Parametrized SOMs for Object Recognition and Pose Estimation

Authors:
Axel Saalbach;Gunther Heidemann;Helge Ritter
Affiliations:
-;-;-
Venue:
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Year:
2002

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Abstract

We present the "Parametrized Self-Organizing Map" (PSOM) as a method for 3D object recognition and pose estimation. The PSOM can be seen as a continuous extension of the standard Self-Organizing Map which generalizes the discrete set of reference vectors to a continuous manifold. In the context of visual learning, manifolds based on PSOMs can be used to represent the appearance of various objects. We demonstrate this approach and its merits in an application example.