Search Results for author: Arianna Traviglia

Found 7 papers, 1 papers with code

Impact of LiDAR visualisations on semantic segmentation of archaeological objects

no code implementations8 Apr 2024 Raveerat Jaturapitpornchai, Giulio Poggi, Gregory Sech, Ziga Kokalj, Marco Fiorucci, Arianna Traviglia

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.

Semantic Segmentation

Pansharpening of PRISMA products for archaeological prospection

no code implementations8 Apr 2024 Gregory Sech, Giulio Poggi, Marina Ljubenovic, Marco Fiorucci, Arianna Traviglia

Hyperspectral data recorded from satellite platforms are often ill-suited for geo-archaeological prospection due to low spatial resolution.


Super-resolution of THz time-domain images based on low-rank representation

no code implementations21 Dec 2023 Marina Ljubenovic, Alessia Artesani, Stefano Bonetti, Arianna Traviglia

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.


Implicit neural representation for change detection

1 code implementation28 Jul 2023 Peter Naylor, Diego Di Carlo, Arianna Traviglia, Makoto Yamada, Marco Fiorucci

We outperform the previous methods by a margin of 10% in the intersection over union metric.

Change Detection

Transfer Learning of Semantic Segmentation Methods for Identifying Buried Archaeological Structures on LiDAR Data

no code implementations7 Jul 2023 Gregory Sech, Paolo Soleni, Wouter B. Verschoof-van der Vaart, Žiga Kokalj, Arianna Traviglia, Marco Fiorucci

When applying deep learning to remote sensing data in archaeological research, a notable obstacle is the limited availability of suitable datasets for training models.

Semantic Segmentation Transfer Learning

Relaxation Labeling Meets GANs: Solving Jigsaw Puzzles with Missing Borders

no code implementations28 Mar 2022 Marina Khoroshiltseva, Arianna Traviglia, Marcello Pelillo, Sebastiano Vascon

This paper proposes JiGAN, a GAN-based method for solving Jigsaw puzzles with eroded or missing borders.

Beam-Shape Effects and Noise Removal from THz Time-Domain Images in Reflection Geometry in the 0.25-6 THz Range

no code implementations1 Mar 2022 Marina Ljubenovic, Alessia Artesani, Stefano Bonetti, Arianna Traviglia

This mode is often the only one that can be effectively used in most cases, for example when analyzing objects that are either opaque in the THz range, or that cannot be displaced from their location (e. g., museums), such as those of cultural interest.

Deblurring Denoising +1

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