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View Dependent Surface Material Recognition

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Advances in Visual Computing (ISVC 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11844))

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Abstract

The paper presents a detailed study of surface material recognition dependence on the illumination and viewing conditions which is a hard challenge in a realistic scene interpretation. The results document sharp classification accuracy decrease when using usual texture recognition approach, i.e., small learning set size and the vertical viewing and illumination angle which is a very inadequate representation of the enormous material appearance variability. The visual appearance of materials is considered in the state-of-the-art Bidirectional Texture Function (BTF) representation and measured using the upper-end BTF gonioreflectometer. The materials in this study are sixty-five different wood species. The supervised material recognition uses the shallow convolutional neural network (CNN) for the error analysis of angular dependency. We propose a Gaussian mixture model-based method for robust material segmentation.

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Acknowledgments

The Czech Science Foundation project GAČR 19-12340S supported this research.

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Authors and Affiliations

  1. The Institute of Information Theory and Automation of the Czech Academy of Sciences, Prague, Czechia

    Stanislav Mikeš & Michal Haindl

Authors
  1. Stanislav Mikeš
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  2. Michal Haindl
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Corresponding author

Correspondence to Michal Haindl .

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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Mikeš, S., Haindl, M. (2019). View Dependent Surface Material Recognition. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2019. Lecture Notes in Computer Science(), vol 11844. Springer, Cham. https://doi.org/10.1007/978-3-030-33720-9_12

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  • DOI: https://doi.org/10.1007/978-3-030-33720-9_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-33719-3

  • Online ISBN: 978-3-030-33720-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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