Interactive Online Learning 1

We present a computer vision system for object recognition, which is integrated in an augmented reality setup. The system can be trained online to recognize objects in an intuitive way. The augmented reality gear allows interaction using hand gestures for the control of displayed “virtual menus.” The underlying neural recognition system combines feature extraction and classification. Its three-stage architecture facilitates fast adaptation: in a fast training (FT) mode, only the last stage is adapted, whereas complete training (CT) rebuilds the system from scratch. Using FT, online acquired views can be added at once to the classifier, the system being operational after a delay of less than a second, though still with reduced classification performance. In parallel, a new classifier is trained (CT) and loaded to the system when ready