RT journal article
T1 Unsupervised Learning for the Segmentation of Small Crystalline Particles at the Atomic Level
A1 Barcena González, Guillermo
A1 Hernández-Robles, Andrei
A1 Mayoral, Álvaro
A1 Martínez, Lidia
A1 Huttel, Yves
A1 Galindo Riaño, Pedro Luis
A1 Ponce, Arturo
A2 Ingeniería Informática
AB Electron backscattering diffraction provides the analysis of crystalline phases at large scales (microns) while precession electron diffraction may be used to get 4D-STEM data to elucidate structure at nanometric resolution. Both are limited by the probe size and also exhibit some difficulties for the generation of large datasets, given the inherent complexity of image acquisition. The latter appoints the application of advanced machine learning techniques, such as deep learning adapted for several tasks, including pattern matching, image segmentation, etc. This research aims to show how Gabor filters provide an appropriate feature extraction technique for electron microscopy images thatcould prevent the need of large volumes of data to train deep learning models.The work presented herein combines an algorithm based on Gabor filters for feature extraction and an unsupervised learning method to perform particle segmentation of polyhedral metallic nanoparticles and crystal orientation mapping at atomic scale. Experimental results have shown that Gabor filtersare convenient for electron microscopy images analysis, that even a nonsupervised learning algorithm can provide remarkable results in crystal segmentation of individual nanoparticles. This approach enables its application to dynamic analysis of particle transformation recorded withaberration-corrected microscopy, offering new possibilities of analysis at nanometric scale.
SN 1521-4079
YR 2023
FD 2023-01-03
LK http://hdl.handle.net/10498/31790
UL http://hdl.handle.net/10498/31790
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 11-nov-2026