Convolutional Neural Network for Extracting 3D Point Clouds of Fibrous Web From Multi-Focus Images
Convolutional Neural Network for Extracting 3D Point Clouds of Fibrous Web From Multi-Focus Images
复制标题
用于从多焦点图像中提取纤维网 3D 点云的卷积神经网络
DOI：
10.1109/access.2020.2993625
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发表时间：
2020
期刊：
影响因子：
3.9
通讯作者：
Wang Rongwu
中科院分区：
文献类型：
--
作者：
Hou Jue;Ouyang Wenbin;Xu Bugao;Wang Rongwu
本文提出了一种从光学显微镜上获取的纤维网络的多聚焦图像中提取3D点云的新方法，以分析纤维网络的显微镜结构。该算法由两个主要部分组成：（1）使用conco
This paper presents a new method for extracting 3D point clouds from multi-focus images of a fibrous web acquired on an optical microscope to analyze microscopic structures of a fibrous web. The algorithm consists of two major parts: (1) utilizing a convolutional neural network (CNN) to extract in-focus objects from multi-focus images, and (2) a depth identification module (DIM) which is a frequency domain-based model used to identify the depths of object points. The network, namely the multi-focus image deblurring network (MIDN), was designed by introducing gradient features into the network to deblur images and generate the ranges of focal depths of object points. Based on the results of MIDN, DIM was constructed to calculates the focal plane depth for each point. The experiments show that the combination of MIDN and DIM provides a practical way to generate complete, accurate 3D structures of nonwoven.
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影响因子：
--
作者：
Biggs, David S C
通讯作者：
Biggs, David S C
DOI：
10.1117/12.602633
发表时间：
2005-03
期刊：
--
影响因子：
--
作者：
J. Conchello
通讯作者：
J. Conchello
影响因子：
19.5
作者：
Xie, Saining;Tu, Zhuowen
通讯作者：
Tu, Zhuowen
DOI：
10.7171/jbt.15-2602-003
发表时间：
2015-07-01
期刊：
Journal of biomolecular techniques : JBT
影响因子：
--
作者：
Jonkman, James;Brown, Claire M
通讯作者：
Brown, Claire M
影响因子：
2.5
作者：
Goldstein, JS;Reed, IS;Scharf, LL
通讯作者：
Scharf, LL