Deep-learning-based pyramid-transformer for localized porosity analysis of hot-press sintered ceramic paste
Explore this paper's citation graph
Summary
PSTNet (Pyramid Segmentation Transformer Net) is proposed for grain and pore segmentation in SEM images, which merges multi-scale feature maps through operations like recombination and upsampling to predict and generate segmentation maps.
- Type
- article
- Published
- 2024-09-04
- Cited by
- 8
- References
- 50
- Access
- Open access
- OpenAlex
- https://openalex.org/W39231159
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:272398552
Keywords
Federalist, Redistricting, Federalism, State (computer science), Political science
References
- Learning Deconvolution Network for Semantic Segmentation
- Fully convolutional networks for semantic segmentation
- SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
- Non-destructive Testing by Infrared Thermography Under Random Excitation and ARMA Analysis
- Effect of grain size on domain structures, dielectric and thermal depoling of Nd-substituted bismuth titanate ceramics
- Automatic grain boundary detection and grain size analysis using polarization micrographs or orientation images
- A non-destructive technique for measuring ceramic porosity using liquid nitrogen
- Deep learning in neural networks: An overview
- Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations
- Deep Residual Learning for Image Recognition
- Grain Size Automatic Determination for 7050 Al Alloy Based on a Fuzzy Logic Method
- Rethinking Atrous Convolution for Semantic Image Segmentation
- Relaxor ferroelectric 0.9BaTiO3–0.1Bi(Zn0.5Zr0.5)O3 ceramic capacitors with high energy density and temperature stable energy storage properties
- Fixing Weight Decay Regularization in Adam
- Progress in high-strain perovskite piezoelectric ceramics
- Feature Extraction and Grain Segmentation of Sandstone Images Based on Convolutional Neural Networks
- Grain size engineered lead-free ceramics with both large energy storage density and ultrahigh mechanical properties
- Quick image analysis of concrete pore structure based on deep learning
- WPU-Net: Boundary learning by using weighted propagation in convolution network
- In-Process monitoring of porosity during laser additive manufacturing process
Cited by
- Injectable hydrogel microsphere orchestrates immune regulation and bone regeneration via sustained release of calcitriol
- Hybrid Data Driven Deep Learning Framework for Material Property Prediction
- CGAM: End-to-end deep learning model for SEM-MLCCs grain morphology data extraction from instance segmentation
- Accurate Pore Segmentation and Porosity Prediction in SEM Images of Alumina Ceramics using Custom U-Net and Random Forest
- UAV-based panoramic imaging for automated bughole segmentation and quantitative color-difference assessment of concrete bridge piers
- Comparative Evaluation of Threshold-Based and CNN-Based Segmentation Methods for Multi-Modal Digital Images of Geotechnical Materials
- Interpretable convolutional neural network prediction of heat treatment conditions from lithium disilicate glass-ceramic microstructures
- Mechanical property prediction of 3D-printed bioceramics by deep learning for sintering condition optimization
Related papers
- Race, Redistricting, and the Manufactured Conundrum
- Guess What Happened on the Way to Revolution? Precursors to the Supreme Court's Federalism Revolution
- Leveraging Federalism: The Real Meaning of the Rehnquist Court's Federalism Jurisprudence for States
- Chief Justice Roberts, Justice Alito, and New Federalism Jurisprudence
- Did the Seventeenth Amendment Repeal Federalism
- Trimming the Least Dangerous Branch: the Anti-Federalists and the Implementation of Article III
- The Casey Five versus the Federalism Five: Supreme Legislator or Prudent Umpire?