Referenced Items are not available for this document.
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
Published in:
Instrumentation and Measurement, IEEE Transactions on
(Volume:50
,
Issue:
4
)
Date of Publication: Aug 2001
- Page(s):
- 995 - 1002
- ISSN :
- 0018-9456
- INSPEC Accession Number:
- 7059355
- Digital Object Identifier :
- 10.1109/19.948314
- Product Type:
- Journals & Magazines
- Date of Current Version :
- 07 August 2002
- Issue Date :
- Aug 2001
- Sponsored by :
- IEEE Instrumentation and Measurement Society
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