Abstract
The “life” of most neural vision systems splits into a one-time training phase and an application phase during which knowledge is no longer acquired. This is both technically inflexible and cognitively unsatisfying. Here we propose an appearance based vision system for object recognition which can be adapted online, both to acquire visual knowledge about new objects and to correct erroneous classification. The system works in an office scenario, acquisition of object knowledge is triggered by hand gestures. The neural classifier offers two ways of training: Firstly, the new samples can be added immediately to the classifier to obtain a running system at once, though at the cost of reduced classification performance. Secondly, a parallel processing branch adapts the classification system thoroughly to the enlarged image domain and loads the new classifier to the running system when ready.
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© 2004 Springer-Verlag Berlin Heidelberg
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Bekel, H., Bax, I., Heidemann, G., Ritter, H. (2004). Adaptive Computer Vision: Online Learning for Object Recognition. In: Rasmussen, C.E., Bülthoff, H.H., Schölkopf, B., Giese, M.A. (eds) Pattern Recognition. DAGM 2004. Lecture Notes in Computer Science, vol 3175. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28649-3_55
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DOI: https://doi.org/10.1007/978-3-540-28649-3_55
Publisher Name: Springer, Berlin, Heidelberg
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