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Journal of Prosthetic Dentistry

Comments on “Jaw tracking integration to the virtual patient: A 4D dynamic approach”

Published:November 21, 2022DOI:https://doi.org/10.1016/j.prosdent.2022.08.038
      We have read with great interest the article entitled “Jaw tracking integration to the virtual patient: A 4D dynamic approach” by Zambrana et al
      • Zambrana N.
      • Sesma N.
      • Fomenko I.
      • Dakir E.I.
      • Pieralli S.
      Jaw tracking integration to the virtual patient: a 4D dynamic approach.
      recently published in the Journal of Prosthetic Dentistry. This dental technique article is of significance because it introduces a novel cost-effective workflow for kinematic jaw tracking in digital dentistry that could have great implications for fixed prosthodontics and implant dentistry. The aim of this commentary was to discuss the current limitations in computer vision that are hindering the technique.
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      References

        • Zambrana N.
        • Sesma N.
        • Fomenko I.
        • Dakir E.I.
        • Pieralli S.
        Jaw tracking integration to the virtual patient: a 4D dynamic approach.
        J Prosthet Dent. 16 March 2022; ([Epub ahead of print])https://doi.org/10.1016/j.prosdent.2022.02.011
        • OpenCV Team
        About OpenCV. 2022.
        https://opencv.org/about/
        Date accessed: August 10, 2022
        • Bradski G.
        The openCV library.
        Dr Dobb's J Softw Tools Prof Program. 2000; 25: 120-123
        • Oscadal P.
        • Heczko D.
        • Vysocky A.
        • et al.
        Improved pose estimation of Aruco tags using a novel 3D placement strategy.
        Sensors (Basel). 2020; 20: 4825
        • Adil E.
        • Mikou M.
        • Mouhsen A.
        A novel algorithm for distance measurement using stereo camera.
        CAAI Trans Intell Technol. 2022; 7: 177-186
        • Sarmadi H.
        • Muñoz-Salinas R.
        • Berbís M.A.
        • Medina-Carnicer R.
        Simultaneous multi-view camera pose estimation and object tracking with square planar markers.
        arXiv. 2021; https://doi.org/10.48550/arXiv.2103.09141
        • López-Cerón A.
        • Cañas J.M.
        Accuracy analysis of marker-based 3D visual localization.
        Actas de las XXXVII Jornadas de Automática. 2016; 37: 1124-1131
        • Pentenrieder K.
        • Meier P.
        • Klinker G.
        Analysis of tracking accuracy for single-camera square-marker-based tracking.
        in: Dritter Workshop Virtuelle und Erweiterte Realitt der GIFachgruppe VR/AR. Metaio GmbH, Koblenz2006: 1-15
        • Bradski G.
        • Kaehler A.
        Learning OpenCV: computer vision with the OpenCV library.
        O'Reilly, Sebastopol2008: 580