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    Geometric analysis of shape variability of lower jaws of prehistoric humans Analyse géométrique de la variabilité de la forme des mâchoires inférieures des humains préhistoriques

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    Name:
    geometric analysis.pdf
    Size:
    24.54Mb
    Format:
    PDF
    Description:
    Accepted manuscript
    Embargo End Date:
    2021-12-13
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    Type
    Article
    Authors
    Ren, Jing cc
    Wonka, Peter cc
    Harihara, Gowtham
    Ovsjanikov, Maks
    KAUST Department
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Visual Computing Center (VCC)
    KAUST Grant Number
    CRG-2017-3426
    Date
    2020-12
    Embargo End Date
    2021-12-13
    Permanent link to this record
    http://hdl.handle.net/10754/666361
    
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    Abstract
    In this document we describe our method and the results obtained for comparing jaws of prehistoric humans. Our main goal was twofold: (1) establish a methodology for comparing the structure of 3D shapes of scans of jaws using geometric data analysis techniques, and (2) use this methodology for comparing and clustering individual objects according to their geometric similarity. Moreover, we also applied geometric modeling techniques to establish a “clean” version of the dataset, without significant artefacts present in the original data, such as large missing parts. We then applied our analysis techniques both on the original and the clean dataset in order to validate our comparison results. For our core task of shape comparison, we used recent state-of-the-art shape matching methods and we present similarity results using different comparison metrics.
    Citation
    Ren, J., Wonka, P., Harihara, G., & Ovsjanikov, M. (2020). Geometric analysis of shape variability of lower jaws of prehistoric humans. L’Anthropologie, 102808. doi:10.1016/j.anthro.2020.102808
    Sponsors
    Parts of this work were supported by the KAUST OSR Award No. CRG-2017-3426, the ERC Starting Grant No. 758800 (EXPROTEA) and the ANR AI Chair AIGRETTE.
    Publisher
    Elsevier BV
    Journal
    Anthropologie (France)
    DOI
    10.1016/j.anthro.2020.102808
    Additional Links
    https://linkinghub.elsevier.com/retrieve/pii/S0003552120301035
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.anthro.2020.102808
    Scopus Count
    Collections
    Articles; Computer Science Program; Visual Computing Center (VCC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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