Complex-Phase Steel Microstructure Segmentation Using UNet: Analysis across Different Magnifications and Steel Types
The quantification of the phase fraction is critical in materials science, bridging the gap between material composition, processing techniques, microstructure, and resultant properties. Traditional methods involving manual annotation are precise but labor-intensive and prone to human inaccuracies....
Main Authors: | Bishal Ranjan Swain, Dahee Cho, Joongcheul Park, Jae-Seung Roh, Jaepil Ko |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2023-11-01
|
Series: | Materials |
Subjects: | |
Online Access: | https://www.mdpi.com/1996-1944/16/23/7254 |
Similar Items
-
ConvWin-UNet: UNet-like hierarchical vision Transformer combined with convolution for medical image segmentation
by: Xiaomeng Feng, et al.
Published: (2023-01-01) -
UNet Deep Learning Architecture for Segmentation of Vascular and Non-Vascular Images: A Microscopic Look at UNet Components Buffered With Pruning, Explainable Artificial Intelligence, and Bias
by: Jasjit S. Suri, et al.
Published: (2023-01-01) -
Dual‐ and triple‐stream RESUNET/UNET architectures for multi‐modal liver segmentation
by: Hagar Louye Elghazy, et al.
Published: (2023-03-01) -
MR-UNet Commodity Semantic Segmentation Based on Transfer Learning
by: Zhengrong Wu, et al.
Published: (2021-01-01) -
A novel approach for semantic segmentation of automatic road network extractions from remote sensing images by modified UNet
by: Miral J. Patel, et al.
Published: (2022-10-01)