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Abstract : |
The construction of computer vision and robot control algorithms from training data is a challenging application for artificial neural networks. However, many practical applications require an approach that is workable with a small number of data examples. In this contribution, we describe results on the use of "Parametrized Self-organizing Maps " ("PSOMs") with this goal in mind. We report results that demonstrate that a small number of labeled training images is sufficient to construct PSOMs to identify the position of finger tips in images of 3D-hand shapes to within an accuracy of only a few pixel locations. Further we present a framework of hierarchical PSOMs that allows rapid "oneshot-learning " after acquiring a number of "basis mappings " during a previous "investment learning stage". We demonstrate the potential of this approach with the task of constructing the position-dependent mapping from camera coordinates to the work space coordinates of a Puma robot. 1, |
