Theory of morphological neural networks
Theory of morphological neural networks
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形态神经网络理论
DOI：
10.1117/12.18085
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发表时间：
1990
期刊：
影响因子：
--
通讯作者：
G. Ritter
中科院分区：
文献类型：
--
作者：
J. Davidson;G. Ritter
与传统的模式识别方法不同，经典的人工神经网络理论已被用于解决图像处理中的模式识别问题。在标准的神经网络理论中，执行神经网络计算的第一步涉及将神经值乘以其突触强度并将结果相加的线性操作。阈值化通常遵循线性运算，以提供网络的非线性。本文提出了一种形态神经网络的基本理论，它用非线性的加法和极大值运算来代替乘法和求和。给出了几种与模式识别技术明显不同的基本应用，包括一种执行筛分算法的网络。
The theory of classical artificial neural networks has been used to solve pattern recognition problems in image processing that is different from traditional pattern recognition approaches. In standard neural network theory, the first step in performing a neural network calculation involves the linear operation of multiplying neural values by their synaptic strengths and adding the results. Thresholding usually follows the linear operation in order to provide for non-linearity of the network. This paper presents the fundamental theory for a morphological neural network which, instead of multiplication and summation, uses the non-linear operation of addition and maximum. Several basic applications which are distinctly different from pattern recognition techniques are given, including a net which performs a sieving algorithm.