Zobrazeno 1 - 10
of 14
pro vyhledávání: '"Iason Katsamenis"'
Autor:
Iason Katsamenis, Nikolaos Bakalos, Eleni Eirini Karolou, Anastasios Doulamis, Nikolaos Doulamis
Publikováno v:
Technologies, Vol 10, Iss 2, p 47 (2022)
Man overboard is an emergency in which fast and efficient detection of the critical event is the key factor for the recovery of the victim. Its severity urges the utilization of intelligent video surveillance systems that monitor the ship’s perimet
Externí odkaz:
https://doaj.org/article/85abac7497a64b698635088830a5d04a
Autor:
Athanasios Voulodimos, Eftychios Protopapadakis, Iason Katsamenis, Anastasios Doulamis, Nikolaos Doulamis
Publikováno v:
Sensors, Vol 21, Iss 6, p 2215 (2021)
Recent studies indicate that detecting radiographic patterns on CT chest scans can yield high sensitivity and specificity for COVID-19 identification. In this paper, we scrutinize the effectiveness of deep learning models for semantic segmentation of
Externí odkaz:
https://doaj.org/article/7fdcf674bba5437198f57299bdfe5dd0
Publikováno v:
NiDS
Man overboard incidents in a maritime vessel are serious accidents where the rapid detection of the even is crucial for the safe retrieval of the person. To this end, the use of deep learning models as automatic detectors of these scenarios has been
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::893811f5cf5b89fcdfa7533925411d26
https://doi.org/10.3233/faia210103
https://doi.org/10.3233/faia210103
Autor:
Iason Katsamenis, Anastasios Doulamis, Eftychios Protopapadakis, Athanasios Voulodimos, Nikolaos Doulamis
Publikováno v:
PETRA
Recent studies indicated that detecting radiographic patterns on CT chest scans can yield high sensitivity and specificity for COVID-19 detection. In this work, we scrutinize the effectiveness of deep learning models for semantic segmentation of pneu
Publikováno v:
PETRA
Man overboard incidents in a maritime vessel are serious accidents where, the efficient and rapid detection is crucial in the recovery of the victim. The severity of such accidents, urge the use of intelligent systems that are able to automatically d
Autor:
Nikolaos Doulamis, Eftychios Protopapadakis, Anastasios Doulamis, Athanasios Voulodimos, Iason Katsamenis
Publikováno v:
Sensors (Basel, Switzerland)
Sensors, Vol 21, Iss 2215, p 2215 (2021)
Sensors
Volume 21
Issue 6
Sensors, Vol 21, Iss 2215, p 2215 (2021)
Sensors
Volume 21
Issue 6
Recent studies indicate that detecting radiographic patterns on CT chest scans can yield high sensitivity and specificity for COVID-19 identification. In this paper, we scrutinize the effectiveness of deep learning models for semantic segmentation of
Autor:
Iason Katsamenis, Nikolaos Doulamis, Anastasios Doulamis, Eftychios Protopapadakis, Athanasios Voulodimos
Publikováno v:
Automation in Construction. 137:104182
Autor:
Nikolaos Doulamis, Anastasios Doulamis, Eftychios Protopapadakis, Iason Katsamenis, Athanasios Voulodimos
Publikováno v:
PCI
We introduce a deep learning framework that can detect COVID-19 pneumonia in thoracic radiographs, as well as differentiate it from bacterial pneumonia infection. Deep classification models, such as convolutional neural networks (CNNs), require large
Autor:
Iason Katsamenis, Eftychios Protopapadakis, Athanasios Voulodimos, D. Dres, Dimitris Drakoulis
Publikováno v:
PETRA
A man overboard is an emergency incident, in which fast detection is the most crucial factor, for the quickest and most efficient recovery of the victim. As such, efficient monitoring methodologies should be employed. A variety of sensors is availabl
Autor:
Anastasios Doulamis, Eftychios Protopapadakis, Athanasios Voulodimos, Iason Katsamenis, Nikolaos Doulamis
Publikováno v:
Advances in Visual Computing ISBN: 9783030645557
ISVC (1)
ISVC (1)
Corrosion detection on metal constructions is a major challenge in civil engineering for quick, safe and effective inspection. Existing image analysis approaches tend to place bounding boxes around the defected region which is not adequate both for s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::976b77bd257025b9f09ed9d37413e3b9
https://doi.org/10.1007/978-3-030-64556-4_13
https://doi.org/10.1007/978-3-030-64556-4_13