Materials discovery: Fine-grained classification of X-ray scattering images
Citation
Kiapour, M.H.; Yager, K.G.; Berg, A.C.; Berg, T.L. "Materials discovery: Fine-grained classification of X-ray scattering images "
Applications of Computer Vision (WACV) 2014,
1 933–940.
doi: 10.1109/WACV.2014.6836004Summary
We evaluate the ability of machine learning algorithms to automatically tag x-ray scattering data.
Abstract
We explore the use of computer vision methods for organizing, 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 intensity 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.
