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ABSTRACTThe two major aspects of camera misalignment that cause visual discomfort when viewing images on a 3D display are vertical and torsional disparities. While vertical disparities are uniform throughout the image, torsional rotations introduce a range of disparities that depend on the location in the image. The goal of this study was to determine the discomfort ranges for the kinds of natural image that people are likely to take with 3D cameras rather than the artificial line and dot stimuli typically used for laboratory studies. We therefore assessed visual discomfort on a five-point scale from 'none' to 'severe' for artificial misalignment disparities applied to a set of full-resolution images of indoor scenes.For viewing times of 2 s, discomfort ratings for vertical disparity in both 2D and 3D images rose rapidly toward the discomfort level of 4 ('severe') by about 60 arcmin of vertical disparity. Discomfort ratings for torsional disparity in the same image rose only gradually, reaching only the discomfort level of 3 ('strong') by about 50 deg of torsional disparity. These data were modeled with a second-order hyperbolic compression function incorporating a term for the basic discomfort of the 3D display in the absence of any misalignments through a Minkowski norm. These fits showed that, at a criterion discomfort level of 2 ('moderate'), acceptable levels of vertical disparity were about 15 arcmin. The corresponding values for the torsional disparity were about 30 deg of relative orientation.
Ad-hoc networks of simple, omni-directional sensors present an attractive solution to low-cost, easily deployable, fault tolerant target tracking systems. In this paper, we present a tracking algorithm that relies on a real time observation of the target power, received by multiple sensors. We remove target position dependency on the emitted target power by taking ratios of the power observed by different sensors, and apply the natural logarithm to effectively transform to another coordinate system. Further, we derive noise statistics in the transformed space and demonstrate that the observation in the new coordinates is linear in the presence of additive Gaussian noise. We also show how a typical dynamic model in Cartesian coordinates can be adapted to the new coordinate system. As a consequence, the problem of tracking target position with omni-directional sensors can be adapted to the conventional Kalman filter framework. We validate the proposed methodology through simulations under different noise, target movement, and sensor density conditions.
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