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Advanced Structural and Chemical Imaging

, 2:14

First Online: 02 November 2016Received: 23 August 2016Accepted: 26 October 2016


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.

KeywordsScanning transmission electron microscopy Scanning tunneling microscopy Computer vision Unsupervised machine learning AbbreviationsSTEMscanning transmission electron microscopy

STMscanning tunneling microscopy

SIFTscale-invariant feature transform

LoGLaplacian of Gaussian




Electronic supplementary materialThe online version of this article doi:10.1186-s40679-016-0028-8 contains supplementary material, which is available to authorized users.

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Autor: Nouamane Laanait - Maxim Ziatdinov - Qian He - Albina Borisevich


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