Many types of tangible interaction systems, such as interactive surfaces and gesture-based interfaces, are based on various kinds of optical tracking, using infrared illuminators and cameras. One drawback of these setups is that they suffer from problems common to optical trackers, such as sensitivity to stray environment light from artificial and natural sources. In this paper, we present a method to significantly enhance tracking robustness for those systems which employ active illumination. Through addition of a small electronic circuit which modulates the LEDs used to illuminate the scene, contrast can be significantly increased.

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.

In our poster presentation at ISMAR '04, we proposed the idea of an AR training solution including capture and 3D replays of subtle movements. The crucial part missing for realizing such a training system was an appropriate way of synchronizing trajectories of similar movements with varying speed in order to simultaneously visualize the motion of experts and trainees, and to study trainees' performances quantitatively. We review the research from different communities on synchronization problems of similar complexity. We give a detailed description of the two most applicable algorithms. We then present results using our AR based forceps delivery training system and therefore evaluate both methods for synchronization of experts' and trainees' 3D movements. We also introduce the first concepts of an online synchronization system allowing the trainee to follow movements of an expert and the experts to annotate 3D trajectories for initiation of actions such as display of timely information. A video demonstration provides an overview of the work and a visual idea of what users of the proposed system could observe through their video-see through HMD.

We propose an AR system that learns from the expert by tracking his movements while he/she uses a simulator or performs a real (often complicated) task. This information is reproduced for demonstration to students in an enhanced simulator. By simultaneous visualization and comparison of the experts and students performance, direct feedback is provided. We propose real-time recording of instruments' tracking data of expert's actions for latter replication in an AR system for teaching purposes. The movements are recorded relative to an existing physical simulator. By this means the student can view the subtle movements from any direction repeatedly without time limits of the expert in order to learn and improve his own performance.