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Unsupervised and Accurate Extraction of Primitive Unit Cells from Crystal Images

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Pattern Recognition (DAGM 2015)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9358))

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Abstract

We present a novel method for the unsupervised estimation of a primitive unit cell, i.e. a unit cell that can’t be further simplified, from a crystal image. Significant peaks of the projective standard deviations of the image serve as candidate lattice vector angles. Corresponding fundamental periods are determined by clustering local minima of a periodicity energy. Robust unsupervised selection of the number of clusters is obtained from the likelihoods of multi-variance cluster models induced by the Akaike information criterion. Initial estimates for lattice angles and periods obtained in this manner are refined jointly using non-linear optimization. Results on both synthetic and experimental images show that the method is able to estimate complex primitive unit cells with sub-pixel accuracy, despite high levels of noise.

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Acknowledgments

The authors would like to thank P.M. Voyles for providing experimental STEM images.

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Authors and Affiliations

  1. AICES Graduate School, RWTH Aachen University, Aachen, Germany

    Niklas Mevenkamp & Benjamin Berkels

Authors
  1. Niklas Mevenkamp
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  2. Benjamin Berkels
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Corresponding author

Correspondence to Niklas Mevenkamp .

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Editors and Affiliations

  1. Institute of Computer Science III, University of Bonn, Bonn, Germany

    Juergen Gall

  2. MPI for Intelligent Systems, University of TĂĽbingen, TĂĽbingen, Germany

    Peter Gehler

  3. Computer Vision Group, Visual Computing Institute, RWTH Aachen, Aachen, Germany

    Bastian Leibe

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Mevenkamp, N., Berkels, B. (2015). Unsupervised and Accurate Extraction of Primitive Unit Cells from Crystal Images. In: Gall, J., Gehler, P., Leibe, B. (eds) Pattern Recognition. DAGM 2015. Lecture Notes in Computer Science(), vol 9358. Springer, Cham. https://doi.org/10.1007/978-3-319-24947-6_9

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  • DOI: https://doi.org/10.1007/978-3-319-24947-6_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24946-9

  • Online ISBN: 978-3-319-24947-6

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