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pro vyhledávání: '"Johansen, Tor"'
Autor:
Grigore, Diana-Nicoleta, Georgescu, Mariana-Iuliana, Justo, Jon Alvarez, Johansen, Tor, Ionescu, Andreea Iuliana, Ionescu, Radu Tudor
Few-shot knowledge distillation recently emerged as a viable approach to harness the knowledge of large-scale pre-trained models, using limited data and computational resources. In this paper, we propose a novel few-shot feature distillation approach
Externí odkaz:
http://arxiv.org/abs/2404.09326
Autor:
Kovac, Daniel, Mucha, Jan, Justo, Jon Alvarez, Mekyska, Jiri, Galaz, Zoltan, Novotny, Krystof, Pitonak, Radoslav, Knezik, Jan, Herec, Jonas, Johansen, Tor Arne
This article explores the latest Convolutional Neural Networks (CNNs) for cloud detection aboard hyperspectral satellites. The performance of the latest 1D CNN (1D-Justo-LiuNet) and two recent 2D CNNs (nnU-net and 2D-Justo-UNet-Simple) for cloud segm
Externí odkaz:
http://arxiv.org/abs/2403.08695
Rapidly Exploring Random Tree (RRT) algorithms, notably used for nonholonomic vehicle navigation in complex environments, are often not thoroughly evaluated for their specific challenges. This paper presents a first such comparison study of the varia
Externí odkaz:
http://arxiv.org/abs/2403.01194
This paper presents a distributed solution for the problem of collaborative collision avoidance for autonomous inland waterway ships. A two-layer collision avoidance framework that considers inland waterway traffic regulations is proposed to increase
Externí odkaz:
http://arxiv.org/abs/2403.00554
Dimensionality reduction can be applied to hyperspectral images so that the most useful data can be extracted and processed more quickly. This is critical in any situation in which data volume exceeds the capacity of the computational resources, part
Externí odkaz:
http://arxiv.org/abs/2402.16566
Hyperspectral Imaging comprises excessive data consequently leading to significant challenges for data processing, storage and transmission. Compressive Sensing has been used in the field of Hyperspectral Imaging as a technique to compress the large
Externí odkaz:
http://arxiv.org/abs/2401.14762
Autor:
Justo, Jon Alvarez, Garrett, Joseph L., Georgescu, Mariana-Iuliana, Gonzalez-Llorente, Jesus, Ionescu, Radu Tudor, Johansen, Tor Arne
Satellites are increasingly adopting on-board AI for enhanced autonomy through in-orbit inference. In this context, the use of deep learning (DL) techniques for segmentation in hyperspectral (HS) satellite imagery offers advantages for remote sensing
Externí odkaz:
http://arxiv.org/abs/2310.16210
Autor:
Justo, Jon A., Garrett, Joseph, Langer, Dennis D., Henriksen, Marie B., Ionescu, Radu T., Johansen, Tor A.
Hyperspectral Imaging, employed in satellites for space remote sensing, like HYPSO-1, faces constraints due to few labeled data sets, affecting the training of AI models demanding these ground-truth annotations. In this work, we introduce The HYPSO-1
Externí odkaz:
http://arxiv.org/abs/2308.13679
The accuracy of sensor fusion algorithms are limited by either the intrinsic sensor noise, or by the quality of time synchronization of the sensors. While the intrinsic sensor noise only depends on the respective sensors, the error induced by quality
Externí odkaz:
http://arxiv.org/abs/2209.01136
Publikováno v:
In Ocean Engineering 15 September 2024 308