Multi-resolution models for data processing: an experimental sensitivity analysis

Author

Ferrari, Stefano ; Borghese, N. Alberto ; Piuri, Vincenzo

Author_Institution

Dept. of Electron. & Inf., Politecnico di Milano, Italy

Volume

2

fYear

2000

fDate

2000

Firstpage

1056

Abstract

Hierarchical Radial Basis Functions Networks (HRBF) have been recently introduced as a tool for adaptive multiscale image reconstruction from range data. They are based on local operation on the data and are able to give a sparse approximation. In this paper HRBF are reframed for the regular sampling case, and they are compared with Wavelet Decomposition. Results show that HRBF, thanks to their constructive approach to approximation, are much more tolerant to errors in the parameters when errors occurs in the configuration phase, while they are more sensitive to the errors which occurs since the network has been configured

Keywords

image reconstruction; image sampling; radial basis function networks; Hierarchical Radial Basis Functions Networks; adaptive multiscale image reconstruction; configuration phase; data processing; errors; experimental sensitivity analysis; multiresolution models; sparse approximation; Convolution; Data processing; Electronic mail; Finite impulse response filter; Image reconstruction; Laboratories; Multiresolution analysis; Radial basis function networks; Sensitivity analysis; Signal resolution;

fLanguage

English

Publisher

ieee

Conference_Titel

Instrumentation and Measurement Technology Conference, 2000. IMTC 2000. Proceedings of the 17th IEEE

Conference_Location

Baltimore, MD

ISSN

1091-5281

Print_ISBN

0-7803-5890-2

Type

conf

DOI

10.1109/IMTC.2000.848902

Filename

848902