Identifying local structural states in atomic imaging by computer vision
The availability of atomically resolved imaging modalities enables an unprecedented view into the local structural states of materials, which manifest themselves by deviations from the fundamental assumptions of periodicity and symmetry. Consequently, approaches that aim to extract these local struc...
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Springer International Publishing
2016
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| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5093204/ |
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pubmed-50932042016-11-17 Identifying local structural states in atomic imaging by computer vision Laanait, Nouamane Ziatdinov, Maxim He, Qian Borisevich, Albina Research The availability of atomically resolved imaging modalities enables an unprecedented view into the local structural states of materials, which manifest themselves by deviations from the fundamental assumptions of periodicity and symmetry. Consequently, approaches that aim to extract these local structural states from atomic imaging data with minimal assumptions regarding the average crystallographic configuration of a material are indispensable to advances in structural and chemical investigations of materials. Here, we present an approach to identify and classify local structural states that is rooted in computer vision. This approach introduces a definition of a structural state that is composed of both local and nonlocal information extracted from atomically resolved images, and is wholly untethered from the familiar concepts of symmetry and periodicity. Instead, this approach relies on computer vision techniques such as feature detection, and concepts such as scale invariance. We present the fundamental aspects of local structural state extraction and classification by application to simulated scanning transmission electron microscopy images, and analyze the robustness of this approach in the presence of common instrumental factors such as noise, limited spatial resolution, and weak contrast. Finally, we apply this computer vision-based approach for the unsupervised detection and classification of local structural states in an experimental electron micrograph of a complex oxides interface, and a scanning tunneling micrograph of a defect-engineered multilayer graphene surface. Springer International Publishing 2016-11-02 2017 /pmc/articles/PMC5093204/ /pubmed/27867837 http://dx.doi.org/10.1186/s40679-016-0028-8 Text en © The Author(s) 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
| repository_type |
Open Access Journal |
| institution_category |
Foreign Institution |
| institution |
US National Center for Biotechnology Information |
| building |
NCBI PubMed |
| collection |
Online Access |
| language |
English |
| format |
Online |
| author |
Laanait, Nouamane Ziatdinov, Maxim He, Qian Borisevich, Albina |
| spellingShingle |
Laanait, Nouamane Ziatdinov, Maxim He, Qian Borisevich, Albina Identifying local structural states in atomic imaging by computer vision |
| author_facet |
Laanait, Nouamane Ziatdinov, Maxim He, Qian Borisevich, Albina |
| author_sort |
Laanait, Nouamane |
| title |
Identifying local structural states in atomic imaging by computer vision |
| title_short |
Identifying local structural states in atomic imaging by computer vision |
| title_full |
Identifying local structural states in atomic imaging by computer vision |
| title_fullStr |
Identifying local structural states in atomic imaging by computer vision |
| title_full_unstemmed |
Identifying local structural states in atomic imaging by computer vision |
| title_sort |
identifying local structural states in atomic imaging by computer vision |
| description |
The availability of atomically resolved imaging modalities enables an unprecedented view into the local structural states of materials, which manifest themselves by deviations from the fundamental assumptions of periodicity and symmetry. Consequently, approaches that aim to extract these local structural states from atomic imaging data with minimal assumptions regarding the average crystallographic configuration of a material are indispensable to advances in structural and chemical investigations of materials. Here, we present an approach to identify and classify local structural states that is rooted in computer vision. This approach introduces a definition of a structural state that is composed of both local and nonlocal information extracted from atomically resolved images, and is wholly untethered from the familiar concepts of symmetry and periodicity. Instead, this approach relies on computer vision techniques such as feature detection, and concepts such as scale invariance. We present the fundamental aspects of local structural state extraction and classification by application to simulated scanning transmission electron microscopy images, and analyze the robustness of this approach in the presence of common instrumental factors such as noise, limited spatial resolution, and weak contrast. Finally, we apply this computer vision-based approach for the unsupervised detection and classification of local structural states in an experimental electron micrograph of a complex oxides interface, and a scanning tunneling micrograph of a defect-engineered multilayer graphene surface. |
| publisher |
Springer International Publishing |
| publishDate |
2016 |
| url |
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5093204/ |
| _version_ |
1613710561339506688 |