Abstract
Radial Basis Function (RBF) networks have been proposed as suitable representations for 3-D objects, in particular, since they can learn view-based representations from a small set of training views. One of the basic questions that arises in the context of RBF networks concerns their complexity, i.e., the number of basis functions that are necessary for a reliable representation, which should balance the accuracy and the robustness. In this paper we propose a systematic approach for building object representations in terms of RBF networks. We studied and designed two procedures: the “off-line” procedure, where the network is constructed after having a complete set of training views of an object, and the “on-line” procedure, where the network is incrementally built as new views of an object arrive.
This work was supported by a grant from the Austrian National Fonds zur Förderung der wissenschaftlichen Forschung (S7002MAT and P10539MAT). A. Leonardis acknowledges partial support by the Ministry of Science and Technology of Republic of Slovenia (Projects J2-0414, J2-8829).
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© 1999 Springer-Verlag Wien
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Bischof, H., Leonardis, A. (1999). On-line Learning of Object Representations. In: Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-6384-9_14
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DOI: https://doi.org/10.1007/978-3-7091-6384-9_14
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-83364-3
Online ISBN: 978-3-7091-6384-9
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