (viewer-to-target) and exocentric (target-to-target) extents. Egocentric distances were underestimated in the HMD-VE while exocentric distances were estimated similarly across environments. Since egocentric distances were displayed in the depth plane and exocentric distances in the frontal plane, the pattern of results could have been related to the orientation of the distance or to the type of distance. Experiments 2 and 3 tested these alternatives. Participants estimated exocentric distances presented along the depth or frontal plane either by turning and walking (Experiment 2) or by turning and throwing a beanbag to indicate the perceived extent (Experiment 3). For both Experiments 2 and 3, depth intervals were underestimated in the HMD-VE compared to the real world. However, frontal intervals were estimated similarly across environments. The findings suggest anisotropy in HMD-VE distance perception such that distance underestimation in the HMD-VE generalizes to intervals in the depth plane, but not to intervals in the frontal plane. (Contains 1 table and 6 figures.)

A series of experiments examined the role of the motor system in imagined movement, finding a strong relationship between imagined walking performance and the biomechanical information available during actual walking. Experiments 1 through 4 established the finding that real and imagined locomotion differ in absolute walking time. We then tested whether executed actions could provide a basis for imagined walking rate using 2 approaches. Experiments 5 and 6 used a perceptual-motor recalibration paradigm, finding that after physically walking in a treadmill virtual reality environment, actors recalibrated the time to imagine walking to a previously viewed target. This finding mirrors previous perceptual-motor recalibration work measuring actual walking to previously viewed targets. Experiments 7 and 8 used a dual-task paradigm in which actions performed concurrently with imagined walking increased the similarity between real and imagined walking time, but only when they were biomechanically consistent with the act of walking. The striking influence of biomechanical information on imagined locomotion provides evidence for shared motor systems in imagined and executed movements and is also directly relevant to the mechanisms involved in egocentric spatial updating of environmental layout. (Contains 3 figures and 1 table.)

In 4 experiments, the authors varied the extent and nature of participant movement in a virtual environment to examine the influence of action on estimates of geographical slant. Previous studies showed that people consciously overestimate hill slant but can still accurately guide an action toward the hill (D. R. Proffitt, M. Bhalla, R. Gossweiler, & J. Midgett, 1995). Related studies suggest that one's potential to act may influence perception of slant and that distinct representations may independently inform perceptual and motoric responses. The authors found that in all conditions, perceptual judgments were overestimated and motoric adjustments were more accurate. The virtual environment allowed manipulation of the effort required to walk up simulated hills. Walking with the effort appropriate to the visual slant led to increased perceptual overestimation of slant compared with active walking with the effort appropriate to level ground, while visually guided actions remained accurate.

Most people with low vision rely on their remaining functional vision for mobility. Our goal is to provide tools to help design architectural spaces in which safe and effective mobility is possible by those with low vision – spaces that we refer to as visually accessible. We describe an approach that starts with a 3D CAD model of a planned space and produces labeled images indicating whether or not structures that are potential mobility hazards are visible at a particular level of low vision. There are two main parts to the analysis. The first, previously described, represents low-vision status by filtering a calibrated luminance image generated from the CAD model and associated lighting and materials information to produce a new image with unseen detail removed. The second part, described in this paper, uses both these filtered images and information about the geometry of the space obtained from the CAD model and related lighting and surface material specifications to produce a quantitative estimate of the likelihood of particular hazards being visible. We provide examples of the workflow required, a discussion of the novelty and implications of the approach, and a short discussion of needed future work. [ABSTRACT FROM AUTHOR]

A robot navigating in an unstructured outdoor environment must determine its own location in spite of problems due to environmental conditions, sensor limitations and map inaccuracies. Exact measurements are seldom known, and the combination of approximate measures can lead to large errors in self-localization. The conventional approach to this problem has been to deal with the errors either during processing or after they occur. We maintain that it is possible to limit the errors before they occur. We analyze how measurement errors affect errors in localization and propose that a simple algorithm can be used to exploit the geometric properties of landmarks in the environment in order to decrease errors in localization. Our goal is to choose landmarks that will provide the best localization regardless of measurement error, determine the best areas in which to identify new landmarks to be used for further localization and choose paths that will provide the least chance of 'straying.' We show the result of implementing this concept in experiments run in simulation with USGS 30 m DEM data for a robot statically locating, following a path and identifying new landmarks.

We describe a method for visually detecting moving objects from a moving camera using point correspondences in two orthographic views. The method applies a simple structure-from-motion analysis and then identifies those points inconsistent with the interpretation of the scene as a single rigid object. It is effective even when the actual motion parameters cannot be recovered. Demonstrations are presented using point correspondences automatically determined from real image sequences.