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Autocalibration method for scanning electron microscope using affine camera model

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Abstract

This paper deals with the task of autocalibration of scanning electron microscope (SEM), which is a technique allowing to compute camera motion and intrinsic parameters. In contrast to classical calibration, which implies the use of a calibration object and is known to be a tedious and rigid operation, auto- or selfcalibration is performed directly on the images acquired for the visual task. As autocalibration represents an optimization problem, all the steps contributing to the success of the algorithm are presented: formulation of the cost function incorporating metric constraints, definition of bounds, regularization, and optimization algorithm. The presented method allows full estimation of camera matrices for all views in the sequence. It was validated on virtual images as well as on real SEM images (pollen grains, cutting tools, etc.). The results show a good convergence range and low execution time, notably compared to classical methods, and even more in the context of the calibration of SEM.

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Funding

This work has been supported by the EIPHI Graduate School (contract “ANR-17-EURE-0002”), the Equipex ROBOTEX project (contract “ANR-10-EQPX-44-01”), the ISITE-BFC project (contract “ANR-15-IDEX-03”) and the Interreg France-Suisse.

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Authors and Affiliations

  1. FEMTO-ST, AS2M, Université Bourgogne Franche-Comté, Université de Franche-Comté/CNRS/ENSMM, Besançon, France

    Andrey V. Kudryavtsev, Valérian Guelpa, Patrick Rougeot, Olivier Lehmann, Sounkalo Dembélé & Nadine Le Fort-Piat

  2. LJK, Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP, Grenoble, France

    Peter Sturm

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  1. Andrey V. Kudryavtsev
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  2. Valérian Guelpa
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  3. Patrick Rougeot
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  4. Olivier Lehmann
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  5. Sounkalo Dembélé
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  6. Peter Sturm
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  7. Nadine Le Fort-Piat
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Correspondence to Valérian Guelpa.

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Kudryavtsev, A.V., Guelpa, V., Rougeot, P. et al. Autocalibration method for scanning electron microscope using affine camera model. Machine Vision and Applications 31, 69 (2020). https://doi.org/10.1007/s00138-020-01109-x

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  • DOI: https://doi.org/10.1007/s00138-020-01109-x

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