In this paper, a novel real-time online network model is presented. It is derived from the hierarchical radial basis function (HRBF) model and it grows by automatically adding units at smaller scales, where the surface details are located, while data points are being collected. Real-time operation is achieved by exploiting the quasi-local nature of the Gaussian units: through the definition of a quad-tree structure to support their receptive field local network reconfiguration can be obtained. The model has been applied to 3-D scanning, where an updated real-time display of the manifold to the operator is fundamental to drive the acquisition procedure itself. Quantitative results are reported, which show that the accuracy achieved is comparable to that of two batch approaches: batch HRBF and support vector machines (SVMs). However, these two approaches are not suitable to real-time online learning. Moreover, proof of convergence is also given.

A hierarchical RBF online learning algorithm for real-time 3-D scanner / S. Ferrari, F. Bellocchio, V. Piuri, N.A. Borghese. - In: IEEE TRANSACTIONS ON NEURAL NETWORKS. - ISSN 1045-9227. - 21:2(2010 Feb), pp. 5352301.275-5352301.285. [10.1109/TNN.2009.2036438]

### A hierarchical RBF online learning algorithm for real-time 3-D scanner

#### Abstract

In this paper, a novel real-time online network model is presented. It is derived from the hierarchical radial basis function (HRBF) model and it grows by automatically adding units at smaller scales, where the surface details are located, while data points are being collected. Real-time operation is achieved by exploiting the quasi-local nature of the Gaussian units: through the definition of a quad-tree structure to support their receptive field local network reconfiguration can be obtained. The model has been applied to 3-D scanning, where an updated real-time display of the manifold to the operator is fundamental to drive the acquisition procedure itself. Quantitative results are reported, which show that the accuracy achieved is comparable to that of two batch approaches: batch HRBF and support vector machines (SVMs). However, these two approaches are not suitable to real-time online learning. Moreover, proof of convergence is also given.
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3-D scanner; Multiscale manifold approximation; Online learning; Radial basis function (RBF) networks; Real-time parameters estimate
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Utilizza questo identificativo per citare o creare un link a questo documento: `http://hdl.handle.net/2434/140076`
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