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In Mixed Reality (MR) Environments, the user's view is augmented with virtual, artificial objects. To visualize virtual objects, the position and orientation of the user's view or the camera is needed. Tracking of the user's viewpoint is an essential area in MR applications, especially for interaction and navigation. In present systems, the initialization is often complex. For this reason, we introduce a new method for fast initialization of markerless object tracking. This method is based on Speed Up Robust Features and paradoxically on a traditional marker-based library. Most markerless tracking algorithms can be divided into two parts: an offline and an online stage. The focus of this paper is optimization of the offline stage, which is often time-consuming.
This paper describes FGPA-based image combining for parallel graphics systems. The goal of our current work is to reduce network traffic and latency for increasing performance in parallel visualization systems. Initial data distribution is based on a common ethernet network whereas image combining and returning differs to traditional parallel rendering methods. Calculated sub-images are grabbed directly from the DVI-Ports for fast image compositing by a FPGA-based combiner.