.png){kind=icon}
返回
Multiscale models for data processing: An experimental sensitivity analysis
.png){kind=icon}
PRE
AI
DOI：10.1109/19.948314.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
basis functions
function spaces
iterative decomposition
multiresolution analysis
multiscale signal decomposition
quantization error
RBF networks
robustness
sensitivity
signal processing
wavelets
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。