A general and robust analytical method for interface normal determination in TEM.

This paper presents a new analytical method to determine interface normals from a series of bright/dark field images taken from arbitrary orientations. This approach, based on a general geometrical model of interface projection, provides a generalized formulation of existing methods. It can treat an excessive number of inputs, i.

3D nanostructural characterisation of grain boundaries in atom probe data utilising machine learning methods.

Boosting is a family of supervised learning algorithm that convert a set of weak learners into a single strong one. It is popular in the field of object tracking, where its main purpose is to extract the position, motion, and trajectory from various features of interest within a sequence of video frames. A scientific application explored in this study is to combine the boosting tracker and the Hough transformation, followed by principal component analysis, to extract the location and trace of grain boundaries within atom probe data.