检索结果-内蒙古大学图书馆

3rd International Joint Conference on Discrete Geometry and Mathematical Morphology (DGM)

作者： Marcondes, Diego Feldman, Mariana Barrera, Junior Univ Sao Paulo Inst Math & Stat Dept Comp Sci Sao Paulo Brazil Texas A&M Univ Dept Elect & Comp Engn College Stn TX USA

ISBN: (纸本)9783031577925;9783031577932

关键词： discrete morphological neural networks image processing mathematical morphology U-curve algorithms stochastic lattice descent algorithm

来源：评论

学校读者我要写书评

暂无评论

Unrestricted Sequential Discrete Morphological Neural Networks

引用

Journal of Mathematical Imaging and Vision 2025年第4期67卷 1-22页

作者： Marcondes, Diego Feldman, Mariana Barrera, Junior Department of Computer Science Institute of Mathematics and Statistics University of São Paulo São Paulo Brazil

There have been attempts to insert mathematical morphology (MM) operators into convolutional neural networks (CNN), and the most successful endeavor to date has been the morphological neural networks (MNN). Although MNN have performed better than CNN in solving some problems, they inherit their black-box nature. Furthermore, in the case of binary images, they are approximations that lose the Boolean lattice structure of MM operators and, thus, it is not possible to represent a specific class of W-operators with desired properties. In a recent work, we proposed the discrete morphological neural networks (DMNN) for binary image transformation to represent specific classes of W-operators and estimate them via machine learning. We also proposed a stochastic lattice descent algorithm (SLDA) to learn the parameters of canonical discrete morphological neural networks (CDMNN), whose architecture is composed only of operators that can be decomposed as the supremum, infimum, and complement of erosions and dilations. In this paper, we propose an algorithm to learn unrestricted sequential DMNN (USDMNN) for image processing and binary classification, whose architecture is given by the composition of general W-operators. With an efficient implementation that leverages GPUs for matrix computations, we illustrate the algorithm in an example of image transformation, for learning the transition W-operator of the Conway’s Game of Life and for classifying the manuscript digits of the MNIST dataset. The performance of USDMNN on the MNIST dataset was compared with a CNN and the USDMNN performed better when trained with small sample sizes. These examples illustrate the robustness of the method to noise and its advantages over CNN related to the ability to learn with fewer samples and the interpretability of the results.

关键词： discrete morphological neural networks image processing image classification mathematical morphology U-curve algorithms stochastic lattice descent algorithm

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：