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Dive into the research topics where Maria E. Rudnaya is active.

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Featured researches published by Maria E. Rudnaya.


Ultramicroscopy | 2011

Defocus and twofold astigmatism correction in HAADF-STEM

Maria E. Rudnaya; W. Van den Broek; R.M.P. Doornbos; R.M.M. Mattheij; J.M.L. Maubach

A new simultaneous autofocus and twofold astigmatism correction method is proposed for High Angle Annular Dark Field Scanning Transmission Electron Microscopy (HAADF-STEM). The method makes use of a modification of image variance, which has already been used before as an image quality measure for different types of microscopy, but its use is often justified on heuristic grounds. In this paper we show numerically that the variance reaches its maximum at Scherzer defocus and zero astigmatism. In order to find this maximum a simultaneous optimization of three parameters (focus, x- and y-stigmators) is necessary. This is implemented and tested on a FEI Tecnai F20. It successfully finds the optimal defocus and astigmatism with time and accuracy, compared to a human operator.


Microscopy and Microanalysis | 2009

Iterative autofocus algorithms for scanning electron microscopy

Maria E. Rudnaya; R.M.M. Mattheij; J.M.L. Maubach

Introduction. A robust and reliable autofocus algorithm is important concern for the automation of a Scanning Electron Microscope (SEM). Comparison of existing autofocus techniques has been done for specific specimen for fluorescence [1] and non-fluorescence microscopy [2-3]. For Scanning Transmission Electron Microscopy some of available algorithms were compared [4]. To the authors’ knowledge broad evolution has not been published yet for SEM.


Journal of Mathematical Imaging and Vision | 2012

A Derivative-Based Fast Autofocus Method in Electron Microscopy

Maria E. Rudnaya; Hg Hennie ter Morsche; J.M.L. Maubach; Robert M. M. Mattheij

Most automatic focusing methods are based on a sharpness function, which delivers a real-valued estimate of an image quality. In this paper, we study an L2-norm derivative-based sharpness function, which has been used before based on heuristic consideration. We give a more solid mathematical foundation for this function and get a better insight into its analytical properties. Moreover an efficient autofocus method is presented, in which an artificial blur variable plays an important role.We show that for a specific choice of the artificial blur control variable, the function is approximately a quadratic polynomial, which implies that after the recording of at least three images one can find the approximate position of the optimal defocus. This provides the speed improvement in comparison with existing approaches, which usually require recording of more than ten images for autofocus. The new autofocus method is employed for the scanning transmission electron microscopy. To be more specific, it has been implemented in the FEI scanning transmission electron microscope and its performance has been tested as a part of a particle analysis application.


world congress on engineering | 2011

Gradient-based sharpness function

Maria E. Rudnaya; R.M.M. Mattheij; J.M.L. Maubach; H.G. ter Morsche


IAENG International Journal of Computer Science | 2011

Sharpness functions for computational aesthetics and image sublimation

Maria E. Rudnaya; Robert Ochshorn


Nonlinear Analysis-real World Applications | 2014

A note on analysis and numerics of algae growth

Kundan Kumar; Maxim Pisarenco; Maria E. Rudnaya; Valeriu Savcenco


CASA-report | 2011

A derivative-based fast autofocus method

Maria E. Rudnaya; H.G. ter Morsche; J.M.L. Maubach; Robert M. M. Mattheij


Engineering Fracture Mechanics | 2009

Scanning electron microscopy : power spectrum analysis

Maria E. Rudnaya; Jml Jos Maubach


Microscopy and Microanalysis | 2011

A New Method for Defocus and Astigmatism Correction in Electron Microscopy

Maria E. Rudnaya; W. Van den Broek; R.M.P. Doornbos; Sc Kho; R.M.M. Mattheij; Joseph Maubach


Higher Education | 2011

Automated focusing and astigmatism correction in electron microscopy

Maria E. Rudnaya

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J.M.L. Maubach

Eindhoven University of Technology

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Maxim Pisarenco

Eindhoven University of Technology

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Rmm Mattheij

Eindhoven University of Technology

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Valeriu Savcenco

Eindhoven University of Technology

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R.M.M. Mattheij

Eindhoven University of Technology

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Robert M. M. Mattheij

Eindhoven University of Technology

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Sc Kho

Eindhoven University of Technology

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