Research Article: Applying shot boundary detection for automated crystal growth analysis during in situ transmission electron microscope experiments

Date Published: January 3, 2017

Publisher: Springer International Publishing

Author(s): W. A. Moeglein, R. Griswold, B. L. Mehdi, N. D. Browning, J. Teuton.

http://doi.org/10.1186/s40679-016-0034-x

Abstract

In situ scanning transmission electron microscopy is being developed for numerous applications in the study of nucleation and growth under electrochemical driving forces. For this type of experiment, one of the key parameters is to identify when nucleation initiates. Typically, the process of identifying the moment that crystals begin to form is a manual process requiring the user to perform an observation and respond accordingly (adjust focus, magnification, translate the stage, etc.). However, as the speed of the cameras being used to perform these observations increases, the ability of a user to “catch” the important initial stage of nucleation decreases (there is more information that is available in the first few milliseconds of the process). Here, we show that video shot boundary detection can automatically detect frames where a change in the image occurs. We show that this method can be applied to quickly and accurately identify points of change during crystal growth. This technique allows for automated segmentation of a digital stream for further analysis and the assignment of arbitrary time stamps for the initiation of processes that are independent of the user’s ability to observe and react.

Partial Text

Atomic-scale images of interfaces/defects obtained from scanning transmission electron microscopes (STEM) have long been used to provide insights into the structure–property relationships of materials—for example, observations of atomic-scale intermixing at interfaces in semiconducting/oxide heterostructures have helped understand the unique electronic and magnetic properties of these systems [1, 2]. The development and application of the STEM techniques used in these and other studies (for example, [3–9]) start from the premise that the atoms in the structure do not move. However, the systems that are being developed for many novel energy technologies are far removed from this paradigm—their intrinsic functionality is wholly dependent on the motion of atoms. For example, in Li-ion batteries, the charge/discharge cycle involves the mobility of ions across the electrolyte–electrode interface [10]. To identify the key aspects of the complex processes and transients occurring in energy technologies, we must therefore develop in situ or operando methods that allow us to observe directly the functions of the system taking place during operation of the device.

The results in this section demonstrate the application of automated change detection techniques to STEM videos. The sample videos are discussed, including the challenges presented in the videos and encoding parameters. Next the algorithm applied to the videos is explained. This covers any assumptions made about the data as well as any defined parameters. Finally, the results of the algorithm applied to the sample videos are shown.

We have demonstrated that video analysis techniques used for shot boundary detection can be used to identify changes in the movies showing Li deposition/dissolution process in the in situ ec-STEM cell. Shot boundary detection offers a wide variety of techniques that can be applied to find points of change for different types of transitions and under different conditions. These methods allow for direct operation on compressed video without the need for full-frame decoding, which reduces the computational complexity. Metrics based on differences in motion between frames in MPEG video in the compressed domain are used. A metric is developed based on the total amount of change occurring at each point, which is used to identify transition regions. Experimental results show positive results for identifying the points where changes occur. These techniques could be applied to find transition points, which can aid in manual interpretation of the results, or potentially be applied to direct automatic frame capture.

 

Source:

http://doi.org/10.1186/s40679-016-0034-x