Cargando…
Deep Learning for Semantic Segmentation of Defects in Advanced STEM Images of Steels
Crystalline materials exhibit long-range ordered lattice unit, within which resides nonperiodic structural features called defects. These crystallographic defects play a vital role in determining the physical and mechanical properties of a wide range of material systems. While computer vision has de...
Autores principales: | Roberts, Graham, Haile, Simon Y., Sainju, Rajat, Edwards, Danny J., Hutchinson, Brian, Zhu, Yuanyuan |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6726638/ https://www.ncbi.nlm.nih.gov/pubmed/31484940 http://dx.doi.org/10.1038/s41598-019-49105-0 |
Ejemplares similares
-
Semantic segmentation of PolSAR image data using advanced deep learning model
por: Garg, Rajat, et al.
Publicado: (2021) -
DefectTrack: a deep learning-based multi-object tracking algorithm for quantitative defect analysis of in-situ TEM videos in real-time
por: Sainju, Rajat, et al.
Publicado: (2022) -
Orchard Mapping with Deep Learning Semantic Segmentation
por: Anagnostis, Athanasios, et al.
Publicado: (2021) -
Deep Semantic Segmentation of Angiogenesis Images
por: Ibragimov, Alisher, et al.
Publicado: (2023) -
How deep learning is empowering semantic segmentation: Traditional and deep learning techniques for semantic segmentation: A comparison
por: Sehar, Uroosa, et al.
Publicado: (2022)