X-Ray scattering image classification using deep learning

Boyu Wang, Kevin Yager, Dantong Yu, Minh Hoai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

18 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationProceedings - 2017 IEEE Winter Conference on Applications of Computer Vision, WACV 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages697-704
Number of pages8
ISBN (Electronic)9781509048229
DOIs
StatePublished - May 11 2017
Externally publishedYes
Event17th IEEE Winter Conference on Applications of Computer Vision, WACV 2017 - Santa Rosa, United States
Duration: Mar 24 2017Mar 31 2017

Publication series

NameProceedings - 2017 IEEE Winter Conference on Applications of Computer Vision, WACV 2017

Other

Other17th IEEE Winter Conference on Applications of Computer Vision, WACV 2017
CountryUnited States
CitySanta Rosa
Period3/24/173/31/17

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Computer Vision and Pattern Recognition

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