Zobrazeno 1 - 10
of 90
pro vyhledávání: '"Kyrkou, Christos"'
Publikováno v:
SN Computer Science, 2024 SN Computer Science, 2024 SN Computer Science, 2024
The integration of Unmanned Aerial Vehicles (UAVs) with artificial intelligence (AI) models for aerial imagery processing in disaster assessment, necessitates models that demonstrate exceptional accuracy, computational efficiency, and real-time proce
Externí odkaz:
http://arxiv.org/abs/2410.13663
Autor:
Telegraph, Kristina, Kyrkou, Christos
Publikováno v:
IEEE Transactions on Artificial Intelligence, 2024
This work presents advancements in multi-class vehicle detection using UAV cameras through the development of spatiotemporal object detection models. The study introduces a Spatio-Temporal Vehicle Detection Dataset (STVD) containing 6, 600 annotated
Externí odkaz:
http://arxiv.org/abs/2410.13616
Autor:
Kyrkou, Christos
High-efficiency deep learning (DL) models are necessary not only to facilitate their use in devices with limited resources but also to improve resources required for training. Convolutional neural networks (ConvNets) typically exert severe demands on
Externí odkaz:
http://arxiv.org/abs/2407.14831
The Forward-Forward (FF) Algorithm has been recently proposed to alleviate the issues of backpropagation (BP) commonly used to train deep neural networks. However, its current formulation exhibits limitations such as the generation of negative data,
Externí odkaz:
http://arxiv.org/abs/2312.12668
Autonomous vehicles increasingly rely on cameras to provide the input for perception and scene understanding and the ability of these models to classify their environment and objects, under adverse conditions and image noise is crucial. When the inpu
Externí odkaz:
http://arxiv.org/abs/2111.03480
Autor:
Kyrkou, Christos
The need for automated real-time visual systems in applications such as smart camera surveillance, smart environments, and drones necessitates the improvement of methods for visual active monitoring and control. Traditionally, the active monitoring t
Externí odkaz:
http://arxiv.org/abs/2107.13233
Autor:
Ignatov, Andrey, Malivenko, Grigory, Timofte, Radu, Chen, Sheng, Xia, Xin, Liu, Zhaoyan, Zhang, Yuwei, Zhu, Feng, Li, Jiashi, Xiao, Xuefeng, Tian, Yuan, Wu, Xinglong, Kyrkou, Christos, Chen, Yixin, Zhang, Zexin, Peng, Yunbo, Lin, Yue, Dutta, Saikat, Das, Sourya Dipta, Shah, Nisarg A., Kumar, Himanshu, Ge, Chao, Wu, Pei-Lin, Du, Jin-Hua, Batutin, Andrew, Federico, Juan Pablo, Lyda, Konrad, Khojoyan, Levon, Thanki, Abhishek, Paul, Sayak, Siddiqui, Shahid
Camera scene detection is among the most popular computer vision problem on smartphones. While many custom solutions were developed for this task by phone vendors, none of the designed models were available publicly up until now. To address this prob
Externí odkaz:
http://arxiv.org/abs/2105.08819
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ( Volume: 13), Page(s): 1687 - 1699, 2020
Deep learning-based algorithms can provide state-of-the-art accuracy for remote sensing technologies such as unmanned aerial vehicles (UAVs)/drones, potentially enhancing their remote sensing capabilities for many emergency response and disaster mana
Externí odkaz:
http://arxiv.org/abs/2104.14006
Autor:
Shafique, Muhammad, Naseer, Mahum, Theocharides, Theocharis, Kyrkou, Christos, Mutlu, Onur, Orosa, Lois, Choi, Jungwook
Publikováno v:
IEEE Design and Test (Volume: 37, Issue: 2, April 2020): 30-57
Machine Learning (ML) techniques have been rapidly adopted by smart Cyber-Physical Systems (CPS) and Internet-of-Things (IoT) due to their powerful decision-making capabilities. However, they are vulnerable to various security and reliability threats
Externí odkaz:
http://arxiv.org/abs/2101.02559
Autor:
Kyrkou, Christos
The increasing need for automated visual monitoring and control for applications such as smart camera surveillance, traffic monitoring, and intelligent environments, necessitates the improvement of methods for visual active monitoring. Traditionally,
Externí odkaz:
http://arxiv.org/abs/2012.06428