From Images via Symbols to Contexts: Using Augmented Reality for Interactive Model Acquisition
Sven Wachsmuth, Marc Hanheide, Sebastian Wrede, Christian Bauckhage · Lincoln Repository (University of Lincoln) · 2005
Abstract. Systems that perform in real environments need to bind the internal state to externally perceived objects, events, or complete scenes. How to learn this correspondence has been a long standing problem in computer vision as well as artificial intelligence. Augmented Reality provides an interesting perspective on this problem because a human user can directly relate displayed system results to real environments. In the following we present a system that is able to bootstrap internal models from user-system interactions. Starting from pictorial representations it learns symbolic object labels that provide the basis for storing observed episodes. In a second step, more complex relational information is extracted from stored episodes that enables the system to react on specific scene contexts. 1