다음과 같은 기능을 한번의 로그인으로 사용 할 수 있습니다.
NTIS 바로가기
Biomedical engineering letters, v.13 no.2, 2023년, pp.141 - 151
Somers, Peter (Institute for System Dynamics, University of Stuttgart, Stuttgart, Germany) , Holdenried-Krafft, Simon (Institute for Computer Graphics, University of Tü) , Zahn, Johannes (bingen, Tü) , Schüle, Johannes (bingen, Germany) , Veil, Carina (Institute for Computer Graphics, University of Tü) , Harland, Niklas (bingen, Tü) , Walz, Simon (bingen, Germany) , Stenzl, Arnulf (Institute for System Dynamics, University of Stuttgart, Stuttgart, Germany) , Sawodny, Oliver (Institute for System Dynamics, University of Stuttgart, Stuttgart, Germany) , Tarín, Cristina (Urology Clinic, University Hospital of Tü) , Lensch, Hendrik P. A. (bingen, Tü)
AI-Helper
Monocular depth estimation from camera images is very important for surrounding scene evaluation in many technical fields from automotive to medicine. However, traditional triangulation methods using stereo cameras or multiple views with the assumption of a rigid environment are not applicable for e...
1. Schüle J, Haag J, Somers P, Veil C, Tarín C, Sawodny O. A model-based simultaneous localization and mapping approach for deformable bodies. In: 2022 IEEE/ASME international conference on advanced intelligent mechatronics (AIM), pp. 607–612 (2022). 10.1109/AIM52237.2022.9863308
2. Karaoglu MA, Brasch N, Stollenga M, Wein W, Navab N, Tombari F, Ladikos A. Adversarial domain feature adaptation for bronchoscopic depth estimation. In: de Bruijne, M., Cattin, P.C., Cotin, S., Padoy, N., Speidel, S., Zheng, Y., Essert, C. (eds.) Medical Image Computing and Computer Assisted Intervention–MICCAI 2021. Lecture Notes in Computer Science, vol. 12904, pp. 300–310. Springer International Publishing, Cham (2021). 10.1007/978-3-030-87202-1_29
3. Li S Liu CH Lin Q Wen Q Su L Huang G Ding Z Deep residual correction network for partial domain adaptation IEEE Trans Pattern Analysis Mach Intell. 2021 43 7 2329 2344 10.1109/tpami.2020.2964173
4. Ullman S. The interpretation of structure from motion. The Royal Society. 1979
5. Schönberger JL, Frahm J-M. Structure-from-motion revisited. In: Conference on Computer Vision and Pattern Recognition (CVPR) (2016)
6. Luo X, Huang J-B, Szeliski R, Matzen K, Kopf J. Consistent video depth estimation. arXiv (2020). 10.48550/ARXIV.2004.15021. https://arxiv.org/abs/2004.15021
7. Mayer N, Ilg E, Häusser P, Fischer P, Cremers D, Dosovitskiy A, Brox T. A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), pp. 4040–4048 (2016). 10.1109/CVPR.2016.438
8. Laina I, Rupprecht C, Belagiannis V, Tombari F, Navab N. Deeper depth prediction with fully convolutional residual networks. In: 2016 fourth international conference on 3D vision (3DV), pp. 239–248 (2016). 10.1109/3DV.2016.32
9. Kundu JN, Uppala PK, Pahuja A, Babu RV. Adadepth: Unsupervised content congruent adaptation for depth estimation. In: 2018 IEEE/CVF Conference on computer vision and pattern recognition, pp. 2656–2665 (2018). 10.1109/CVPR.2018.00281
10. Mahmood F Durr NJ Deep learning and conditional random fields-based depth estimation and topographical reconstruction from conventional endoscopy Medical Image Analysis 2018 48 230 243 10.1016/j.media.2018.06.005 29990688
11. Aitken AP, Ledig C, Theis L, Caballero J, Wang Z, Shi W. Checkerboard artifact free sub-pixel convolution: a note on sub-pixel convolution, resize convolution and convolution resize. CoRR abs/1707.02937 (2017) arXiv:1707.02937
12. Alayrac J-B, Donahue J, Luc P, Miech A, Barr I, Hasson Y, Lenc K, Mensch A, Millican K, Reynolds M, Ring R, Rutherford E, Cabi S, Han T, Gong Z, Samangooei S, Monteiro M, Menick J, Borgeaud S, Brock A, Nematzadeh A, Sharifzadeh S, Binkowski M, Barreira R, Vinyals O, Zisserman A, Simonyan K. Flamingo: a visual language model for few-shot learning. arXiv (2022). 10.48550/ARXIV.2204.14198. https://arxiv.org/abs/2204.14198
13. Bachlechner T, Majumder BP, Mao HH, Cottrell GW, McAuley J. Rezero is all you need: Fast convergence at large depth. In: thirty-seventh conference on uncertainty in artificial intelligence. arXiv: Machine Learning, ??? (2020). 10.48550/ARXIV.2003.04887. https://arxiv.org/abs/2003.04887
14. Blender Development Team: Blender 3.1.0. accessed: 20.04.2022 (2022). https://www.blender.org/download/releases/3-1/ Accessed 20.04.2022
15. Peddie J Ray tracing: a tool for all 2019 Cham Springer
16. Rajpura PS, Hegde RS, Bojinov H. Object detection using deep CNNS trained on synthetic images. ArXiv 2017. 10.48550/arXiv.1706.06782
17. Rister B Yi D Shivakumar K Nobashi T Rubin DL Ct-org, a new dataset for multiple organ segmentation in computed tomography Scientific Data 2020 7 1 381 10.1038/s41597-020-00715-8 33177518
18. Zwald L, Lambert-Lacroix S. The berhu penalty and the grouped effect. ArXiv: Statistics Theory 2012. 10.48550/arXiv.1207.6868
19. Eigen D, Fergus R. Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture. In: 2015 IEEE international conference on computer vision (ICCV). 2015 2650–2658. 10.1109/ICCV.2015.304
20. Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y. Generative adversarial nets. In: advances in neural information processing systems 2014. 10.48550/ARXIV.1406.2661
*원문 PDF 파일 및 링크정보가 존재하지 않을 경우 KISTI DDS 시스템에서 제공하는 원문복사서비스를 사용할 수 있습니다.
Copyright KISTI. All Rights Reserved.
※ AI-Helper는 부적절한 답변을 할 수 있습니다.