Learning optimization of morphological filters with gray scale structuring elements

被引：4
作者：
Asano, A
Yamashita, T
Yokozeki, S
机构：
[1] Kyushu Institute of Technology, Department of Mechanical System Engineering, Iizuka, Fukuoka 820
来源：
OPTICAL ENGINEERING | 1996年 / 35卷 / 08期
关键词：
optical neural networks; mathematical morphology; nonlinear filters; image processing; simulated annealing; neural networks;
D O I：
10.1117/1.600827
中图分类号：
O43 [光学];
学科分类号：
070207 ; 0803 ;
摘要：
Mathematical morphology with gray scale structuring elements has attracted much attention, since combinations of the operations in this class can realize almost all noise-removing filters. However, the optimization method for the combination is still uncertain. In this paper, an optimization method for a mathematical morphological filter with gray scale structuring elements is proposed. This method is based on the concept of a neural network with morphological operations and on learning using simulated annealing. The method is also applied to gray scale bipolar morphological filters for image differentiation. (C) 1996 Society of Photo-Optical Instrumentation Engineers.
引用
收藏
页码：2203 / 2213
页数：11
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