PUB - Publikationen an der Universität Bielefeld

In the last years, computer vision research is more and more shifting from algorithmic solutions to the construction of active systems. One novel approach to system construction combines data- and event-driven architectures, concentrating on the flow of information between components. A challenge in data-driven architectures is to optimize communications behavior without changing component implementations. For example, in computer vision, a common problem is that low-level components produce many very similar results whereas on a higher level, only significant changes are of interest. This distinction can be defined as a pattern recognition task that analyzes the data flow in the system. In the following, we will first give a short introduction into the architecture, then describe a generic solution for data-flow reduction based on XML distance metrics. We present first results on the application of this component in an integration framework for a vision-based Human-Computer-Interface within an augmented reality scenario.