Rodlike nanoparticle parameter measurement method based on improved Mask R-CNN segmentation
Authors:
- 18 November 2020
- journal article
- research article
- Published by Springer Nature in Signal, Image and Video Processing
- Vol. 15 (3) , 579-587
Abstract
No abstract availableKeywords
Funding Information
- Program for Innovative Research Team in University of Tianjin (TD13-5034)
- Tianjin Science and Technology Major Projects and Engineering (17ZXSCSY00060)
This publication has 15 references indexed in Scilit:
- Rod-Like Rutile Nanoparticles: Synthesis, Structure and MorphologyJournal of Nano Research, 2017
- Densely Connected Convolutional Networks2017
- Deep Residual Learning for Image Recognition2016
- Fully Convolutional Networks for Semantic SegmentationIEEE Transactions on Pattern Analysis and Machine Intelligence, 2016
- U-Net: Convolutional Networks for Biomedical Image Segmentation2015
- A multistage, semi-automated procedure for analyzing the morphology of nanoparticlesIIE Transactions, 2012
- Automatic segmentation of metaphase cells based on global context and variant analysisPattern Recognition, 2008
- LabelMe: A Database and Web-Based Tool for Image AnnotationInternational Journal of Computer Vision, 2007
- ImageJ for MicroscopyBioTechniques, 2007
- Cross-entropy embedding of high-dimensional data using the neural gas modelNeural Networks, 2005