Multiscale models for data processing: an experimental sensitivity analysis

Author

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

Author_Institution

Dipt. di Elettronica & Inf., Politecnico di Milano, Italy

Volume

50

Issue

4

fYear

2001

fDate

8/1/2001 12:00:00 AM

Firstpage

995

Lastpage

1002

Abstract

Hierarchical radial basis functions (HRBFs) networks have been recently introduced as a tool for adaptive multiscale image reconstruction from range data. These are based on local operation on the data and are able to give a sparse approximation. In this paper, HRBFs are reframed for the regular sampling case, and they are compared with wavelet decomposition. Results show that HRBFs, thanks to their constructive approach to approximation, are much more tolerant on errors in the parameters when errors occur in the configuration phase

Keywords

adaptive systems; image reconstruction; iterative methods; radial basis function networks; sensitivity analysis; wavelet transforms; RBF networks; adaptive multiscale image reconstruction; approximation; configuration phase; data processing; error tolerance; experimental sensitivity analysis; hierarchical radial basis functions networks; iterative decomposition; multiresolution analysis; multiscale models; quantisation errors; robustness; sparse approximation; wavelets; Biomedical signal processing; Data processing; Embedded system; Image reconstruction; Image sampling; Multiresolution analysis; Quantization; Sensitivity analysis; Signal resolution; Wavelet analysis;

fLanguage

English

Journal_Title

Instrumentation and Measurement, IEEE Transactions on

Publisher

ieee

ISSN

0018-9456

Type

jour

DOI

10.1109/19.948314

Filename

948314