Improvement of SIMS image classification by means of wavelet de-noising

被引：0
|
作者：
M. Wolkenstein
H. Hutter
S. G. Nikolov
M. Grasserbauer
机构：
[1] Institute of Analytical Chemistry,
[2] Vienna University of Technology,undefined
[3] Getreidemarkt 9/151,undefined
[4] Vienna 1060,undefined
[5] Austria,undefined
来源：
Fresenius' Journal of Analytical Chemistry | 1997年 / 357卷
关键词：
Shrinkage; Analytical Image; Noise Level; Single Phase; Image Classification;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Classification is a powerful tool for the extraction of chemical information from analytical images. However, classification may sometimes not be applicable, when the clusters corresponding to single phases overlap due to high noise levels, such that they are not significantly distinguishable. We investigate the effect of the wavelet shrinkage de-noising algorithm on the subsequent classification of analytical images. By application of wavelet de-noising the distinction of phases in classification can be significantly improved.
引用
收藏
页码：783 / 788
页数：5
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