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Artificial Immune Systems Approach for Surface Reconstruction of Shapes with Large Smooth Bumps

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Computational Science – ICCS 2023 (ICCS 2023)

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Abstract

Reverse engineering is one of the classical approaches for quailty assessment in industrial manufacturing. A key technology in reverse engineering is surface reconstruction, which aims at obtaining a digital model of a physical object from a cloud of 3D data points obtained by scanning the object. In this paper we address the surface reconstruction problem for surfaces that can exhibit large smooth bumps. To account for this type of features, our approach is based on using exponentials of polynomial functions in two variables as the approximating functions. In particular, we consider three different models, given by bivariate distributions obtained by combining a normal univariate distribution with a normal, Gamma, and Weibull distribution, respectively. The resulting surfaces depend on some parameters whose values have to be optimized. This yields a difficult nonlinear continuous optimization problem solved through an artificial immune systems approach based on the clonal selection theory. The performance of the method is discussed through its application to a benchmark comprised of three examples of point clouds.

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Acknowledgment

Akemi Gálvez, Lihua You and Andrés Iglesias thank the financial support from the project PDE-GIR of the European Union’s Horizon 2020 research and innovation programme, in the Marie Sklodowska-Curie Actions programme, with grant agreement of reference number 778035, and also from the Agencia Estatal de Investigación (AEI) of the Spanish Ministry of Science and Innovation (Computer Science National Program), for the grant of reference number PID2021-127073OB-I00 of the MCIN/AEI/10.13039/501100011033/FEDER, EU. Iztok Fister Jr. thanks the Slovenian Research Agency for the financial support under Research Core Funding No. P2-0057. Iztok Fister thanks the Slovenian Research Agency for the financial support under Research Core Funding No. P2-0042 - Digital twin.

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Authors and Affiliations

  1. Department of Applied Mathematics and Computational Sciences, University of Cantabria, 39005, Santander, Spain

    Akemi Gálvez & Andrés Iglesias

  2. Faculty of Pharmaceutical Sciences, Toho University, 2-2-1 Miyama, Funabashi, 274-8510, Japan

    Akemi Gálvez & Andrés Iglesias

  3. Faculty of Electrical Engineering and Computer Science, University of Maribor, Maribor, Slovenia

    Iztok Fister Jr. & Iztok Fister

  4. National Center for Computer Animation, Faculty of Media and Communication, Bournemouth University, Poole, BH12 5BB, UK

    Lihua You

Authors
  1. Akemi Gálvez
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  2. Iztok Fister Jr.
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  3. Lihua You
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  4. Iztok Fister
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  5. Andrés Iglesias
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Corresponding author

Correspondence to Andrés Iglesias .

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Editors and Affiliations

  1. Czech Technical University in Prague, Prague, Czech Republic

    Jiří Mikyška

  2. University of Amsterdam, Amsterdam, The Netherlands

    Clélia de Mulatier

  3. AGH University of Science and Technology, Krakow, Poland

    Maciej Paszynski

  4. University of Amsterdam, Amsterdam, The Netherlands

    Valeria V. Krzhizhanovskaya

  5. University of Tennessee at Knoxville, Knoxville, TN, USA

    Jack J. Dongarra

  6. University of Amsterdam, Amsterdam, The Netherlands

    Peter M.A. Sloot

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Gálvez, A., Fister, I., You, L., Fister, I., Iglesias, A. (2023). Artificial Immune Systems Approach for Surface Reconstruction of Shapes with Large Smooth Bumps. In: Mikyška, J., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M. (eds) Computational Science – ICCS 2023. ICCS 2023. Lecture Notes in Computer Science, vol 10476. Springer, Cham. https://doi.org/10.1007/978-3-031-36027-5_22

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  • DOI: https://doi.org/10.1007/978-3-031-36027-5_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-36026-8

  • Online ISBN: 978-3-031-36027-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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