@article {2032,
	title = {Co valence transformation in isopolar LaCoO3/LaTiO3 perovskite heterostructures via interfacial engineering},
	journal = {Phys. Rev. Materials},
	volume = {4},
	year = {2020},
	chapter = {026001},
	doi = {https://doi.org/10.1103/PhysRevMaterials.4.026001},
	author = {G Araizi-Kanoutas and J Geessinck and N Gauquelin and S Smit and X H Verbeeck and S K Mishra and P Bencok and C Schlueter and T-L Lee and D Krishnan and J Fatermans and Jo Verbeeck and G Rijnders and G Koster and M S Golden}
}
@conference {1974,
	title = {Atom column detection from STEM images using the maximum a posteriori probability rule},
	year = {2019},
	author = {J Fatermans and Arnold Jan den Dekker and O{\textquoteright}Leary, C M. and Peter D Nellist and Sandra Van Aert}
}
@conference {1975,
	title = {Atom detection from electron microscopy images},
	year = {2019},
	pages = {15},
	author = {J Fatermans and Arnold Jan den Dekker and Sandra Van Aert}
}
@article {1925,
	title = {The maximum a posteriori probability rule for atom column detection from HAADF STEM images},
	journal = {Ultramicroscopy},
	volume = {201},
	year = {2019},
	pages = {81-91},
	abstract = {Recently, the maximum a posteriori (MAP) probability rule has been proposed as an objective and quantitative method to detect atom columns and even single atoms from high-resolution high-angle annular dark-field (HAADF) scanning transmission electron microscopy (STEM) images. The method combines statistical parameter estimation and model-order selection using a Bayesian framework and has been shown to be especially useful for the analysis of the structure of beam-sensitive nanomaterials. In order to avoid beam damage, images of such materials are usually acquired using a limited incoming electron dose resulting in a low contrast-to-noise ratio (CNR) which makes visual inspection unreliable. This creates a need for an objective and quantitative approach. The present paper describes the methodology of the MAP probability rule, gives its step-by-step derivation and discusses its algorithmic implementation for atom column detection. In addition, simulation results are presented showing that the performance of the MAP probability rule to detect the correct number of atomic columns from HAADF STEM images is superior to that of other model-order selection criteria, including the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC). Moreover, the MAP probability rule is used as a tool to evaluate the relation between STEM image quality measures and atom detectability resulting in the introduction of the so-called integrated CNR (ICNR) as a new image quality measure that better correlates with atom detectability than conventional measures such as signal-to-noise ratio (SNR) and CNR.},
	keywords = {Atom detectability, Atom detection, Model selection, Scanning transmission electron microscopy (STEM)},
	issn = {0304-3991},
	doi = {https://doi.org/10.1016/j.ultramic.2019.02.003},
	url = {http://www.sciencedirect.com/science/article/pii/S0304399118304236},
	author = {J Fatermans and Sandra Van Aert and Arnold Jan den Dekker}
}
@conference {1959,
	title = {Quantifying 3D atomic structures of nanomaterials and their dynamics using model-based scanning transmission electron microscopy},
	year = {2019},
	author = {Sandra Van Aert and De wael, A and J Fatermans and Ivan Lobato and Annick De Backer and Jones, L and Arnold Jan den Dekker and Peter D Nellist}
}
@mastersthesis {1976,
	title = {Quantitative atom detection from atomic-resolution transmission electron microscopy images},
	volume = {Doctor of Science/Physics},
	year = {2019},
	type = {PhD thesis},
	author = {J Fatermans}
}
@conference {1958,
	title = {Strategies for quantifying 3D atomic structures of nanomaterials and their dynamics using dose-efficient ADF STEM},
	year = {2019},
	author = {Sandra Van Aert and De wael, A and J Fatermans and Ivan Lobato and Annick De Backer and Jones, L and Arnold Jan den Dekker and Peter D Nellist}
}
@conference {1890,
	title = {Bayesian analysis of noisy scanning transmission electron microscopy images for single atom detection},
	year = {2018},
	pages = {95},
	author = {J Fatermans and Arnold Jan den Dekker and M{\"u}ller-Caspary, K and Ivan Lobato and Sandra Van Aert}
}
@conference {1957,
	title = {Maximising dose efficiency in quantitative STEM to reveal the 3D atomic structure of nanomaterials},
	year = {2018},
	author = {Sandra Van Aert and J Fatermans and Annick De Backer and van den Bos, K. H. W. and O{\textquoteright}Leary, C M. and M{\"u}ller-Caspary, K and Jones, L and Ivan Lobato and B{\'e}ch{\'e}, A and Arnold Jan den Dekker and Sara Bals and Peter D Nellist}
}
@conference {1891,
	title = {The maximum a posteriori probability rule to detect single atoms from low signal-to-noise ratio scanning transmission electron microscopy images},
	year = {2018},
	author = {J Fatermans and Arnold Jan den Dekker and M{\"u}ller-Caspary, K and Ivan Lobato and Sandra Van Aert}
}
@article {1878,
	title = {Single Atom Detection from Low Contrast-to-Noise Ratio Electron Microscopy Images},
	journal = {Phys. Rev. Lett.},
	volume = {121},
	year = {2018},
	month = {Jul},
	pages = {056101},
	abstract = {Single atom detection is of key importance to solving a wide range of scientific and technological problems. The strong interaction of electrons with matter makes transmission electron microscopy one of the most promising techniques. In particular, aberration correction using scanning transmission electron microscopy has made a significant step forward toward detecting single atoms. However, to overcome radiation damage, related to the use of high-energy electrons, the incoming electron dose should be kept low enough. This results in images exhibiting a low signal-to-noise ratio and extremely weak contrast, especially for light-element nanomaterials. To overcome this problem, a combination of physics-based model fitting and the use of a model-order selection method is proposed, enabling one to detect single atoms with high reliability.},
	doi = {10.1103/PhysRevLett.121.056101},
	url = {https://link.aps.org/doi/10.1103/PhysRevLett.121.056101},
	author = {J Fatermans and Arnold Jan den Dekker and M{\"u}ller-Caspary, K. and Ivan Lobato and O{\textquoteright}Leary, C. M. and Peter D Nellist and Sandra Van Aert}
}
@conference {1889,
	title = {Detection of atomic columns from noisy STEM images},
	year = {2017},
	pages = {445-446},
	author = {J Fatermans and M{\"u}ller-Caspary, K and Arnold Jan den Dekker and Sandra Van Aert}
}
@conference {1888,
	title = {Bayesian model-order selection in electron microscopy to detect atomic columns in noisy images},
	year = {2016},
	pages = {53},
	author = {J Fatermans and Sandra Van Aert and Arnold Jan den Dekker}
}
