## First milestone (may 2003)

At first MAVR at DSI has focused on building the software engine able to filter
the noise in the range data acquired by robot sensors and to transform them
into 3D models. This problem has been tackled by optimizing for our problem,
a particular neural network model developed by our group, called Hierarchical
Radial Basis Function Network. Optimization is based on defining a set of operations,
which can work locally on the data. The introduction of an efficient range partitioning
schema has allowed to produce a very fast algorithm. Moreover, the developed
technique allows to produce a model at different scales, feature which can be
extremely useful in wire-less low bandwidth comunication.

For the first milestone, the software which implements this model has also been
developed and it is available at the software WEB page of MAVR: homes.dsi.unimi.it/˜borghese/Research/Software/Software.html.

**References**

Borghese N.A., Maggioni M. and Ferrari S., Multi-Scale Approximation with Hierarchical
Radial Basis Functions Networks, IEEE Trans. Neural Networks, In press.

N.A. Borghese, S. Ferrari and V. Piuri (2002) Real-time
Surface Reconstruction through HRBF Networks. Proc. IEEE International Workshop
on Haptic Audio Visual Environments and their Applications, **Invited Paper.**
Proc. HAVE 2002, pp.19-25.
Ferrari S., Borghese N.A. and Piuri V. (2001), Multi-resolution
Models for Data Processing: an Experimental Sensitivity Analysis, *IEEE
Trans. on Instrumentation & Measurement*. Vol. 50(4), pp. 995-1002.
Borghese N.A. and Ferrari S. (2000), A
Portable Modular System for Automatic Acquisition of 3D Objects, *IEEE
Trans. Instrumentation & Mesurement* , Vol. 49(5), pp. 1128-1136.