Materials Discovery: Fine-Grained Classification of X-Ray Scattering Images
Abstract
We explore the use of computer vision methods for orga-nizing, searching, and classifying x-ray scattering images. X-ray scattering is a technique that shines an intense beam of x-rays through a sample of interest. By recording the in-tensity of x-ray deflection as a function of angle, scientists can measure the structure of materials at the molecular and nano-scale. Current and planned synchrotron instruments are producing x-ray scattering data at an unprecedented rate, making the design of automatic analysis techniques crucial for future research. In this paper, we devise an attribute-based approach to recognition in x-ray scattering images and demonstrate applications to image annotation and retrieval. 1.
Cite
Text
Kiapour et al. "Materials Discovery: Fine-Grained Classification of X-Ray Scattering Images." IEEE/CVF Winter Conference on Applications of Computer Vision, 2014. doi:10.1109/WACV.2014.6836004Markdown
[Kiapour et al. "Materials Discovery: Fine-Grained Classification of X-Ray Scattering Images." IEEE/CVF Winter Conference on Applications of Computer Vision, 2014.](https://mlanthology.org/wacv/2014/kiapour2014wacv-materials/) doi:10.1109/WACV.2014.6836004BibTeX
@inproceedings{kiapour2014wacv-materials,
title = {{Materials Discovery: Fine-Grained Classification of X-Ray Scattering Images}},
author = {Kiapour, M. Hadi and Yager, Kevin G. and Berg, Alexander C. and Berg, Tamara L.},
booktitle = {IEEE/CVF Winter Conference on Applications of Computer Vision},
year = {2014},
pages = {933-940},
doi = {10.1109/WACV.2014.6836004},
url = {https://mlanthology.org/wacv/2014/kiapour2014wacv-materials/}
}