Εμφανίζονται 1 - 20 Αποτελέσματα από 74 για την αναζήτηση '"трехмерная реконструкция"', χρόνος αναζήτησης: 0,59δλ Περιορισμός αποτελεσμάτων
  1. 1
  2. 2
  3. 3
    Academic Journal

    Πηγή: Urology Herald; Том 9, № 3 (2021); 19-24 ; Вестник урологии; Том 9, № 3 (2021); 19-24 ; 2308-6424 ; 10.21886/2308-6424-2021-9-3

    Περιγραφή αρχείου: application/pdf

    Relation: https://www.urovest.ru/jour/article/view/468/335; Brisbane W, Bailey MR, Sorensen MD. An overview of kidney stone imaging techniques. Nat Rev Urol. 2016;13(11):654-62. DOI:10.1038/nrurol.2016.154; Rudnick MR, Leonberg-Yoo AK, Litt HI, Cohen RM, hilton S, Reese PP. The Controversy of contrast-Induced nephropathy with Intravenous contrast: What Is the risk? Am J Kidney Dis. 2020;75(1):105-13. DOI:10.1053/j.ajkd.2019.05.022; Shimizu A, Ohno R, Ikegami T, Kobatake H, Nawano S, Smutek D. Segmentation of multiple organs in non-contrast 3D abdominal CT images. Int J CARS. 2007;2:135-42. DOI 10.1007/s11548-007-0135-z; Parkhomenko E, O’Leary M, Safiullah S, Walia S, Owyong M, Lin C, James R, Okhunov Z, Patel RM, Kaler KS, Landman J, Clayman R. Pilot assessment of Immersive virtual reality Renal Models as an Educational and Preoperative Planning Tool for Percutaneous Nephrolithotomy. J Endourol. 2019;33(4):283-8. DOI:10.1089/end.2018.0626; Sung JM, Jefferson FA, Tapiero S, Patel RM, Owyong M, Xie L, Karani R, Ghamarian P, Lall C, Clayman RV, Landman J. evaluation of a diuresis enhanced non-contrast computed tomography for kidney stones protocol to maximize Collecting System Distention. J Endourol. 2020;34(3):255-61. DOI:10.1089/end.2019.0719; Türk C, Petřík A, Sarica K, Seitz C, Skolarikos A, Straub M, Knoll T. EAU guidelines on Interventional treatment for urolithiasis. Eur Urol. 2016;69(3):475-82. DOI:10.1016/j.eururo.2015.07.041; Shahzad R, Bos D, Budde RP, Pellikaan K, Niessen WJ, van der Lugt A, van Walsum T. Automatic segmentation and quantification of the cardiac structures from non-contrast-enhanced cardiac CT scans. Phys Med Biol. 2017;62(9):3798-813. DOI:10.1088/1361-6560/aa63cb; Sedghi Gamechi Z, Bons LR, Giordano M, Bos D, Budde RPJ, Kofoed KF, Pedersen JH, Roos-Hesselink JW, de Bruijne M. Automated 3D segmentation and diameter measurement of the thoracic aorta on non-contrast enhanced CT. Eur Radiol. 2019;29(9):4613-23. DOI:10.1007/s00330-018-5931-z; Patel A, Schreuder FHBM, Klijn CJM, Prokop M, Ginneken BV, Marquering HA, Roos YBWEM, Baharoglu MI, Meijer FJA, Manniesing R. Intracerebral Haemorrhage Segmentation in Non-Contrast CT. Sci Rep. 2019;9(1):17858. DOI:10.1038/s41598-019-54491-6; Khalifa F, Elnakib A, Beache GM, Gimel’farb G, El-Ghar MA, Ouseph R, Sokhadze G, Manning S, McClure P, El-Baz A. 3D kidney segmentation from CT images using a level set approach guided by a novel stochastic speed function. Med Image Comput Assist Interv. 2011;14(3):587-94. DOI:10.1007/978-3-642-23626-6_72; https://www.urovest.ru/jour/article/view/468

  4. 4
  5. 5
    Academic Journal

    Πηγή: Informatics; Том 17, № 1 (2020); 18-28 ; Информатика; Том 17, № 1 (2020); 18-28 ; 2617-6963 ; 1816-0301

    Περιγραφή αρχείου: application/pdf

    Relation: https://inf.grid.by/jour/article/view/1061/933; Usefulness of the 3D virtual visualization surgical planning simulation and 3D model for endoscopic endonasal transsphenoidal surgery of pituitary adenoma: technical report and review of literature / A. Shinomiya [et al.] // Interdisciplinary Neurosurgery. – 2018. – Vol. 13. – P. 13–19. https://doi.org/10.1016/j.inat. 2018.02.002; Gatys, L. A. Texture synthesis using convolutional neural networks / L. A. Gatys, A. S. Ecker, M. Bethge // Advances in Neural Information Processing Systems. – 2015. – Vol. 28. – P. 262–270.; Mamgain, P. Autodesk 3ds Max 2019: a Detailed Guide to Arnold Renderer / P. Mamgain. – Padexi Academic, 2018. – 192 p.; Simonyan, K. Very deep convolutional networks for large-scale image recognition / K. Simonyan, A. Zisserman // Intern. Conf. on Learning Representations 2014 (ICLR 2014), Banff, Canada, 14–16 Apr. 2014. – Banff, 2014. – P. 1–14.; Bergmann, U. Learning texture manifolds with the periodic spatial GAN / U. Bergmann, N. Jetchev, R. Vollgraf // Proc. of the 34th Intern. Conf. on Machine Learning, Sydney, Australia, 6–11 Aug. 2017. –Sydney, 2017. – Vol. 70. – P. 469–477.; Bundle adjustment in the large / S. Agarwal [et al.] // Proc. of the 11th European Сonf. on Computer Vision (ECCV 2010), Heraklion, Greece, 5–11 Sept. 2010. – Heraklion, 2010. – P. 29–42.; Головатая, Е. А. Модель формирования изображений для трехмерной реконструкции сцен по данным видеоэндоскопических исследований / Е. А. Головатая, В. C. Садов // Вестн. Полоц. гос. ун-та. Сер. С. Фундам. науки. – 2019. – № 12. – С. 43–49.; Lowe, D. G. Distinctive image features from scale-invariant keypoints / D. G. Lowe // Intern. J. of Computer Vision. – 2004. – Vol. 60, no. 2. – P. 91–110.; Rosten, E. Machine learning for high-speed corner detection / E. Rosten, T. Drummond // Proc. of the 9th European Сonf. on Computer Vision (ECCV 2006), Graz, Austria, 7–13 May 2006. – Graz, 2006. –P. 430–443.; BRIEF: binary robust independent elementary features / M. Calonder [et al.] // Proc. of the 11th European Сonf. on Computer Vision (ECCV 2010), Heraklion, Greece, 5–11 Sept. 2010. – Heraklion, 2010. – P. 778–792.; ORB: an efficient alternative to SIFT or SURF / E. Rublee [et al.] // IEEE Intern. Conf. on Computer Vision (ICCV 2011), Barcelona, 6–13 Nov. 2011. – Barcelona, 2011. – P. 2564–2571.; Halavataya, K. Optimizing local feature description and matching for realtime video sequence object detection / K. Halavataya, V. Sadov // Open Semantic Technologies for Intelligent Systems : Research Papers Collection / BSUIR. – Minsk, 2019. – P. 269–272.; https://inf.grid.by/jour/article/view/1061

  6. 6
    Academic Journal
  7. 7
  8. 8
  9. 9
  10. 10
  11. 11
    Conference

    Συγγραφείς: Буй Ван Донг

    Συνεισφορές: Солдатов, Алексей Иванович

    Relation: Современные техника и технологии : сборник трудов XX международной научно-практической конференции студентов, аспирантов и молодых ученых, Томск, 14-18 апреля 2014 г. Т. 3. — Томск, 2014.; http://earchive.tpu.ru/handle/11683/20881

    Διαθεσιμότητα: http://earchive.tpu.ru/handle/11683/20881

  12. 12
  13. 13
  14. 14
  15. 15
  16. 16
  17. 17
  18. 18
  19. 19
  20. 20