Zobrazeno 1 - 10
of 134
pro vyhledávání: '"Traviglia, Arianna"'
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
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
This paper proposes JiGAN, a GAN-based method for solving Jigsaw puzzles with eroded or missing borders. Missing borders is a common real-world situation, for example, when dealing with the reconstruction of broken artifacts or ruined frescoes. In th
Externí odkaz:
http://arxiv.org/abs/2203.14428
The increasing need of restoring high-resolution Hyper-Spectral (HS) images is determining a growing reliance on Computer Vision-based processing to enhance the clarity of the image content. HS images can, in fact, suffer from degradation effects or
Externí odkaz:
http://arxiv.org/abs/2203.00417
Autor:
Abate, Francesco, De Bernardin, Michela, Stratigaki, Maria, Franceschin, Giulia, Albertin, Fauzia, Bettuzzi, Matteo, Brancaccio, Rosa, Bressan, Anita, Morigi, Maria Pia, Daniele, Salvatore, Traviglia, Arianna
Publikováno v:
In Journal of Cultural Heritage March-April 2024 66:436-443
Publikováno v:
In Journal of Cultural Heritage November-December 2023 64:132-143