Zobrazeno 1 - 10
of 96
pro vyhledávání: '"S. DiPietro"'
Autor:
Robert S. DiPietro, Gregory D. Hager
Publikováno v:
Handbook of Medical Image Computing and Computer Assisted Intervention ISBN: 9780128161760
Recurrent neural networks (RNNs) are a class of neural networks that are naturally suited to processing time-series data and other sequential data. Here we introduce recurrent neural networks as an extension to feedforward networks, in order to allow
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::155a1e0f791593dc1901969f0ee0b65e
https://doi.org/10.1016/b978-0-12-816176-0.00026-0
https://doi.org/10.1016/b978-0-12-816176-0.00026-0
Autor:
Robert S. DiPietro, Gregory D. Hager
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783030322533
MICCAI (5)
MICCAI (5)
Prior work has demonstrated the feasibility of automated activity recognition in robot-assisted surgery from motion data. However, these efforts have assumed the availability of a large number of densely-annotated sequences, which must be provided ma
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::94261486970f4179add5aac32fe6788a
https://doi.org/10.1007/978-3-030-32254-0_51
https://doi.org/10.1007/978-3-030-32254-0_51
Autor:
Mija R. Lee, Madeleine M. Waldram, Narges Ahmidi, Robert S. DiPietro, Anand Malpani, Gregory D. Hager, Gyusung Lee, S. Swaroop Vedula
Publikováno v:
International journal of computer assisted radiology and surgery. 14(11)
Automatically segmenting and classifying surgical activities is an important prerequisite to providing automated, targeted assessment and feedback during surgical training. Prior work has focused almost exclusively on recognizing gestures, or short,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c66d657cfc947c9b26ac034d305b559f
https://pubmed.ncbi.nlm.nih.gov/31037493
https://pubmed.ncbi.nlm.nih.gov/31037493
Autor:
Robert S. DiPietro, Gregory D. Hager
Publikováno v:
Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 ISBN: 9783030009366
MICCAI (4)
MICCAI (4)
We show that it is possible to learn meaningful representations of surgical motion, without supervision, by learning to predict the future. An architecture that combines an RNN encoder-decoder and mixture density networks (MDNs) is developed to model
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0dc26089c9e8530c1b2adc712c7b3585
https://doi.org/10.1007/978-3-030-00937-3_33
https://doi.org/10.1007/978-3-030-00937-3_33
Publikováno v:
ICCV
One-shot pose estimation for tasks such as body joint localization, camera pose estimation, and object tracking are generally noisy, and temporal filters have been extensively used for regularization. One of the most widely-used methods is the Kalman
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::50eff8c911e6a93e7cf6d5872f3eeaa3
https://doi.org/10.1109/iccv.2017.589
https://doi.org/10.1109/iccv.2017.589
Publikováno v:
IEEE Signal Processing Magazine. 31:120-141
Many military and civilian applications depend on the ability to remotely sense chemical agent (CA) clouds, from detecting small but lethal concentrations of chemical warfare agents (CWAs) to mapping plumes in the aftermath of natural disasters. Hype
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c63a94419bb1da149f96ddf05931f231
https://doi.org/10.1109/msp.2013.2294804
https://doi.org/10.1109/msp.2013.2294804
Autor:
Don Heiman, Robert S. DiPietro, Brian D. Plouffe, Lewis H. Lewis, Shashi K. Murthy, Dattatri Nagesha, Srinvas Sridhar
Publikováno v:
Journal of Magnetism and Magnetic Materials. 323:2310-2317
The utility and promise of magnetic nanoparticles (MagNPs) for biomedicine rely heavily on accurate determination of the particle diameter attributes. While the average functional size and size distribution of the magnetic nanoparticles directly impa
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::80ce9ff6dcd781605e18a1adfc4eca37
https://doi.org/10.1016/j.jmmm.2011.04.013
https://doi.org/10.1016/j.jmmm.2011.04.013
Akademický článek
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Autor:
Ian L. Pegg, Robert S. DiPietro, Sungmu Kang, Jugdersuren Battogtokh, Andrew C. Buechele, David A. McKeown, Don Heiman, John Philip, Greg Brewer
Publikováno v:
Nanoscience and Nanotechnology Letters. 1:77-81
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8d6518b115ae041f2306647d4abb27da
https://doi.org/10.1166/nnl.2009.1018
https://doi.org/10.1166/nnl.2009.1018