X-Ray Scattering Image Classification Using Deep Learning

Boyu Wang, Kevin G. Yager, Dantong Yu, Minh Hoai

WACV 2017 pp. 697-704

doi:10.1109/WACV.2017.83 /wacv/2017/wang2017wacv-x/

Abstract

Visual inspection of x-ray scattering images is a powerful technique for probing the physical structure of materials at the molecular scale. In this paper, we explore the use of deep learning to develop methods for automatically analyzing x-ray scattering images. In particular, we apply Convolutional Neural Networks and Convolutional Autoencoders for x-ray scattering image classification. To acquire enough training data for deep learning, we use simulation software to generate synthetic x-ray scattering images. Experiments show that deep learning methods outperform previously published methods by 10% on synthetic and real datasets.

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Cite

Text

Wang et al. "X-Ray Scattering Image Classification Using Deep Learning." IEEE/CVF Winter Conference on Applications of Computer Vision, 2017. doi:10.1109/WACV.2017.83

Markdown

[Wang et al. "X-Ray Scattering Image Classification Using Deep Learning." IEEE/CVF Winter Conference on Applications of Computer Vision, 2017.](https://mlanthology.org/wacv/2017/wang2017wacv-x/) doi:10.1109/WACV.2017.83

BibTeX

@inproceedings{wang2017wacv-x,
  title     = {{X-Ray Scattering Image Classification Using Deep Learning}},
  author    = {Wang, Boyu and Yager, Kevin G. and Yu, Dantong and Hoai, Minh},
  booktitle = {IEEE/CVF Winter Conference on Applications of Computer Vision},
  year      = {2017},
  pages     = {697-704},
  doi       = {10.1109/WACV.2017.83},
  url       = {https://mlanthology.org/wacv/2017/wang2017wacv-x/}
}