Abstract
Electron and X-ray interactions with matter can be recorded as digital images, which are signal acquisition mechanisms often used to investigate materials microstructure. Recently, the ability to quickly acquire large datasets at high resolution has created new challenges in areas that rely upon image-based information. The proposed analysis schemes employ Convolutional Neural Networks as the core algorithm in the reconnaissance of expected events from data gathered in two regimes: experimentally and by simulation. At the interface of physical and digital datasets, we propose classification schemes that exploit complex geometrical structure from scientific images through different machine learning packages, such as MatConvNet and TensorFlow. Our results show correct classification rates over 90% considering thousands of samples from four image modalities: cryo-electron microscopy, X-ray diffraction, X-ray scattering and X-ray microtomography. Our main contributions are: (a) developing algorithms designed for data that stem from physical experiments; (b) building new software to constrain parameter space, particularly given new hardware; and (c) testing different CNN models for classification of scientific images.
Original language | English |
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Title of host publication | 2016 IEEE Applied Imagery Pattern Recognition Workshop, AIPR 2016 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Electronic) | 9781509032846 |
DOIs | |
State | Published - Aug 14 2017 |
Externally published | Yes |
Event | 2016 IEEE Applied Imagery Pattern Recognition Workshop, AIPR 2016 - Washington, United States Duration: Oct 18 2016 → Oct 20 2016 |
Publication series
Name | Proceedings - Applied Imagery Pattern Recognition Workshop |
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ISSN (Print) | 2164-2516 |
Conference
Conference | 2016 IEEE Applied Imagery Pattern Recognition Workshop, AIPR 2016 |
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Country/Territory | United States |
City | Washington |
Period | 10/18/16 → 10/20/16 |
Funding
The authors would like to thank the grants LBNL LDRD Neuromorphic Computing for Image Recognition and the Early Career Research project IDEAL, both under ASCR (DE-AC02-05CH11231) and the Center for Applied Mathematics for Energy Related Applications (CAMERA), under management of ASCR and Office of Basic Energy Sciences (BES) of the U.S. Department of Energy. We also thank IBM Almaden, particularly Ben Shaw and Alexander Andreopou-los for supporting our team developments during the IBM TrueNorth Boot Camp in May of 2016. Any opinion, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the Department of Energy.