Zobrazeno 1 - 10
of 255
pro vyhledávání: '"Fabio Tosi"'
Autor:
Arsal-Hanif Livoroi, Andrea Conti, Luca Foianesi, Fabio Tosi, Filippo Aleotti, Matteo Poggi, Flavia Tauro, Elena Toth, Salvatore Grimaldi, Stefano Mattoccia
Publikováno v:
Applied Sciences, Vol 11, Iss 15, p 7027 (2021)
As reported in the recent image velocimetry literature, tracking the motion of sparse feature points floating on the river surface as done by the Optical Tracking Velocimetry (OTV) algorithm is a promising strategy to address surface flow monitoring.
Externí odkaz:
https://doaj.org/article/5dd54fb8019c41b8aefdb3e811744caf
Autor:
Gian Nicola Bisciotti, Piero Volpi, Maurizio Amato, Giampietro Alberti, Francesco Allegra, Alessandro Aprato, Matteo Artina, Alessio Auci, Corrado Bait, Gian Matteo Bastieri, Luca Balzarini, Andrea Belli, Gianandrea Bellini, Pierfrancesco Bettinsoli, Alessandro Bisciotti, Andrea Bisciotti, Stefano Bona, Lorenzo Brambilla, Marco Bresciani, Michele Buffoli, Filippo Calanna, Gian Luigi Canata, Davide Cardinali, Giulia Carimati, Gabriella Cassaghi, Enrico Cautero, Emanuele Cena, Barbara Corradini, Alessandro Corsini, Cristina D'Agostino, Massimo De Donato, Giacomo Delle Rose, Francesco Di Marzo, Francesco Di Pietto, Drapchind Enrica, Cristiano Eirale, Luigi Febbrari, Paolo Ferrua, Andrea Foglia, Alberto Galbiati, Alberto Gheza, Carlo Giammattei, Francesco Masia, Gianluca Melegati, Biagio Moretti, Lorenzo Moretti, Roberto Niccolai, Antonio Orgiani, Claudio Orizio, Andrea Pantalone, Federica Parra, Paolo Patroni, Maria Teresa Pereira Ruiz, Marzio Perri, Stefano Petrillo, Luca Pulici, Alessandro Quaglia, Luca Ricciotti, Francesco Rosa, Nicola Sasso, Claudio Sprenger, Chiara Tarantola, Fabio Gianpaolo Tenconi, Fabio Tosi, Michele Trainini, Agostino Tucciarone, Ali Yekdah, Zarko Vuckovic, Raul Zini, Karim Chamari
Publikováno v:
BMJ Open Sport & Exercise Medicine, Vol 4, Iss 1 (2018)
Provide the state of the art concerning (1) biology and aetiology, (2) classification, (3) clinical assessment and (4) conservative treatment of lower limb muscle injuries (MI) in athletes. Seventy international experts with different medical backgro
Externí odkaz:
https://doaj.org/article/f51bc9d37cc74e3d8d1879ac1c73a046
Autor:
Filippo Aleotti, Giulio Zaccaroni, Luca Bartolomei, Matteo Poggi, Fabio Tosi, Stefano Mattoccia
Publikováno v:
Sensors, Vol 21, Iss 1, p 15 (2020)
Depth perception is paramount for tackling real-world problems, ranging from autonomous driving to consumer applications. For the latter, depth estimation from a single image would represent the most versatile solution since a standard camera is avai
Externí odkaz:
https://doaj.org/article/83e5aac131de4abd8bf18ee9b2d826a8
Autor:
Valentino Peluso, Fabio Tosi, Stefano Mattoccia, Antonio Cipolletta, Andrea Calimera, Filippo Aleotti, Matteo Poggi
Publikováno v:
IEEE Internet of Things Journal. 9:25-36
The recent advancements in deep learning have demonstrated that inferring high-quality depth maps from a single image has become feasible and accurate, thanks to convolutional neural networks (CNNs), but how to process such compute- and memory-intens
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::98ae051e5ec3633e3fcda51d4c7d8716
https://doi.org/10.1109/jiot.2021.3080827
https://doi.org/10.1109/jiot.2021.3080827
Autor:
Jaime Spencer, C. Stella Qian, Chris Russell, Simon Hadfield, Erich Graf, Wendy Adams, Andrew J. Schofield, James Elder, Richard Bowden, Heng Cong, Stefano Mattoccia, Matteo Poggi, Zeeshan Khan Suri, Yang Tang, Fabio Tosi, Hao Wang, Youmin Zhang, Yusheng Zhang, Chaoqiang Zhao
Publikováno v:
2023 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW).
This paper summarizes the results of the first Monocular Depth Estimation Challenge (MDEC) organized at WACV2023. This challenge evaluated the progress of self-supervised monocular depth estimation on the challenging SYNS-Patches dataset. The challen
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9948cd528bbc04b8047ace8e541f0fff
https://doi.org/10.1109/wacvw58289.2023.00069
https://doi.org/10.1109/wacvw58289.2023.00069
Autor:
Fabio Tosi, Matteo Rocca, Filippo Aleotti, Matteo Poggi, Stefano Mattoccia, Flavia Tauro, Elena Toth, Salvatore Grimaldi
Publikováno v:
Remote Sensing, Vol 12, Iss 12, p 2047 (2020)
Monitoring streamflow velocity is of paramount importance for water resources management and in engineering practice. To this aim, image-based approaches have proved to be reliable systems to non-intrusively monitor water bodies in remote places at v
Externí odkaz:
https://doaj.org/article/536462b5b50a43c6b4e58d5faf28518e
Autor:
Chaoqiang Zhao, Youmin Zhang, Matteo Poggi, Fabio Tosi, Xianda Guo, Zheng Zhu, Guan Huang, Yang Tang, Stefano Mattoccia
Self-supervised monocular depth estimation is an attractive solution that does not require hard-to-source depth labels for training. Convolutional neural networks (CNNs) have recently achieved great success in this task. However, their limited recept
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::74d2f2ef10aac1b6856f76519543e8da
http://arxiv.org/abs/2208.03543
http://arxiv.org/abs/2208.03543
Autor:
Pierluigi Zama Ramirez, Fabio Tosi, Matteo Poggi, Samuele Salti, Stefano Mattoccia, Luigi Di Stefano
We present a novel high-resolution and challenging stereo dataset framing indoor scenes annotated with dense and accurate ground-truth disparities. Peculiar to our dataset is the presence of several specular and transparent surfaces, i.e. the main ca
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bcc23533216c37cf73e1c9174b18e5bb
http://arxiv.org/abs/2206.04671
http://arxiv.org/abs/2206.04671
Autor:
Filippo Aleotti, Fabio Tosi, Pierluigi Zama Ramirez, Matteo Poggi, Samuele Salti, Stefano Mattoccia, Luigi Di Stefano
We introduce a novel architecture for neural disparity refinement aimed at facilitating deployment of 3D computer vision on cheap and widespread consumer devices, such as mobile phones. Our approach relies on a continuous formulation that enables to
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fe9637fbb2cd26e21029e4355f88ed48
http://arxiv.org/abs/2110.15367
http://arxiv.org/abs/2110.15367
Autor:
Flavia Tauro, Fabio Tosi, Stefano Mattoccia, Elena Toth, Rodolfo Piscopia, Salvatore Grimaldi
Publikováno v:
Remote Sensing, Vol 10, Iss 12, p 2010 (2018)
Nonintrusive image-based methods have the potential to advance hydrological streamflow observations by providing spatially distributed data at high temporal resolution. Due to their simplicity, correlation-based approaches have until recent been pref
Externí odkaz:
https://doaj.org/article/68e0d24c17244241865e0cc29202a96f