We conducted an experiment in an attempt to generate baseline accuracy and precision values for optical see-through (OST) head mounted display (HMD) calibration without the inclusion of human postural sway error. This preliminary work will act as a control condition for future studies into postural error reduction. An experimental apparatus was constructed to allow performance of a SPAAM calibration using 25 alignments taken using one of three distance distribution patterns: static, sequential, and magic square. The accuracy of the calibrations were determined by calculating the extrinsic X, Y, Z translation values from the resulting projection matrix. The standard deviation for each translation component was also calculated. The results show that the magic square distribution resulted in the most accurate parameter estimation and also resulted in the smallest standard deviation for each extrinsic translation component.

The parameter estimation variance of the Single Point Active Alignment Method (SPAAM) is studied through an experiment where 11 subjects are instructed to create alignments using an Optical See-Through Head Mounted Display (OSTHMD) such that three separate correspondence point distributions are acquired. Modeling the OSTHMD and the subject's dominant eye as a pinhole camera, findings show that a correspondence point distribution well distributed along the user's line of sight yields less variant parameter estimates. The estimated eye point location is studied in particular detail. The findings of the experiment are complemented with simulated data which show that image plane orientation is sensitive to the number of correspondence points. The simulated data also illustrates some interesting properties on the numerical stability of the calibration problem as a function of alignment noise, number of correspondence points, and correspondence point distribution.

While label placement algorithms are generally successful in managing visual clutter by preventing label overlap, they can also cause significant label movement in dynamic displays. This study investigates motion detection thresholds for various types of label movement in realistic and complex virtual environments, which can be helpful for designing less salient and disturbing algorithms. Our results show that label movement in stereoscopic depth is shown to be less noticeable than similar lateral monoscopic movement, inherent to 2D label placement algorithms. Furthermore, label movement can be introduced more readily into the visual periphery (over 15° eccentricity) because of reduced sensitivity in this region. Moreover, under the realistic viewing conditions that we used, motion of isolated labels is more easily detected than that of overlapping labels. This perhaps counterintuitive finding may be explained by visual masking due to the visual clutter arising from the label overlap. The quantitative description of the findings presented in this paper should be useful not only for label placement applications, but also for any cluttered AR or VR application in which designers wish to control the users' visual attention, either making text labels more or less noticeable as needed.

This paper describes a novel technique for segregating overlapping labels in stereoscopic see-through displays. The present study investigates the labeling of far-field objects, with distances ranging 100-120 m. At these distances the stereoscopic disparity difference between objects is below 1 arcmin, so labels rendered at the same distance as their corresponding objects appear as if on a flat layer in the display. This flattening is due to limitations of both display and human visual resolution. By remapping labels to pre-determined depth layers on the optical path between the observer and the labeled object, an interlayer disparity ranging from 5 to 20 arcmin can be achieved for 5 overlapping labels. The present study evaluates the impact of such depth separation of superimposed layers, and found that a 5 arcmin interlayer disparity yields a significantly lower response time, over 20% on average, in a visual search task compared to correctly registering labels and objects in depth. Notably the performance does not improve when doubling the interlayer disparity to 10 arcmin and, surprisingly, the performance degrades significantly when again doubling the interlayer disparity to 20 arcmin, approximating the performance in situations with no interlayer disparity. These results confirm that our technique can be used to segregate overlapping labels in the far visual field, without the cost associated with traditional label placement algorithms.

The precision with which users can maintain boresight alignment between visual targets at different depths is recorded for 24 subjects using two different boresight targets. Subjects' normal head stability is established using their Romberg coefficients. Weibull distributions are used to describe the probabilities of the magnitude of head positional errors and the three dimensional cloud of errors is displayed by orthogonal two dimensional density plots. These data will lead to an understanding of the limits of user introduced calibration error in augmented reality systems.

We present a new technique for managing visual clutter caused by overlapping labels in complex information displays. This technique, "label layering", utilizes stereoscopic disparity as a means to segregate labels in depth for increased legibility and clarity. By distributing overlapping labels in depth, we have found that selection time during a visual search task in situations with high levels of overlap is reduced by four seconds or 24%. Our data show that the depth order of the labels must be correlated with the distance order of their corresponding objects. Since a random distribution of stereoscopic disparity in contrast impairs performance, the benefit is not solely due to the disparity-based image segregation. An algorithm using our label layering technique accordingly could be an alternative to traditional label placement algorithms that avoid label overlap at the cost of distracting motion, symbology dimming or label size reduction.

The postural sway in 24 subjects performing a boresight calibration task on a large format head-up display is studied to estimate the impact of human limits on boresight calibration precision and ultimately on static registration errors. The dependent variables, accumulated sway path and omni-directional standard deviation, are analyzed for the calibration exercise and compared against control cases where subjects are quietly standing with eyes open and eyes closed. Findings show that postural stability significantly deteriorates during boresight calibration compared to when the subject is not occupied with a visual task. Analysis over time shows that the calibration error can be reduced by 39% if calibration measurements are recorded in a three second interval at approximately 15 seconds into the calibration session as opposed to an initial reading. Furthermore parameter optimization on experiment data suggests a Weibull distribution as a possible error description and estimation for omni-directional calibration precision. This paper extends previously published preliminary analyses and the conclusions are verified with experiment data that has been corrected for subject inverted pendulum compensatory head rotation by providing a better estimate of the position of the eye. With correction the statistical findings are reinforced.

Recent work has shown that overlapping labels in far-field AR environments can be successfully segregated by remapping them to predefined stereoscopic depth layers. User performance was found to be optimal when setting the interlayer disparity to 5--10 arcmin. The current paper investigates to what extent this label segregation technique, label layering, is affected by important perceptual defects in AR such as registration errors and mismatches in accommodation, visual resolution and contrast. A virtual environment matched to a corresponding AR condition but lacking these problems showed a reduction in average response time by 10%. However, the performance pattern for different label layering parameters was not significantly different in the AR and VR environments, showing robustness of this label segregation technique against such perceptual issues.