Training-based optimization of soft morphological filters

被引：8
|
作者：
Koivisto, P
Huttunen, H
Kuosmanen, P
机构：
[1] Tampere University of Technology, Signal Processing Laboratory, FIN-33101 Tampere
来源：
JOURNAL OF ELECTRONIC IMAGING | 1996年 / 5卷 / 03期
关键词：
D O I：
10.1117/12.242617
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Soft morphological filters farm a large class of nonlinear filters with many desirable properties. However, few design methods exist for these filters. This paper demonstrates how optimization schemes. simulated annealing and genetic algorithms, can be employed in the search for soft morphological filters having optimal performance in a given signal processing task. Furthermore, the properties of the achieved optimal soft morphological filters in different situations are analyzed. (C) 1996 SPIE and IS&T.
引用
收藏
页码：300 / 322
页数：23
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