We describe an augmented reality system designed for online acquisition of visual knowledge and retrieval of memorized objects. The system relies on a head mounted camera and display, which allow the user to view the environment together with overlaid augmentations by the system. In this setup, communication by hand gestures and speech is mandatory as common input devices like mouse and keyboard are not available. Using gesture and speech, basically three types of tasks must be handled: (i) Communication with the system about the environment, in particular, directing attention towards objects and commanding the memorization of sample views; (ii) control of system operation, e.g. switching between display modes; and (iii) re-adaptation of the interface itself in case communication becomes unreliable due to changes in external factors, such as illumination conditions. We present an architecture to manage these tasks and describe and evaluate several of its key elements, including modules for pointing gesture recognition, menu control based on gesture and speech, and control strategies to cope with situations when vision becomes unreliable and has to be re-adapted by speech.

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Abstract. We present a vision system for human-machine interaction that relies on a small wearable camera which can be mounted to common glasses. The camera views the area in front of the user, especially the hands. To evaluate hand movements for pointing gestures to objects and to recognise object reference, an approach relying on the integration of bottom-up generated feature maps and top-down propagated recognition results is introduced. In this vision system, modules for context free focus of attention work in parallel to a recognition system for hand gestures. In contrast to other approaches, the fusion of the two branches is not on the symbolic but on the sub-symbolic level by use of attention maps. This method is plausible from a cognitive point of view and facilitates the integration of entirely different modalities.

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Abstract-We present an approach for the convenient labeling of image patches gathered from an unrestricted environment. The system is employed for a mobile Augmented Reality (AR) gear: While the user walks around with the head-mounted AR-gear, context-free modules for focus-of-attention permanently sample the most "interesting" image patches. After this acquisition phase, a Self-Organizing Map (SOM) is trained on the complete set of patches, using combinations of MPEG-7 features as a data representation. The SOM allows visualization of the sampled patches and an easy manual sorting into categories. With very little effort, the user can compose a training set for a classifier, thus, unknown objects can be made known to the system. We evaluate the system for COIL-imagery and demonstrate that a user can reach satisfying categorization within few steps, even for image data sampled from walking in an office environment.