Search Results for author: Marco Fiorucci

Found 8 papers, 2 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.


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

Optimal Transport for Change Detection on LiDAR Point Clouds

1 code implementation14 Feb 2023 Marco Fiorucci, Peter Naylor, Makoto Yamada

The method is based on unbalanced optimal transport and can be generalised to any change detection problem with LiDAR data.

Change Detection Multi-class Classification +1

Regular Partitions and Their Use in Structural Pattern Recognition

no code implementations16 Sep 2019 Marco Fiorucci

This study provide us a principled way to develop a graph decomposition algorithm based on stochastic block model which is fitted using likelihood maximization.

Clustering Image Segmentation +3

On the Interplay between Strong Regularity and Graph Densification

no code implementations21 Mar 2017 Marco Fiorucci, Alessandro Torcinovich, Manuel Curado, Francisco Escolano, Marcello Pelillo

In this paper we analyze the practical implications of Szemer\'edi's regularity lemma in the preservation of metric information contained in large graphs.


Revealing Structure in Large Graphs: Szemerédi's Regularity Lemma and its Use in Pattern Recognition

no code implementations21 Sep 2016 Marcello Pelillo, Ismail Elezi, Marco Fiorucci

Introduced in the mid-1970's as an intermediate step in proving a long-standing conjecture on arithmetic progressions, Szemer\'edi's regularity lemma has emerged over time as a fundamental tool in different branches of graph theory, combinatorics and theoretical computer science.


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