图像滤波的形态学开、闭型神经网络算法
MORPHOLOGICAL OPENING AND CLOSING NEURAL NETWORKS FOR IMAGE FILTERING
-
摘要: 该文设计完成了一种具有实用意义的形态学开、闭滤波的神经网络模型及其滤波参数的优化训练算法。实验结果表明该方法设计简便,实用性强且易于推广,对提高形态滤波性能效果明显。分析表明,形态滤波器可分解为形态滤波运算和结构元素选择两个基本问题。形态滤波运算规则已由定义本身确定,于是形态滤波器的最终滤波性能就仅仅取决于结构元素的选择。进行自适应优化训练的目的正是使结构元素具有图像目标的形态结构特征,从而使形态滤波器对复杂变化的图像具有良好的滤波性能和稳健的适应能力。Abstract: This paper presents morphological neural networks of opening and closing operation for pratical use, and the algorithm to design optimal parameters of a morphological filter, Experimental results show that this method is good in practice and easy to extend. It has better filtering properties than that of the conventional morphological ones. The task of creating a morphological filter can be divided into two basic problems, selecting a morphological operation and Structuring Element (SE). The set of morpholo...
-
E.R. Dougherty, et al., Digital Image Processing Methods, New York, Marcel Dekker, 1994,110-138.[2]龚炜,石青云,程民德,数字空间中的数学形态学,北京,科学出版社,1997,137-162.[3]C.P. Suarez-Araujo, Novel neural-network models for computing homothetic invariances: Animage algebra notation, J. Math. Imaging and Vision, 1997, 7(1), 69-83.[4]Yonggwan Won, et al., Morphological shared-weight networks with applications to automatictarget recognition, IEEE Trans. on Neural Networks, 1997, NN-8(5), 1195-1203.[5]Gerhard X. Ritter, Morphological associatiative memories, IEEE Trans. on Neural Networks,1998, NN-9(2), 281-292.[6]余农,李吉成,一种自适应训练形态滤波参数的神经网络方法,中国电子学会第四届青年学术年会文集,北京,电子工业出版社,1998,537-540.[7]李吉成,李飚,灰度形态滤波器的神经网络实现方法,系统工程与电子技术,1999,21(3),56-59.[8]汪云九,崔,齐翔林,BP学习网络中权值的感受野型初始化研究,自然科学进展,1996,6(3),346-350. -
计量
- 文章访问数: 2349
- HTML全文浏览量: 120
- PDF下载量: 520
- 被引次数: 0
下载:
