Machine eye for defects: Machine learning-based solution to identify and characterize topological defects in textured images of nematic materials

Haijie Ren, Weiqiang Wang, Wentao Tang, Rui Zhang

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DOI: 10.1103/physrevresearch.6.013259
Published: 3
Container: Physical Review Research
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.1103/physrevresearch.6.013259,
  title = {Machine eye for defects: Machine learning-based solution to identify and characterize topological defects in textured images of nematic materials},
  author = {Haijie Ren and Weiqiang Wang and Wentao Tang and Rui Zhang},
  year = {2024},
  journal = {Physical Review Research},
  doi = {10.1103/physrevresearch.6.013259},
  url = {https://doi.org/10.1103/physrevresearch.6.013259}
}

RIS

TY  - JOUR
TI  - Machine eye for defects: Machine learning-based solution to identify and characterize topological defects in textured images of nematic materials
AU  - Haijie Ren
AU  - Weiqiang Wang
AU  - Wentao Tang
AU  - Rui Zhang
PY  - 2024
JO  - Physical Review Research
DO  - 10.1103/physrevresearch.6.013259
UR  - https://doi.org/10.1103/physrevresearch.6.013259
ER  -

APA

Ren, H., Wang, W., Tang, W., & Zhang, R. (2024). Machine eye for defects: Machine learning-based solution to identify and characterize topological defects in textured images of nematic materials. Physical Review Research. https://doi.org/10.1103/physrevresearch.6.013259

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doaj · retrieved 2026-09-25T22:35:06.481Z