Latency is a key property of video see through AR systems since users' performance is strongly related to it. However, there is no standard way of latency measurement of an AR system in the literature. We have created a stable and comparable way of estimating the latency in a video see through AR system. The latency is estimated by encoding the time in the image and decoding the time after camera feedback. We have encoded the time as a translation of a circle in the image. The cross ratio has been used as an image feature that is preserved in a projective transformation. The encoding allows for a simple but accurate way of decoding. We show that this way of encoding has an adequate accuracy for latency measurements. As the method allows for a series of automatic measurements we propose to visualize the measurements in a histogram. This histogram reveals meaningful information about the system other than the mean value and standard deviation of the latency. The method has been tested on four different AR systems that use different camera technology, resolution and frame rates.

We report on a new single PC based stereoscopic video-see-through AR system which we developed for medical applications. Recent advances in graphics hardware, memory bandwidth, and computing power of standard PCs made it possible that this system outperforms an earlier version which included 3 networked SGI workstations. We designed and implemented a new AR software platform. It is component based and - in conjunction with XML configuration files - provides efficiency, modularity, and extensibility for fast and robust prototyping of AR applications. The system has a compelling real-time performance with 30 frames/second, displaying stereoscopic augmented video views with XGA resolution.

We have built a system for augmented reality visualization based on a single head mounted tracking camera. The camera includes an infrared illuminator and works in conjunction with a set of retro-reflective markers that are placed around the workspace. This marker frame configuration delivers excellent pose information, which translates to stable, jitter-free augmentation. In this article, we describe using the same single camera system for tracking relatively small marker clusters, which can be used for tool or instrument tracking. Tracking of such a marker cluster is more susceptible to noise compared to tracking of a marker frame, mainly due to its small image coverage. The sensitivity to noise is studied using Monte Carlo simulations and verified in an experimental setup. We achieved jitter-free augmentation with an optimized cluster design.

We present a (spiral + circles) scan cone beam reconstruction algorithm in which image reconstruction proceeds via backprojection in the object space. In principle, the algorithm can reconstruct sectional region-of-interest (ROI) in a long object. The approach is a generalization of the cone beam backprojection technique developed by Kudo and Saito in two aspects: the resource-demanding normalization step in the Kudo and Saito's algorithm is eliminated through the technique of data combination that we published earlier, and the elimination of the restriction that the detector be big enough to capture the entire cone beam projection of the ROI. Restricting the projection data to the appropriate angular range required by data combination can be accomplished by a masking process. Because of the simplification resulting from the elimination of the normalization step, the most time-consuming operations of the algorithm can be approximated by the efficient step of line-by-line ramp filtering the cone beam image in the direction of the scan path, plus a correction image. The correction image, which can be computed exactly, is needed because data combination is not properly matched at the mask boundary when ramp filtering is involved. Empirical two-dimensional (2-D) point spread function (PSF) is developed to improve matching with the correction image which is computed with finite samplings. The use of transition region to further improve matching is introduced. The results of testing the algorithm on simulated phantoms are presented.