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239 results on '"Ortis, Alessandro"'

1. Deepfake Media Forensics: State of the Art and Challenges Ahead

Author

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, and Vitulano, Domenico

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

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 Deepfakes, producing highly realistic yet fabricated content. While these technologies open up new creative possibilities, they also bring substantial ethical and security risks due to their potential misuse. The rise of such advanced media has led to the development of a cognitive bias known as Impostor Bias, where individuals doubt the authenticity of multimedia due to the awareness of AI's capabilities. As a result, Deepfake detection has become a vital area of research, focusing on identifying subtle inconsistencies and artifacts with machine learning techniques, especially Convolutional Neural Networks (CNNs). Research in forensic Deepfake technology encompasses five main areas: detection, attribution and recognition, passive authentication, detection in realistic scenarios, and active authentication. This paper reviews the primary algorithms that address these challenges, examining their advantages, limitations, and future prospects.

Published
2024

2. MITS-GAN: Safeguarding Medical Imaging from Tampering with Generative Adversarial Networks

Author

Pasqualino, Giovanni, Guarnera, Luca, Ortis, Alessandro, and Battiato, Sebastiano

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

The progress in generative models, particularly Generative Adversarial Networks (GANs), opened new possibilities for image generation but raised concerns about potential malicious uses, especially in sensitive areas like medical imaging. This study introduces MITS-GAN, a novel approach to prevent tampering in medical images, with a specific focus on CT scans. The approach disrupts the output of the attacker's CT-GAN architecture by introducing finely tuned perturbations that are imperceptible to the human eye. Specifically, the proposed approach involves the introduction of appropriate Gaussian noise to the input as a protective measure against various attacks. Our method aims to enhance tamper resistance, comparing favorably to existing techniques. Experimental results on a CT scan demonstrate MITS-GAN's superior performance, emphasizing its ability to generate tamper-resistant images with negligible artifacts. As image tampering in medical domains poses life-threatening risks, our proactive approach contributes to the responsible and ethical use of generative models. This work provides a foundation for future research in countering cyber threats in medical imaging. Models and codes are publicly available on https://iplab.dmi.unict.it/MITS-GAN-2024/.

Published
2024

3. Deep Learning Algorithm for Advanced Level-3 Inverse-Modeling of Silicon-Carbide Power MOSFET Devices

Author

Spata, Massimo Orazio, Battiato, Sebastiano, Ortis, Alessandro, Rundo, Francesco, Calabretta, Michele, Pino, Carmelo, and Messina, Angelo

Subjects
Electrical Engineering and Systems Science - Signal Processing, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Inverse modelling with deep learning algorithms involves training deep architecture to predict device's parameters from its static behaviour. Inverse device modelling is suitable to reconstruct drifted physical parameters of devices temporally degraded or to retrieve physical configuration. There are many variables that can influence the performance of an inverse modelling method. In this work the authors propose a deep learning method trained for retrieving physical parameters of Level-3 model of Power Silicon-Carbide MOSFET (SiC Power MOS). The SiC devices are used in applications where classical silicon devices failed due to high-temperature or high switching capability. The key application of SiC power devices is in the automotive field (i.e. in the field of electrical vehicles). Due to physiological degradation or high-stressing environment, SiC Power MOS shows a significant drift of physical parameters which can be monitored by using inverse modelling. The aim of this work is to provide a possible deep learning-based solution for retrieving physical parameters of the SiC Power MOSFET. Preliminary results based on the retrieving of channel length of the device are reported. Channel length of power MOSFET is a key parameter involved in the static and dynamic behaviour of the device. The experimental results reported in this work confirmed the effectiveness of a multi-layer perceptron designed to retrieve this parameter., Comment: 13 pages, 8 figures, to be published on Journal of Physics: Conference Series

Published
2023

4. Boosting multiple sclerosis lesion segmentation through attention mechanism

Author

Rondinella, Alessia, Crispino, Elena, Guarnera, Francesco, Giudice, Oliver, Ortis, Alessandro, Russo, Giulia, Di Lorenzo, Clara, Maimone, Davide, Pappalardo, Francesco, and Battiato, Sebastiano

Subjects
Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Magnetic resonance imaging is a fundamental tool to reach a diagnosis of multiple sclerosis and monitoring its progression. Although several attempts have been made to segment multiple sclerosis lesions using artificial intelligence, fully automated analysis is not yet available. State-of-the-art methods rely on slight variations in segmentation architectures (e.g. U-Net, etc.). However, recent research has demonstrated how exploiting temporal-aware features and attention mechanisms can provide a significant boost to traditional architectures. This paper proposes a framework that exploits an augmented U-Net architecture with a convolutional long short-term memory layer and attention mechanism which is able to segment and quantify multiple sclerosis lesions detected in magnetic resonance images. Quantitative and qualitative evaluation on challenging examples demonstrated how the method outperforms previous state-of-the-art approaches, reporting an overall Dice score of 89% and also demonstrating robustness and generalization ability on never seen new test samples of a new dedicated under construction dataset.

Published
2023
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5. Early detection of hip periprosthetic joint infections through CNN on Computed Tomography images

Author

Guarnera, Francesco, Rondinella, Alessia, Giudice, Oliver, Ortis, Alessandro, Battiato, Sebastiano, Rundo, Francesco, Fallica, Giorgio, Traina, Francesco, and Conoci, Sabrina

Subjects
Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Early detection of an infection prior to prosthesis removal (e.g., hips, knees or other areas) would provide significant benefits to patients. Currently, the detection task is carried out only retrospectively with a limited number of methods relying on biometric or other medical data. The automatic detection of a periprosthetic joint infection from tomography imaging is a task never addressed before. This study introduces a novel method for early detection of the hip prosthesis infections analyzing Computed Tomography images. The proposed solution is based on a novel ResNeSt Convolutional Neural Network architecture trained on samples from more than 100 patients. The solution showed exceptional performance in detecting infections with an experimental high level of accuracy and F-score.

Published
2023
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6. UniCT DMI Solution for 3rd COV19D Competition on COVID-19 Detection through attention-based CNN for CT Scan

Author

Rondinella, Alessia, Guarnera, Francesco, Giudice, Oliver, Ortis, Alessandro, Rundo, Francesco, and Battiato, Sebastiano

Subjects
Electrical Engineering and Systems Science - Image and Video Processing
Abstract

This paper presents our solution for the first challenge of the 3rd Covid-19 competition, which is part of the "AI-enabled Medical Image Analysis Workshop" organized by IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP) 2023. Our proposed solution is based on a Resnet as a backbone network with the addition of attention mechanisms. The Resnet provides an effective feature extractor for the classification task, while the attention mechanisms improve the model's ability to focus on important regions of interest within the images. We conducted extensive experiments on the provided dataset and achieved promising results. Our proposed approach has the potential to assist in the accurate diagnosis of Covid-19 from chest computed tomography images, which can aid in the early detection and management of the disease.

Published
2023

7. Evaluation of CNNs for Wildcats Classification in Real World Scenario

Author

Fargetta, Georgia, Ortis, Alessandro, Anile, Stefano, Battiato, Sebastiano, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Garcia, Fausto P., editor, Jamil, Akhtar, editor, Hameed, Alaa Ali, editor, Ortis, Alessandro, editor, and Ramirez, Isaac Segovia, editor

Published
2024
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8. A Cross Platform Mobile Application for Indoor Navigation

Author

Spata, Massimo Orazio, Priano, Luigi, Ortis, Alessandro, Battiato, Sebastiano, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, and Arai, Kohei, editor

Published
2024
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9. A New Deep Learning Pipeline for Acoustic Attack on Keyboards

Author

Spata, Massimo Orazio, Ortis, Alessandro, Battiato, Sebastiano, Russo, Valerio Maria, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, and Arai, Kohei, editor

Published
2024
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10. Real-Time Multiclass Face Spoofing Recognition Through Spatiotemporal Convolutional 3D Features

Author

Giurato, Salvatore, Ortis, Alessandro, Battiato, Sebastiano, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Foresti, Gian Luca, editor, Fusiello, Andrea, editor, and Hancock, Edwin, editor

Published
2024
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11. E-ELPV: Extended ELPV Dataset for Accurate Solar Cells Defect Classification

Author

Grisanti, Marco, Spatafora, Maria Ausilia Napoli, Ortis, Alessandro, Battiato, Sebastiano, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, and Arai, Kohei, editor

Published
2024
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13. Pollen13K: A Large Scale Microscope Pollen Grain Image Dataset

Author

Battiato, Sebastiano, Ortis, Alessandro, Trenta, Francesca, Ascari, Lorenzo, Politi, Mara, and Siniscalco, Consolata

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Pollen grain classification has a remarkable role in many fields from medicine to biology and agronomy. Indeed, automatic pollen grain classification is an important task for all related applications and areas. This work presents the first large-scale pollen grain image dataset, including more than 13 thousands objects. After an introduction to the problem of pollen grain classification and its motivations, the paper focuses on the employed data acquisition steps, which include aerobiological sampling, microscope image acquisition, object detection, segmentation and labelling. Furthermore, a baseline experimental assessment for the task of pollen classification on the built dataset, together with discussion on the achieved results, is presented., Comment: This paper is a preprint of a paper accepted at the IEEE International Conference on Image Processing 2020

Published
2020

14. Survey on Visual Sentiment Analysis

Author

Ortis, Alessandro, Farinella, Giovanni Maria, and Battiato, Sebastiano

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Multimedia
Abstract

Visual Sentiment Analysis aims to understand how images affect people, in terms of evoked emotions. Although this field is rather new, a broad range of techniques have been developed for various data sources and problems, resulting in a large body of research. This paper reviews pertinent publications and tries to present an exhaustive overview of the field. After a description of the task and the related applications, the subject is tackled under different main headings. The paper also describes principles of design of general Visual Sentiment Analysis systems from three main points of view: emotional models, dataset definition, feature design. A formalization of the problem is discussed, considering different levels of granularity, as well as the components that can affect the sentiment toward an image in different ways. To this aim, this paper considers a structured formalization of the problem which is usually used for the analysis of text, and discusses it's suitability in the context of Visual Sentiment Analysis. The paper also includes a description of new challenges, the evaluation from the viewpoint of progress toward more sophisticated systems and related practical applications, as well as a summary of the insights resulting from this study., Comment: This paper is a postprint of a paper accepted by IET Image Processing and is subject to Institution of Engineering and Technology Copyright. When the final version is published, the copy of record will be available at the IET Digital Library

Published
2020
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15. Benchmarking of Blind Video Deblurring Methods on Long Exposure and Resource Poor Settings

Author

Napoli Spatafora, Maria Ausilia, Spata, Massimo O., Guarnera, Luca, Ortis, Alessandro, Battiato, Sebastiano, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Foresti, Gian Luca, editor, Fusiello, Andrea, editor, and Hancock, Edwin, editor

Published
2023
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16. GLR: Gradient-Based Learning Rate Scheduler

Author

Napoli Spatafora, Maria Ausilia, Ortis, Alessandro, Battiato, Sebastiano, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Foresti, Gian Luca, editor, Fusiello, Andrea, editor, and Hancock, Edwin, editor

Published
2023
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17. Mixup Data Augmentation for COVID-19 Infection Percentage Estimation

Author

Napoli Spatafora, Maria Ausilia, Ortis, Alessandro, Battiato, Sebastiano, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Mazzeo, Pier Luigi, editor, Frontoni, Emanuele, editor, Sclaroff, Stan, editor, and Distante, Cosimo, editor

Published
2022
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18. Recognition of Complex Gestures for Real-Time Emoji Assignment

Author

Zuccarà, Rosa, Ortis, Alessandro, Battiato, Sebastiano, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Mazzeo, Pier Luigi, editor, Frontoni, Emanuele, editor, Sclaroff, Stan, editor, and Distante, Cosimo, editor

Published
2022
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19. Fooling a Face Recognition System with a Marker-Free Label-Consistent Backdoor Attack

Author

Cauli, Nino, Ortis, Alessandro, Battiato, Sebastiano, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Sclaroff, Stan, editor, Distante, Cosimo, editor, Leo, Marco, editor, Farinella, Giovanni M., editor, and Tombari, Federico, editor

Published
2022
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20. User-Biased Food Recognition for Health Monitoring

Author

Hussain, Mazhar, Ortis, Alessandro, Polosa, Riccardo, Battiato, Sebastiano, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Sclaroff, Stan, editor, Distante, Cosimo, editor, Leo, Marco, editor, Farinella, Giovanni M., editor, and Tombari, Federico, editor

Published
2022
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21. Towards an Efficient Facial Image Compression with Neural Networks

Author

Napoli Spatafora, Maria Ausilia, Ortis, Alessandro, Battiato, Sebastiano, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Sclaroff, Stan, editor, Distante, Cosimo, editor, Leo, Marco, editor, Farinella, Giovanni M., editor, and Tombari, Federico, editor

Published
2022
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25. Fine-Grained Image Classification for Pollen Grain Microscope Images

Author

Trenta, Francesca, Ortis, Alessandro, Battiato, Sebastiano, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Tsapatsoulis, Nicolas, editor, Panayides, Andreas, editor, Theocharides, Theo, editor, Lanitis, Andreas, editor, Pattichis, Constantinos, editor, and Vento, Mario, editor

Published
2021
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26. Real-Time Thermal Face Identification System for Low Memory Vision Applications Using CNN

Author

Devaram, Rami Reddy, Ortis, Alessandro, Battiato, Sebastiano, Bruna, Arcangelo R., Tomaselli, Valeria, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Del Bimbo, Alberto, editor, Cucchiara, Rita, editor, Sclaroff, Stan, editor, Farinella, Giovanni Maria, editor, Mei, Tao, editor, Bertini, Marco, editor, Escalante, Hugo Jair, editor, and Vezzani, Roberto, editor

Published
2021
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27. Pollen Grain Classification Challenge 2020 : Challenge Report

Author

Battiato, Sebastiano, Guarnera, Francesco, Ortis, Alessandro, Trenta, Francesca, Ascari, Lorenzo, Siniscalco, Consolata, De Gregorio, Tommaso, Suárez, Eloy, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Del Bimbo, Alberto, editor, Cucchiara, Rita, editor, Sclaroff, Stan, editor, Farinella, Giovanni Maria, editor, Mei, Tao, editor, Bertini, Marco, editor, Escalante, Hugo Jair, editor, and Vezzani, Roberto, editor

Published
2021
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33. Nose-to-Brain Drug Delivery and Physico-Chemical Properties of Nanosystems: Analysis and Correlation Studies of Data from Scientific Literature

Author

Bonaccorso,Angela, Ortis,Alessandro, Musumeci,Teresa, Carbone,Claudia, Hussain,Mazhar, Di Salvatore,Valentina, Battiato,Sebastiano, Pappalardo,Francesco, Pignatello,Rosario, Bonaccorso,Angela, Ortis,Alessandro, Musumeci,Teresa, Carbone,Claudia, Hussain,Mazhar, Di Salvatore,Valentina, Battiato,Sebastiano, Pappalardo,Francesco, and Pignatello,Rosario

Abstract

Angela Bonaccorso,1,2,* Alessandro Ortis,3,* Teresa Musumeci,1,2 Claudia Carbone,1,2 Mazhar Hussain,3 Valentina Di Salvatore,1 Sebastiano Battiato,3 Francesco Pappalardo,1,2 Rosario Pignatello1,2 1Department of Drug and Health Sciences, University of Catania, Catania, Italy; 2NANOMED–Research Centre for Nanomedicine and Pharmaceutical Nanotechnology, University of Catania, Catania, 95125, Italy; 3Department of Mathematics and Computer Science, University of Catania, Catania, Italy*These authors contributed equally to this workCorrespondence: Teresa Musumeci, Email teresa.musumeci@unict.itBackground: In the last few decades, nose-to-brain delivery has been investigated as an alternative route to deliver molecules to the Central Nervous System (CNS), bypassing the Blood–Brain Barrier. The use of nanotechnological carriers to promote drug transfer via this route has been widely explored. The exact mechanisms of transport remain unclear because different pathways (systemic or axonal) may be involved. Despite the large number of studies in this field, various aspects still need to be addressed. For example, what physicochemical properties should a suitable carrier possess in order to achieve this goal? To determine the correlation between carrier features (eg, particle size and surface charge) and drug targeting efficiency percentage (DTE%) and direct transport percentage (DTP%), correlation studies were performed using machine learning.Methods: Detailed analysis of the literature from 2010 to 2021 was performed on Pubmed in order to build “NANOSE” database. Regression analyses have been applied to exploit machine-learning technology.Results: A total of 64 research articles were considered for building the NANOSE database (102 formulations). Particle-based formulations were characterized by an average size between 150– 200 nm and presented a negative zeta potential (ZP) from − 10 to − 25 mV. The most general-purpose model for the reg

Published
2024

34. COVID-19 Infection Percentage Estimation from Computed Tomography Scans: Results and Insights from the International Per-COVID-19 Challenge

Author

Ciencia de la computación e inteligencia artificial, Konputazio zientziak eta adimen artifiziala, Bougourzi, Fares, Distante, Cosimo, Dornaika, Fadi, Taleb-Ahmed, Abdelmalik, Hadid, Abdenour, Chaudhary, Suman, Yang, Wanting, Qiang, Yan, Anwar, Talha, Breaban, Mihaela Elena, Hsu, Chih-Chung, Tai, Shen-Chieh, Chen, Shao-Ning, Tricarico, Davide, Chaudhry, Hafiza Ayesha Hoor, Fiandrotti, Attilio, Grangetto, Marco, Spatafora, Maria Ausilia Napoli, Ortis, Alessandro, Battiato, Sebastiano, Ciencia de la computación e inteligencia artificial, Konputazio zientziak eta adimen artifiziala, Bougourzi, Fares, Distante, Cosimo, Dornaika, Fadi, Taleb-Ahmed, Abdelmalik, Hadid, Abdenour, Chaudhary, Suman, Yang, Wanting, Qiang, Yan, Anwar, Talha, Breaban, Mihaela Elena, Hsu, Chih-Chung, Tai, Shen-Chieh, Chen, Shao-Ning, Tricarico, Davide, Chaudhry, Hafiza Ayesha Hoor, Fiandrotti, Attilio, Grangetto, Marco, Spatafora, Maria Ausilia Napoli, Ortis, Alessandro, and Battiato, Sebastiano

Abstract

COVID-19 analysis from medical imaging is an important task that has been intensively studied in the last years due to the spread of the COVID-19 pandemic. In fact, medical imaging has often been used as a complementary or main tool to recognize the infected persons. On the other hand, medical imaging has the ability to provide more details about COVID-19 infection, including its severity and spread, which makes it possible to evaluate the infection and follow-up the patient’s state. CT scans are the most informative tool for COVID-19 infection, where the evaluation of COVID-19 infection is usually performed through infection segmentation. However, segmentation is a tedious task that requires much effort and time from expert radiologists. To deal with this limitation, an efficient framework for estimating COVID-19 infection as a regression task is proposed. The goal of the Per-COVID-19 challenge is to test the efficiency of modern deep learning methods on COVID-19 infection percentage estimation (CIPE) from CT scans. Participants had to develop an efficient deep learning approach that can learn from noisy data. In addition, participants had to cope with many challenges, including those related to COVID-19 infection complexity and crossdataset scenarios. This paper provides an overview of the COVID-19 infection percentage estimation challenge (Per-COVID-19) held at MIA-COVID-2022. Details of the competition data, challenges, and evaluation metrics are presented. The best performing approaches and their results are described and discussed.

Published
2024

35. Prediction of Social Image Popularity Dynamics

Author

Ortis, Alessandro, Farinella, Giovanni Maria, Battiato, Sebastiano, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Ricci, Elisa, editor, Rota Bulò, Samuel, editor, Snoek, Cees, editor, Lanz, Oswald, editor, Messelodi, Stefano, editor, and Sebe, Nicu, editor

Published
2019
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36. The Impact of Padding on Image Classification by Using Pre-trained Convolutional Neural Networks

Author

Tang, Hongxiang, Ortis, Alessandro, Battiato, Sebastiano, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Ricci, Elisa, editor, Rota Bulò, Samuel, editor, Snoek, Cees, editor, Lanz, Oswald, editor, Messelodi, Stefano, editor, and Sebe, Nicu, editor

Published
2019
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37. Generalised Gradient Vector Flow for Content-Aware Image Resizing

Author

Rotondo, Tiziana, Ortis, Alessandro, Battiato, Sebastiano, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Ricci, Elisa, editor, Rota Bulò, Samuel, editor, Snoek, Cees, editor, Lanz, Oswald, editor, Messelodi, Stefano, editor, and Sebe, Nicu, editor

Published
2019
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38. COVID-19 Infection Percentage Estimation from Computed Tomography Scans: Results and Insights from the International Per-COVID-19 Challenge

Author

Bougourzi, Fares, primary, Distante, Cosimo, additional, Dornaika, Fadi, additional, Taleb-Ahmed, Abdelmalik, additional, Hadid, Abdenour, additional, Chaudhary, Suman, additional, Yang, Wanting, additional, Qiang, Yan, additional, Anwar, Talha, additional, Breaban, Mihaela Elena, additional, Hsu, Chih-Chung, additional, Tai, Shen-Chieh, additional, Chen, Shao-Ning, additional, Tricarico, Davide, additional, Chaudhry, Hafiza Ayesha Hoor, additional, Fiandrotti, Attilio, additional, Grangetto, Marco, additional, Spatafora, Maria Ausilia Napoli, additional, Ortis, Alessandro, additional, and Battiato, Sebastiano, additional

Published
2024
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44. Pollen Grain Classification Challenge 2020

Author

Battiato, Sebastiano, primary, Guarnera, Francesco, additional, Ortis, Alessandro, additional, Trenta, Francesca, additional, Ascari, Lorenzo, additional, Siniscalco, Consolata, additional, De Gregorio, Tommaso, additional, and Suárez, Eloy, additional

Published
2021
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46. Organizing Videos Streams for Clustering and Estimation of Popular Scenes

Author

Battiato, Sebastiano, Farinella, Giovanni M., Milotta, Filippo L. M., Ortis, Alessandro, Stanco, Filippo, D’Amico, Valeria, Addesso, Luca, Torrisi, Giovanni, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Battiato, Sebastiano, editor, Gallo, Giovanni, editor, Schettini, Raimondo, editor, and Stanco, Filippo, editor

Published
2017
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47. A System for Autonomous Landing of a UAV on a Moving Vehicle

Author

Battiato, Sebastiano, Cantelli, Luciano, D’Urso, Fabio, Farinella, Giovanni Maria, Guarnera, Luca, Guastella, Dario, Melita, Carmelo Donato, Muscato, Giovanni, Ortis, Alessandro, Ragusa, Francesco, Santoro, Corrado, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Battiato, Sebastiano, editor, Gallo, Giovanni, editor, Schettini, Raimondo, editor, and Stanco, Filippo, editor

Published
2017
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