Zobrazeno 1 - 10
of 1 176
pro vyhledávání: '"A. Traviglia"'
The primary challenge for handwriting recognition systems lies in managing long-range contextual dependencies, an issue that traditional models often struggle with. To mitigate it, attention mechanisms have recently been employed to enhance context-a
Externí odkaz:
http://arxiv.org/abs/2409.05699
Publikováno v:
Il Foro Italiano, 2010 Mar 01. 133(3), 139/140-167/168.
Externí odkaz:
https://www.jstor.org/stable/23204322
Autor:
Jaturapitpornchai, Raveerat, Poggi, Giulio, Sech, Gregory, Kokalj, Ziga, Fiorucci, Marco, Traviglia, Arianna
Publikováno v:
IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium
Deep learning methods in LiDAR-based archaeological research often leverage visualisation techniques derived from Digital Elevation Models to enhance characteristics of archaeological objects present in the images. This paper investigates the impact
Externí odkaz:
http://arxiv.org/abs/2404.05512
Publikováno v:
IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium
Hyperspectral data recorded from satellite platforms are often ill-suited for geo-archaeological prospection due to low spatial resolution. The established potential of hyperspectral data from airborne sensors in identifying archaeological features h
Externí odkaz:
http://arxiv.org/abs/2404.05447
Publikováno v:
2023 Sixth International Workshop on Mobile Terahertz Systems (IWMTS), Bonn, Germany, 2023, pp. 1-5
Terahertz time-domain spectroscopy (THz-TDS) employs sub-picosecond pulses to probe dielectric properties of materials giving as a result a 3-dimensional hyperspectral data cube. The spatial resolution of THz images is primarily limited by two source
Externí odkaz:
http://arxiv.org/abs/2312.13820
Identifying changes in a pair of 3D aerial LiDAR point clouds, obtained during two distinct time periods over the same geographic region presents a significant challenge due to the disparities in spatial coverage and the presence of noise in the acqu
Externí odkaz:
http://arxiv.org/abs/2307.15428
Autor:
Sech, Gregory, Soleni, Paolo, der Vaart, Wouter B. Verschoof-van, Kokalj, Žiga, Traviglia, Arianna, Fiorucci, Marco
When applying deep learning to remote sensing data in archaeological research, a notable obstacle is the limited availability of suitable datasets for training models. The application of transfer learning is frequently employed to mitigate this drawb
Externí odkaz:
http://arxiv.org/abs/2307.03512
Publikováno v:
In: Image Analysis and Processing. ICIAP 2022 Workshops. Lecture Notes in Computer Science, vol. 13373. Springer, Cham (2022)
Most computer vision and machine learning-based approaches for historical document analysis are tailored to grayscale or RGB images and thus, mostly exploit their spatial information. Multispectral (MS) and hyperspectral (HS) images contain, next to
Externí odkaz:
http://arxiv.org/abs/2303.05130
Publikováno v:
Il Foro Italiano, 1975 Sep 01. 98(9), 2121/2122-2121/2122.
Externí odkaz:
https://www.jstor.org/stable/23166747
Publikováno v:
Il Foro Italiano, 1903 Jan 01. 28, 1355/1356-1363/1364.
Externí odkaz:
https://www.jstor.org/stable/23105579