Page 251 - Artificial Intelligence for Computational Modeling of the Heart

224 Bibliography

276. G. Huang, Z. Liu, L. Van Der Maaten, K.Q. 289. Q.Guo,W.Feng, C. Zhou,R.Huang,L.Wan,S.
Weinberger, Densely connected convolutional Wang, Learning dynamic Siamese network for
networks, in: IEEE CVPR, 2017, pp. 4700–4708. visual object tracking, in: 2017 IEEE International
277. J.D.Dormer, L. Ma,M.Halicek,C.M.Reilly,E. Conference on Computer Vision (ICCV), IEEE,
Schreibmann, B. Fei, Heart chamber segmentation 2017, pp. 1781–1789.
from ct using convolutional neural networks, in: 290. J. Valmadre, L. Bertinetto, J. Henriques, A. Vedaldi,
Medical Imaging: Biomedical Applications in P.H. Torr, End-to-end representation learning for
Molecular, Structural, and Functional Imaging, correlation filter based tracking, in: Computer
vol. 10578, 2018. Vision and Pattern Recognition (CVPR), 2017 IEEE
278. Q.Zheng,H.Delingette,N.Duchateau,N.Ayache, Conference on, IEEE, 2017, pp. 5000–5008.
3-D consistent and robust segmentation of 291. Q. Wang, J. Gao, J. Xing, M. Zhang, W. Hu, Dcfnet:
cardiac images by deep learning with spatial discriminant correlation filters network for visual
propagation, IEEE Transactions on Medical tracking, arXiv preprint, arXiv:1704.04057, 2017.
Imaging 37 (9) (2018) 2137–2148. 292. B.Li, J. Yan, W. Wu,Z.Zhu,X.Hu, High
279. C. Bercea, O. Pauly, A.K. Maier, F.C. Ghesu, performance visual tracking with Siamese region
SHAMANN: shared memory augmented neural proposal network, in: Proceedings of the IEEE
networks, in: IPMI, 2019, in press. Conference on Computer Vision and Pattern
280. D.Yang, D. Xu,S.K. Zhou,B.Georgescu,M.Chen, Recognition, 2018, pp. 8971–8980.
S. Grbic, D. Metaxas, D. Comaniciu, Automatic 293. B.Li, W. Wu,Q.Wang, F. Zhang, J. Xing,J.Yan,
liver segmentation using an adversarial Siamrpn++: evolution of Siamese visual tracking
image-to-image network, in: MICCAI, Springer, with very deep networks, arXiv preprint,
2017, pp. 507–515. arXiv:1812.11703, 2018.
281. Y.Wang, B. Georgescu, T. Chen,W.Wu, P. Wang,X. 294. Z.Zhu,Q.Wang, B. Li,W.Wu, J. Yan, W. Hu,
Lu,R.Ionasec,Y.Zheng,D.Comaniciu, Distractor-aware Siamese networks for visual
Learning-based detection and tracking in medical object tracking, in: Proceedings of the European
imaging: a probabilistic approach, in: Conference on Computer Vision (ECCV), 2018,
Deformation Models, Springer, 2013, pp. 209–235. pp. 101–117.
282. A. Yilmaz, O. Javed, M. Shah, Object tracking: a 295. H. Nam, B. Han, Learning multi-domain
survey, ACM Computing Surveys (CSUR) 38 (4) convolutional neural networks for visual tracking,
(2006) 13. in: The IEEE Conference on Computer Vision and
283. Y. Wu, J. Lim, M.-H. Yang, Online object tracking: a Pattern Recognition (CVPR), June 2016.
benchmark, in: Proceedings of the IEEE 296. Y. Song, C. Ma, L. Gong, J. Zhang, R.W. Lau, M.-H.
Conference on Computer Vision and Pattern Yang, Crest: convolutional residual learning for
Recognition, 2013, pp. 2411–2418. visual tracking, in: Computer Vision (ICCV), 2017
284. H.Yang, L. Shao,F.Zheng,L.Wang, Z. Song, IEEE International Conference on, IEEE, 2017,
Recent advances and trends in visual tracking: a pp. 2574–2583.
review, Neurocomputing 74 (18) (2011) 3823–3831. 297. D.S.Bolme,J.R.Beveridge,B.A.Draper, Y.M. Lui,
285. M.Fiaz, A. Mahmood,S.K. Jung,Trackingnoisy Visual object tracking using adaptive correlation
targets: a review of recent object tracking filters, in: Computer Vision and Pattern
approaches, arXiv preprint, arXiv:1802.03098, Recognition (CVPR), 2010 IEEE Conference on,
2018. IEEE, 2010, pp. 2544–2550.
286. J.Bromley,I.Guyon,Y.LeCun,E.Säckinger,R. 298. M. Danelljan, G. Hager, F. Shahbaz Khan, M.
Shah, Signature verification using a “Siamese” Felsberg, Convolutional features for correlation
time delay neural network, in: Advances in Neural filter based visual tracking, in: Proceedings of the
Information Processing Systems, 1994, IEEE International Conference on Computer
pp. 737–744. Vision Workshops, 2015, pp. 58–66.
287. L. Bertinetto, J. Valmadre, J.F. Henriques, A. 299. Z.Zhu,W.Wu, W. Zou, J. Yan, End-to-endflow
Vedaldi, P.H. Torr, Fully-convolutional Siamese correlation tracking with spatial-temporal
networks for object tracking, in: European attention, Illumination 42 (2018) 20.
Conference on Computer Vision, Springer, 2016, 300. N. Parajuli, A. Lu, J.C. Stendahl, M. Zontak, N.
pp. 850–865. Boutagy, I. Alkhalil, M. Eberle, B.A. Lin, M.
288. D. Held, S. Thrun, S. Savarese, Learning to track at O’Donnell, A.J. Sinusas, et al., Flow network based
100 fps with deep regression networks, in: cardiac motion tracking leveraging learned feature
European Conference Computer Vision (ECCV), matching, in: International Conference on
2016. Medical Image Computing and