Search Results for author:
Found 3 papers, 2 papers with code
A robust synthetic data generation framework for machine learning in High-Resolution Transmission Electron Microscopy (HRTEM)
Machine learning techniques are attractive options for developing highly-accurate automated analysis tools for nanomaterials characterization, including high-resolution transmission electron microscopy (HRTEM).
Generalization Across Experimental Parameters in Machine Learning Analysis of High Resolution Transmission Electron Microscopy Datasets
Overall, we find that our neural networks are not robust across microscope parameters, but do generalize across certain sample parameters.
Understanding the Influence of Receptive Field and Network Complexity in Neural-Network-Guided TEM Image Analysis
We find that for low-resolution TEM images which rely on amplitude contrast to distinguish nanoparticles from background, the receptive field does not significantly influence segmentation performance.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
