Zobrazeno 1 - 10
of 58
pro vyhledávání: '"Eising, Ciarán"'
Publikováno v:
Proceedings of the 12th IEEE International Conference on Healthcare Informatics (IEEE ICHI 2024)
This research develops a new method to detect anomalies in time series data using Convolutional Neural Networks (CNNs) in healthcare-IoT. The proposed method creates a Distributed Denial of Service (DDoS) attack using an IoT network simulator, Cooja,
Externí odkaz:
http://arxiv.org/abs/2407.20695
Autor:
Manzoor, Anam, Singh, Aryan, Sistu, Ganesh, Mohandas, Reenu, Grua, Eoin, Scanlan, Anthony, Eising, Ciarán
Publikováno v:
Proceedings of the Irish Machine Vision and Image Processing Conference 2024
This study investigates the effectiveness of modern Deformable Convolutional Neural Networks (DCNNs) for semantic segmentation tasks, particularly in autonomous driving scenarios with fisheye images. These images, providing a wide field of view, pose
Externí odkaz:
http://arxiv.org/abs/2407.16647
Publikováno v:
Proceedings of the Irish Machine Vision and Image Processing Conference 2024
Fusing different sensor modalities can be a difficult task, particularly if they are asynchronous. Asynchronisation may arise due to long processing times or improper synchronisation during calibration, and there must exist a way to still utilise thi
Externí odkaz:
http://arxiv.org/abs/2407.16636
Autor:
Jakab, Daniel, Braun, Alexander, Agnew, Cathaoir, Mohandas, Reenu, Deegan, Brian Michael, Molloy, Dara, Ward, Enda, Scanlan, Tony, Eising, Ciarán
Publikováno v:
Proceedings of the Irish Machine Vision and Image Processing Conference 2024
Automotive simulation can potentially compensate for a lack of training data in computer vision applications. However, there has been little to no image quality evaluation of automotive simulation and the impact of optical degradations on simulation
Externí odkaz:
http://arxiv.org/abs/2407.15646
In this study, we present the Graph Sub-Graph Network (GSN), a novel hybrid image classification model merging the strengths of Convolutional Neural Networks (CNNs) for feature extraction and Graph Neural Networks (GNNs) for structural modeling. GSN
Externí odkaz:
http://arxiv.org/abs/2407.14772
Publikováno v:
Proceedings of the Irish Machine Vision and Image Processing Conference 2024
Predicting ego vehicle trajectories remains a critical challenge, especially in urban and dense areas due to the unpredictable behaviours of other vehicles and pedestrians. Multimodal trajectory prediction enhances decision-making by considering mult
Externí odkaz:
http://arxiv.org/abs/2407.05811
Visual Question Answering (VQA) models play a critical role in enhancing the perception capabilities of autonomous driving systems by allowing vehicles to analyze visual inputs alongside textual queries, fostering natural interaction and trust betwee
Externí odkaz:
http://arxiv.org/abs/2406.09203
Autor:
Jakab, Daniel, Deegan, Brian Michael, Sharma, Sushil, Grua, Eoin Martino, Horgan, Jonathan, Ward, Enda, Van De Ven, Pepijn, Scanlan, Anthony, Eising, Ciarán
Publikováno v:
IEEE Transactions on Intelligent Transportation Systems, 2024
In this paper, we provide a survey on automotive surround-view fisheye optics, with an emphasis on the impact of optical artifacts on computer vision tasks in autonomous driving and ADAS. The automotive industry has advanced in applying state-of-the-
Externí odkaz:
http://arxiv.org/abs/2402.12041
Autor:
Das, Arindam, Paul, Sudarshan, Scholz, Niko, Malviya, Akhilesh Kumar, Sistu, Ganesh, Bhattacharya, Ujjwal, Eising, Ciarán
Accurate obstacle identification represents a fundamental challenge within the scope of near-field perception for autonomous driving. Conventionally, fisheye cameras are frequently employed for comprehensive surround-view perception, including rear-v
Externí odkaz:
http://arxiv.org/abs/2402.00637
Autor:
Jakab, Daniel, Grua, Eoin Martino, Deegan, Brian Micheal, Scanlan, Anthony, Van De Ven, Pepijn, Eising, Ciarán
The Modulation Transfer Function (MTF) is an important image quality metric typically used in the automotive domain. However, despite the fact that optical quality has an impact on the performance of computer vision in vehicle automation, for many pu
Externí odkaz:
http://arxiv.org/abs/2401.05232