DocumentCode :
2096311
Title :
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
Link To Document :
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