PUB - Publikationen an der Universität Bielefeld

Computer vision is more and more becoming an integral part in human-machine interfaces. Recent research aims at establishing a seamless and natural way of interaction between a user and an application system. Gesture recognition, context awareness, and grounding concepts in the commonly perceived environment as well as in the interaction history are key abilities of such systems. In parallel over the last years, computer vision research has indicated that integrated systems that are embedded in the world and actively interact with their environment seem to be a necessary precondition for solving more general computer vision tasks. In this context, cognitive computer vision systems emerged which aim at the generation of knowledge on the basis of perception, reasoning, learning and prior models. In both cases, integration, interaction and organization of memory become key issues in system design and practical research. In this article we will present a computational framework for integrated vision systems that is centered around an active memory component. It supports a fast integration and substitution of system components, various means of interaction patterns, and enables a system to reason about its own memory content. It will be exemplified by a cognitive humanmachine interface in an Augmented Reality scenario. The system is able to acquire new concepts from an interaction history and provides a context aware scene augmentation for the user.