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Evaluating Fiber Detection Models Using Neural Networks

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Advances in Visual Computing (ISVC 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11845))

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Abstract

Ceramic matrix composites are resistant materials that withstand high temperatures, but quality control of such composites depends on microtomography image analysis to enable the spatial analysis of fibers, matrix cracks detection, among others. While there are several approaches for fiber detection from microtomography, materials scientists lack computational schemes to validate the accuracy of different fiber detection models. This paper proposes a set of statistical methods to analyse images of CMC in 3D and visualize respective fiber beds, including a lossless data reduction algorithm. The main contribution is our method based on a convolutional neural network that enables evaluation of results from automated fiber detection models, particularly when compared with human curated datasets. We build all the algorithms using free tools to allow full reproducibility of the experiments, and illustrate our results using algorithms designed to probe sample content from gigabyte-size image volumes with minimalistic computational infrastructure.

Both data and computer codes will be available upon acceptance of this paper.

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Notes

  1. 1.

    Filename: “rec20160318_191511_232p3_2cm_cont_4097im_1500ms_ML17keV_6ḣ5” as available in the Materials Facility website [3].

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Acknowledgments

This research is funded in part by the Gordon and Betty Moore Foundation through Grant GBMF3834 and by the Alfred P. Sloan Foundation through Grant 2013-10-27 to the University of California, Berkeley. Algorithmic work is partially supported by the Office of Science of the US Department of Energy (DOE) under Contract No. DE-AC02-05CH11231, Advanced Scientific Computing Research (ASCR) Early Career Award. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and they do not necessarily reflect the views of DOE or the University of California.

Author information

Authors and Affiliations

  1. University of California, Berkeley, CA, USA

    Silvia Miramontes & Daniela M. Ushizima

  2. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Silvia Miramontes, Daniela M. Ushizima & Dilworth Y. Parkinson

Authors
  1. Silvia Miramontes
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  2. Daniela M. Ushizima
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  3. Dilworth Y. Parkinson
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Corresponding authors

Correspondence to Silvia Miramontes , Daniela M. Ushizima or Dilworth Y. Parkinson .

Editor information

Editors and Affiliations

  1. University of Nevada, Reno, NV, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, CA, USA

    Richard Boyle

  3. University of Nevada, Reno, NV, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, NV, USA

    Darko Koracin

  5. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Daniela Ushizima

  6. Latent AI, Palo Alto, CA, USA

    Sek Chai

  7. Texas A&M University, College Station, TX, USA

    Shinjiro Sueda

  8. Louisiana State University, Baton Rouge, LA, USA

    Xin Lin

  9. University of North Carolina at Charlotte, Charlotte, NC, USA

    Aidong Lu

  10. École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

    Daniel Thalmann

  11. Notre Dame University, Notre Dame, IN, USA

    Chaoli Wang

  12. Bosch Research North America, Palo Alto, CA, USA

    Panpan Xu

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© 2019 This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply

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Miramontes, S., Ushizima, D.M., Parkinson, D.Y. (2019). Evaluating Fiber Detection Models Using Neural Networks. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2019. Lecture Notes in Computer Science(), vol 11845. Springer, Cham. https://doi.org/10.1007/978-3-030-33723-0_44

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  • DOI: https://doi.org/10.1007/978-3-030-33723-0_44

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-33722-3

  • Online ISBN: 978-3-030-33723-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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