Zobrazeno 1 - 10
of 1 769
pro vyhledávání: '"Guarnera A"'
Autor:
Rondinella, Alessia, Guarnera, Francesco, Crispino, Elena, Russo, Giulia, Di Lorenzo, Clara, Maimone, Davide, Pappalardo, Francesco, Battiato, Sebastiano
This report summarizes the outcomes of the ICPR 2024 Competition on Multiple Sclerosis Lesion Segmentation (MSLesSeg). The competition aimed to develop methods capable of automatically segmenting multiple sclerosis lesions in MRI scans. Participants
Externí odkaz:
http://arxiv.org/abs/2410.07924
Autor:
Zappalà, Antonino, Guarnera, Luca, Rinaldi, Vincenzo, Livatino, Salvatore, Battiato, Sebastiano
The analysis of a crime scene is a pivotal activity in forensic investigations. Crime Scene Investigators and forensic science practitioners rely on best practices, standard operating procedures, and critical thinking, to produce rigorous scientific
Externí odkaz:
http://arxiv.org/abs/2409.18458
Autor:
Amerini, Irene, Barni, Mauro, Battiato, Sebastiano, Bestagini, Paolo, Boato, Giulia, Bonaventura, Tania Sari, Bruni, Vittoria, Caldelli, Roberto, De Natale, Francesco, De Nicola, Rocco, Guarnera, Luca, Mandelli, Sara, Marcialis, Gian Luca, Micheletto, Marco, Montibeller, Andrea, Orru', Giulia, Ortis, Alessandro, Perazzo, Pericle, Puglisi, Giovanni, Salvi, Davide, Tubaro, Stefano, Tonti, Claudia Melis, Villari, Massimo, Vitulano, Domenico
AI-generated synthetic media, also called Deepfakes, have significantly influenced so many domains, from entertainment to cybersecurity. Generative Adversarial Networks (GANs) and Diffusion Models (DMs) are the main frameworks used to create Deepfake
Externí odkaz:
http://arxiv.org/abs/2408.00388
Autor:
Fiorenza, Patrick, Zignale, Marco, Zanetti, Edoardo., Alessandrino, Mario S., Carbone, Beatrice, Guarnera, Alfio, Saggio, Mario, Giannazzo, Filippo, Roccaforte, Fabrizio
This paper reports the results presented in an invited poster during the International Conference on Silicon Carbide and Related Materials (ICSCRM) 2023 held in Sorrento, Italy. The suitability of scanning probe methods based on atomic force microsco
Externí odkaz:
http://arxiv.org/abs/2407.13370
Autor:
Litrico, Mattia, Guarnera, Francesco, Giuffirda, Valerio, Ravì, Daniele, Battiato, Sebastiano
Generating realistic images to accurately predict changes in the structure of brain MRI is a crucial tool for clinicians. Such applications help assess patients' outcomes and analyze how diseases progress at the individual level. However, existing me
Externí odkaz:
http://arxiv.org/abs/2406.12411
Autor:
Puglisi, Lemuel, Rondinella, Alessia, De Meo, Linda, Guarnera, Francesco, Battiato, Sebastiano, Ravì, Daniele
Brain age is a critical measure that reflects the biological ageing process of the brain. The gap between brain age and chronological age, referred to as brain PAD (Predicted Age Difference), has been utilized to investigate neurodegenerative conditi
Externí odkaz:
http://arxiv.org/abs/2406.00365
We introduce Reflectance Diffusion, a new neural text-to-texture model capable of generating high-fidelity SVBRDF maps from textual descriptions. Our method leverages a tandem neural approach, consisting of two modules, to accurately model the distri
Externí odkaz:
http://arxiv.org/abs/2406.14565
Deepfakes, synthetic images generated by deep learning algorithms, represent one of the biggest challenges in the field of Digital Forensics. The scientific community is working to develop approaches that can discriminate the origin of digital images
Externí odkaz:
http://arxiv.org/abs/2404.15697
{The study of frequency components derived from Discrete Cosine Transform (DCT) has been widely used in image analysis. In recent years it has been observed that significant information can be extrapolated from them about the lifecycle of the image,
Externí odkaz:
http://arxiv.org/abs/2403.14789
Publikováno v:
2024 IEEE International Conference on Image Processing (ICIP)
Deepfakes represent one of the toughest challenges in the world of Cybersecurity and Digital Forensics, especially considering the high-quality results obtained with recent generative AI-based solutions. Almost all generative models leave unique trac
Externí odkaz:
http://arxiv.org/abs/2402.02209