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Correspondence matchingin 3D models for 3D hand fitting

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Warianty tytułu
PL
Dopasowanie zgodności w modelach 3D dla dopasowania dłoni 3D
Języki publikacji
EN
Abstrakty
EN
Upper limb prosthetic is an area of medical research and development that aims to restore functionality and improve the quality of life of people affected by the loss of one or both upper limbs. The development and implementation of 3D scanning tools and analysis of 3D scanning data requires the use of specialized analysis methods that ensure the achievement of the required indicators. It should take into account the impact of the model resolution on the result. This paper is devoted to the analysis of finding matches between a point cloud of a hand model and another point cloud using Gromov Wasserstein distance. For analysis, a subset of the MANO dataset was employed, containing a substantial volume of data and serving as a representative sample of the human population. The results obtained indicate the possibility of using this approach in the processing and analysis of three-dimensional data, which serves as one of the stages of designing individualized prostheses.
PL
Protetyka kończyn górnych to dziedzina badań i rozwoju medycznego mająca na celu przywrócenie funkcjonalności i poprawę jakości życia osób dotkniętych utratą jednej lub obu kończyn górnych. Opracowanie i wdrożenie narzędzi do skanowania 3D oraz analiza danych pochodzących ze skanowania 3D wymaga zastosowania specjalistycznych metod analizy, które zapewnią osiągnięcie wymaganych wskaźników. Należy przy tym uwzględnić wpływ rozdzielczości modelu na uzyskany wynik. Niniejszy artykuł poświęcony jest analizie znajdowania dopasowań między chmurą punktów modelu dłoni a inną chmurą punktów przy użyciu odległości Gromova-Wassersteina. Do analizy wykorzystano podzbiór zbioru danych MANO, który zawiera znaczną ilość danych i służy jako reprezentatywna próbka populacji ludzkiej. Uzyskane wyniki wskazują na możliwość wykorzystania tego podejścia w przetwarzaniu i analizie danych trójwymiarowych, które służą jako jeden z etapów projektowania zindywidualizowanych protez.
Rocznik
Strony
78--82
Opis fizyczny
Bibliogr. 27 poz., rys., tab., wykr.
Twórcy
  • Kharkiv National University of Radio Electronics, Kharkiv, Ukraine
autor
  • Kharkiv National University of Radio Electronics, Kharkiv, Ukraine
  • Kharkiv National University of Radio Electronics, Kharkiv, Ukraine
  • Vinnytsia National Technical University, Vinnytsia, Ukraine
  • Vinnytsia Pyrohov National Medical University, Vinnytsia, Ukraine
  • D.Serikbayev East Kazakhstan State Technical University, Ust-Kamenogorsk, Kazakhstan
Bibliografia
  • [1] Avrunin O. G. et al.: Application of 3D printing technologies in building patient-specific training systems for computing planning in rhinology. Proceedings of the International Scientific Internet Conference on Computer Graphics and Image Processing and 48th International Scientific and Practical Conference on Application of Lasers in Medicine and Biology, 2019, 1 [https://doi.org/10.1201/9780429057618-1].
  • [2] Boleneus G. J. et al.: Top-down design enables flexible design of prosthetic forearms and hands. ASEE Annual Conference and Exposition, Conference Proceedings, 2019.
  • [3] Cignoni P. et al.: MeshLab: an Open-Source Mesh Processing Tool. Sixth Eurographics Italian Chapter Conference, 2008, 129–136.
  • [4] Garland M., Heckbert P. S.: Simplifying surfaces with color and texture using quadric error metrics. Proceedings Visualization 98, 2000.
  • [5] Guidi G., Gonizzi S., Micoli L.: 3D capturing performances of low-cost range sensors for mass-market applications. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 41, 2016, 33–40 [https:// doi.org/10.5194/isprsarchives-XLI-B5-33-2016].
  • [6] Kim Y. et al.: Dynamic elasticity measurement for prosthetic socket design. International Conference on Rehabilitation Robotics – ICORR, London, UK, 2017, 1281–1286 [https://doi.org/10.1109/ICORR.2017.8009425].
  • [7] Neri P. et al.: Semi-automatic Point Clouds Registration for Upper Limb Anatomy. International Joint Conference on Mechanics, Design Engineering and Advanced Manufacturing – JCM, 2023, 733–742 [https://doi.org/10.1007/978-3-031-15928-2_64].
  • [8] Neri P. et al.: 3D scanning of Upper Limb anatomy by a depth-camera-based system. International Journal on Interactive Design and Manufacturing, 2023 [https://10.1007/s12008-023-01248-1].
  • [9] Olsen J. et al.: 3D-Printing and Upper-Limb Prosthetic Sockets: Promises and Pitfalls. IEEE Transactions on Neural Systems and Rehabilitation Engineering 29, 2021, 527–535 [https://doi.org/10.1109/tnsre.2021.3057984].
  • [10] Pavlov S. V. et al.: Photoplethysmohrafic technologies of the cardiovascular control. Universum-Vinnitsa, Vinnitsa, 2007.
  • [11] Pavlov S. V. et al.: A simulation model of distribution of optical radiation in biological tissues. Visnyk VNTU 3, 2011, 191–195.
  • [12] Pavlov S. V. et al.: Laser photoplethysmography in integrated evaluation of collateral circulation of lower extremities. Proc. SPIE 8698, 2012, 869808.
  • [13] Peyré G., Cuturi M., Solomon J.: Gromov-Wasserstein averaging of kernel and distance matrices. International Conference on Machine Learning (ICML), 2016.
  • [14] Román-Casares A. M., García-Gómez O., Guerado E.: Prosthetic Limb Design and Function: Latest Innovations and Functional Results. Current Trauma Reports 4(4), 2018, 256–262 [https://doi.org/10.1007/s40719-018-0150-2].
  • [15] Romero J., Tzionas D., Black M. J.: Embodied hands: Modeling and capturing hands and bodies together. ACM Transactions on Graphics 36(6), 2017, 245 [https://doi.org/10.1145/3130800.3130883].
  • [16] Ryniewicz A. et al.: The use of laser scanning in the procedures replacing lower limbs with prosthesis. Measurement 112, 2017, 9–15.
  • [17] Selivanova K. G. et al.: 3D visualization of human body internal structures surface during stereo-endoscopic operations using computer vision techniques. Przeglad Elektrotechniczny [https://doi.org/10.15199/48.2021.09.06]. 9, 2021, 30–33
  • [18] Serkova V. et al.: Medical expert system for assessment of coronary heart disease destabilization based on the analysis of the level of soluble vascular adhesion molecules. Proc. SPIE 10445, 2017, 104453O.
  • [19] Sokol Y. et al.: Using medical imaging in disaster medicine. Proceedings of IEEE 4th International Conference on Intelligent Energy and Power Systems – IEPS 2020, 287–290 [https://doi.org/10.1109/IEPS51250.2020.9263175].
  • [20] Tymkovych M. et al.: Ice crystals microscopic images segmentation based on active contours. 2019 IEEE 39th International Conference on Electronics and Nanotechnology – ELNANO 2019, 493–496 [https://doi.org/10.1109/ELNANO.2019.8783332].
  • [21] Tymkovych M. et al.: Detection of Chest Deviation During Breathing Using a Depth Camera. Proceedings of IEEE 8th International Conference on Problems of Infocommunications, Science and Technology – PIC S and T, 85 [https://doi.org/10.1109/PICST54195.2021.9772111].
  • [22] Tymkovych M. et al.: Application of SOFA Framework for Physics-Based Simulation of Deformable Human Anatomy of Nasal Cavity. Proceedings of IFMBE, 2021, 112 [https://doi.org/10.1007/978-3-030-64610-3_14].
  • [23] Tymkovych M. et al.: Application of Artificial Neural Networks for Analysis of Ice Recrystallization Process for Cryopreservation. Proceedings of IFMBE, 2021, 102 [https://doi.org/10.1007/978-3-030-64610-3_13].
  • [24] Wojcik W. et al.: ECTL application for carbon monoxide measurements. Proc. of SPIE 5958, 2005, 595837.
  • [25] Xu H. et al.: Gromov-wasserstein learning for graph matching and node embedding. International Conference on Machine Learning – ICML, 2019.
  • [26] Zabolotna N. et al.: Diagnostic efficiency of Mueller-matrix polarization reconstruction system of the phase structure of liver tissue, Proc. SPIE 9816, 2015, 98161E [https://doi.org/10.1117/12.2229018].
  • [27] Zanuttigh P. et al.: Time-of-Flight and Structured Light Depth Cameras Technology and Applications. Springer, 2016.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-a6708ebc-7e3f-4867-8a25-25948622cc3d
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