2025
Other
Open Access
SI-Lab Annual Research Report 2024
Awais Ch Muhammad, Benassi A., Berti A., Bertini G., Buongiorno R., Cafiso M., Carboni A., Carloni G., Caudai C., Colantonio S., Conti F., Daoudagh S., Del Corso G., Fusco G., Galesi G., Germanese D., Gravili S., Ignesti G., Kuruoglu E. E., Lazzini G., Leone G. R., Leporini B., Magrini M., Martinelli M., Omrani A. R., Pachetti E., Papini O., Paradisi P., Pardini F., Pascali M. A., Pieri G., Reggiannini M., Righi M., Salerno E., Salvetti O., Scozzari A., Sebastiani L., Straface S., Tampucci M., Tarabella L., Tonazzini A., Moroni D.
The Signal & Images Laboratory (SI-Lab) is an interdisciplinary research group in computer vision, signal analysis, intelligent vision systems and multimedia data understanding. It is part of the Institute of Information Science and Technologies (ISTI) of the National Research Council of Italy (CNR). This report accounts for the research activities of the Signal and Images Laboratory of the Institute of Information Science and Technologies during the year 2024.DOI: 10.32079/isti-ar-2025/002
Metrics:
Awais Ch Muhammad, Benassi A., Berti A., Bertini G., Buongiorno R., Cafiso M., Carboni A., Carloni G., Caudai C., Colantonio S., Conti F., Daoudagh S., Del Corso G., Fusco G., Galesi G., Germanese D., Gravili S., Ignesti G., Kuruoglu E. E., Lazzini G., Leone G. R., Leporini B., Magrini M., Martinelli M., Omrani A. R., Pachetti E., Papini O., Paradisi P., Pardini F., Pascali M. A., Pieri G., Reggiannini M., Righi M., Salerno E., Salvetti O., Scozzari A., Sebastiani L., Straface S., Tampucci M., Tarabella L., Tonazzini A., Moroni D.
The Signal & Images Laboratory (SI-Lab) is an interdisciplinary research group in computer vision, signal analysis, intelligent vision systems and multimedia data understanding. It is part of the Institute of Information Science and Technologies (ISTI) of the National Research Council of Italy (CNR). This report accounts for the research activities of the Signal and Images Laboratory of the Institute of Information Science and Technologies during the year 2024.DOI: 10.32079/isti-ar-2025/002
Metrics:
2025
Journal article
Open Access
Machine learning to detect vocal stereotypy: improving duration-based measures
Omrani A. R., Lanovaz M. J., Moroni D.
Direct observation is a process central to behavior science, but its implementation may be challenging in some contexts (e.g., classrooms, homes). One potential solution to improve the feasibility of conducting behavioral observation and measurement involves machine learning. Using previously published data, we developed and tested novel models to automatically measure the duration of vocal stereotypy in eight children with autism. In addition to accuracy and the kappa statistic, we examined session-by-session correlations between values measured by machine learning and those recorded by a human observer. Nearly all our models produced high correlations (i.e., .90 or more) and resulted in better metrics than those reported by the original study. The next step is for researchers to test the models on novel datasets to examine the generalizability of our findings.Source: BEHAVIOR MODIFICATION
DOI: 10.1177/01454455251380510
Metrics:
Omrani A. R., Lanovaz M. J., Moroni D.
Direct observation is a process central to behavior science, but its implementation may be challenging in some contexts (e.g., classrooms, homes). One potential solution to improve the feasibility of conducting behavioral observation and measurement involves machine learning. Using previously published data, we developed and tested novel models to automatically measure the duration of vocal stereotypy in eight children with autism. In addition to accuracy and the kappa statistic, we examined session-by-session correlations between values measured by machine learning and those recorded by a human observer. Nearly all our models produced high correlations (i.e., .90 or more) and resulted in better metrics than those reported by the original study. The next step is for researchers to test the models on novel datasets to examine the generalizability of our findings.Source: BEHAVIOR MODIFICATION
DOI: 10.1177/01454455251380510
Metrics:
See at:
CNR IRIS 
| journals.sagepub.com | CNR IRIS 
2025
Journal article
Open Access
The 18th International Workshop on Advanced Infrared Technology and Applications (AITA 2025)
Sakagami Takahide, Cadelano Gianluca, D'Acunto Mario, Ferrarini Giovanni, Maldague Xavier, Martyniuk Piotr, Moroni Davide, Raimondi Valentina, Strojnik Marija, Ihara Ikuo, Inoue Hirotsugu, Ooka Norikazu, Ogata Takamasa
First held in 1992, AITA is an international conference that aims to present state-of-the-art research and recent applications in the field of infrared spectral range technology. The AITA workshop has been consistently supported and organized by the following organizations: Fondazione “Giorgio Ronchi”, the Istituto di Fisica Applicata “Nello Carrara” (CNR-IFAC), the Istituto per le Tecnologie della Costruzione (CNR-ITC), the Istituto di Scienza e Tecnologie dell’Informazione “Alessandro Faedo” (CNR-ISTI), the Istituto di Biofisica (CNR-IBF), the Istituto di Scienze dell’Atmosfera e del Clima (CNR-ISAC) and the Politecnico di Torino. The 18th International Workshop on Advanced Infrared Technology and Applications (AITA 2025), held in Kobe, Japan, was organized by the Japanese Society for Non-Destructive Inspection (JSNDI) and co-organized by Kobe University.Source: PROCEEDINGS, vol. 129 (issue 1)
DOI: 10.3390/proceedings2025129078
Metrics:
Sakagami Takahide, Cadelano Gianluca, D'Acunto Mario, Ferrarini Giovanni, Maldague Xavier, Martyniuk Piotr, Moroni Davide, Raimondi Valentina, Strojnik Marija, Ihara Ikuo, Inoue Hirotsugu, Ooka Norikazu, Ogata Takamasa
First held in 1992, AITA is an international conference that aims to present state-of-the-art research and recent applications in the field of infrared spectral range technology. The AITA workshop has been consistently supported and organized by the following organizations: Fondazione “Giorgio Ronchi”, the Istituto di Fisica Applicata “Nello Carrara” (CNR-IFAC), the Istituto per le Tecnologie della Costruzione (CNR-ITC), the Istituto di Scienza e Tecnologie dell’Informazione “Alessandro Faedo” (CNR-ISTI), the Istituto di Biofisica (CNR-IBF), the Istituto di Scienze dell’Atmosfera e del Clima (CNR-ISAC) and the Politecnico di Torino. The 18th International Workshop on Advanced Infrared Technology and Applications (AITA 2025), held in Kobe, Japan, was organized by the Japanese Society for Non-Destructive Inspection (JSNDI) and co-organized by Kobe University.Source: PROCEEDINGS, vol. 129 (issue 1)
DOI: 10.3390/proceedings2025129078
Metrics:
See at:
CNR IRIS | www.mdpi.com | CNR IRIS
2025
Other
Open Access
Progettazione di codici 2D per la tracciabilità circolare di cavi
Cuttano M. -L., Marrazzini L., Del Corso G., Moroni D.
Questa relazione descrive l’attività di tirocinio svolta presso il “Laboratorio Segnali e Immagini” dell’Istituto di Scienza e Tecnologia dell’Informazione “A. Faedo” CNR-ISTI su “CircularEconomyCable”, progetto condotto dall’azienda produttrice di cavi “Tratos S.p.A.”. Il progetto, della durata prevista di due anni, mira allo sviluppo e prototipazione di innovativi cavi elettrici e in fibra ottica ad elevata sostenibilità, tracciabilità e visibilità, per consentire la piena attuazione dei principi dell’economia circolare e della transizione digitale nei settori del trasporto energia e della connettività dati.
Cuttano M. -L., Marrazzini L., Del Corso G., Moroni D.
Questa relazione descrive l’attività di tirocinio svolta presso il “Laboratorio Segnali e Immagini” dell’Istituto di Scienza e Tecnologia dell’Informazione “A. Faedo” CNR-ISTI su “CircularEconomyCable”, progetto condotto dall’azienda produttrice di cavi “Tratos S.p.A.”. Il progetto, della durata prevista di due anni, mira allo sviluppo e prototipazione di innovativi cavi elettrici e in fibra ottica ad elevata sostenibilità, tracciabilità e visibilità, per consentire la piena attuazione dei principi dell’economia circolare e della transizione digitale nei settori del trasporto energia e della connettività dati.
2025
Journal article
Metadata Only Access
Virtual, augmented and mixed reality for motor neurorehabilitation: a scoping review focused on the role of body representation
Massimo Magrini, Olivia Curzio, Cristina Dolciotti, Gabriele Donzelli, Maria Cristina Imiotti, Fabrizio Minichilli, Davide Moroni, Paolo Bongioanni
Background: In neurorehabilitation, virtual reality (VR) applications cover a wide range of areas, including the rehabilitation of patients with various types of brain and spinal cord injuries. VR provides the subject multisensory feedback, enhancing neuronal plasticity within the sensorimotor cortex. Objective: The systematic review critically analyses the existing literature on VR applications related to motor problems and somatic representation to propose new tools and experiments. Methods: The Protocol was registered in the international database for systematic reviews PROSPERO (ID: 481092 - 22 November 2023). The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Guidelines. To implement the search string, a broad overview of previous literature reviews in the field was developed. The databases PubMed, Embase, Scopus, and Web of Science (7 December 2023) were explored, and data regarding study design, methodology, participant characteristics, specific devices and instruments used and tested, body representation, and virtual somatic embodiment were collected. The Newcastle-Ottawa Scale was used to assess the methodological quality of the studies; for case report studies, a dedicated scale was used. Results: The review included 26 studies, mainly clinical trials on neurological patients. Internationally, VR technologies in the period 2008-2023 have evolved significantly; the emergence of inexpensive devices such as Oculus Rift and HTC Vive has stimulated research in this area. The best results have been achieved for patients with sensorimotor deficits. In VR systems, users experience a first- or third-person view (where their avatar is present) of the synthetic world around them. All included studies used the first-person perspective, which was found to be most effective. Five studies incorporated EEG for recording brain responses during experiments, while two studies used transcranial stimulators to enhance the effect of the VR intervention. A couple of studies employed other kinds of devices, such as eye trackers. Regarding the 3D engine used, Unity 3D remains the preferred choice for the development of VR applications in research due to its ease of learning and seamless integration with devices. Conclusions: The review of the selected studies shows that the use of VR devices enhances reinforcement learning, thereby improving motor and cognitive recovery. The emerging operational proposition supports the use of tailor-made techniques in the rehabilitation setting - aimed at improving and evaluating the outcomes of therapeutic interventions in the treatment of neurological patients. Clinical Trial: International database for systematic reviews PROSPERO, ID: 481092 - 22 November 2023.Source: JMIR XR and Spatial Computing
DOI: 10.2196/63487
Metrics:
Massimo Magrini, Olivia Curzio, Cristina Dolciotti, Gabriele Donzelli, Maria Cristina Imiotti, Fabrizio Minichilli, Davide Moroni, Paolo Bongioanni
Background: In neurorehabilitation, virtual reality (VR) applications cover a wide range of areas, including the rehabilitation of patients with various types of brain and spinal cord injuries. VR provides the subject multisensory feedback, enhancing neuronal plasticity within the sensorimotor cortex. Objective: The systematic review critically analyses the existing literature on VR applications related to motor problems and somatic representation to propose new tools and experiments. Methods: The Protocol was registered in the international database for systematic reviews PROSPERO (ID: 481092 - 22 November 2023). The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Guidelines. To implement the search string, a broad overview of previous literature reviews in the field was developed. The databases PubMed, Embase, Scopus, and Web of Science (7 December 2023) were explored, and data regarding study design, methodology, participant characteristics, specific devices and instruments used and tested, body representation, and virtual somatic embodiment were collected. The Newcastle-Ottawa Scale was used to assess the methodological quality of the studies; for case report studies, a dedicated scale was used. Results: The review included 26 studies, mainly clinical trials on neurological patients. Internationally, VR technologies in the period 2008-2023 have evolved significantly; the emergence of inexpensive devices such as Oculus Rift and HTC Vive has stimulated research in this area. The best results have been achieved for patients with sensorimotor deficits. In VR systems, users experience a first- or third-person view (where their avatar is present) of the synthetic world around them. All included studies used the first-person perspective, which was found to be most effective. Five studies incorporated EEG for recording brain responses during experiments, while two studies used transcranial stimulators to enhance the effect of the VR intervention. A couple of studies employed other kinds of devices, such as eye trackers. Regarding the 3D engine used, Unity 3D remains the preferred choice for the development of VR applications in research due to its ease of learning and seamless integration with devices. Conclusions: The review of the selected studies shows that the use of VR devices enhances reinforcement learning, thereby improving motor and cognitive recovery. The emerging operational proposition supports the use of tailor-made techniques in the rehabilitation setting - aimed at improving and evaluating the outcomes of therapeutic interventions in the treatment of neurological patients. Clinical Trial: International database for systematic reviews PROSPERO, ID: 481092 - 22 November 2023.Source: JMIR XR and Spatial Computing
DOI: 10.2196/63487
Metrics:
See at:
CNR IRIS
2025
Other
Open Access
ISTI-day 2025 Proceedings
Del Corso G., Pedrotti A., Federico G., Gennaro C., Carrara F., Amato G., Di Benedetto M., Gabrielli E., Belli D., Matrullo Z., Miori V., Tolomei G., Waheed T., Marchetti E., Calabrò A., Rossetti G., Stella M., Cazabet R., Abramski K., Cau E., Citraro S., Failla A., Mesina V., Morini V., Pansanella V., Colantonio S., Germanese D., Pascali M. A., Bianchi L., Messina N., Falchi F., Barsellotti L., Pacini G., Cassese M., Puccetti G., Esuli A., Volpi L., Moreo A., Sebastiani F., Sperduti G., Nguyen D., Broccia G., Ter Beek M. H., Ferrari A., Massink M., Belmonte G., Ciancia V., Papini O., Canapa G., Catricalà B., Manca M., Paternò F., Santoro C., Zedda E., Gallo S., Maenza S., Mattioli A., Simeoli L., Rucci D., Carlini E., Dazzi P., Kavalionak H., Mordacchini M., Rulli C., Muntean Cristina Ioana, Nardini F. M., Perego R., Rocchietti G., Lettich F., Renso C., Pugliese C., Casini G., Haldimann J., Meyer T., Assante M., Candela L., Dell'Amico A., Frosini L., Mangiacrapa F., Oliviero A., Pagano P., Panichi G., Peccerillo B., Procaccini M., Mannocci A., Manghi P., Lonetti F., Kang D., Di Giandomenico F., Jee E., Lazzini G., Conti F., Scopigno R., D'Acunto M., Moroni D., Cafiso M., Paradisi P., Callieri M., Pavoni G., Corsini M., De Falco A., Sala F., Saraceni Q., Gattiglia G.
ISTI-Day is an annual information and networking event organized by the Institute of Information Science and Technologies "A. Faedo" (ISTI) of the Italian National Research Council (CNR). This event features an opening talk of the Director of the Dept. DIITET (Emilio F. Campana) as well as an overview of the Institute's activities presented by the ISTI Director (Roberto Scopigno). Those institutional segments are complemented by dedicated presentations and round tables featuring former staff members, as well as internal and external collaborators. To foster a network of knowledge and collaboration among newcomers, the 2025 ISTI Day edition also includes a large poster session that provides a comprehensive overview of current research activities. Each of the 13 laboratories contributes 1–3 posters, highlighting the most innovative work and offering early-career researchers a platform for discussion. Thus these proceedings include the posters selected for ISTI-Day 2025, reflecting the diverse and innovative nature of the Institute's research.
Del Corso G., Pedrotti A., Federico G., Gennaro C., Carrara F., Amato G., Di Benedetto M., Gabrielli E., Belli D., Matrullo Z., Miori V., Tolomei G., Waheed T., Marchetti E., Calabrò A., Rossetti G., Stella M., Cazabet R., Abramski K., Cau E., Citraro S., Failla A., Mesina V., Morini V., Pansanella V., Colantonio S., Germanese D., Pascali M. A., Bianchi L., Messina N., Falchi F., Barsellotti L., Pacini G., Cassese M., Puccetti G., Esuli A., Volpi L., Moreo A., Sebastiani F., Sperduti G., Nguyen D., Broccia G., Ter Beek M. H., Ferrari A., Massink M., Belmonte G., Ciancia V., Papini O., Canapa G., Catricalà B., Manca M., Paternò F., Santoro C., Zedda E., Gallo S., Maenza S., Mattioli A., Simeoli L., Rucci D., Carlini E., Dazzi P., Kavalionak H., Mordacchini M., Rulli C., Muntean Cristina Ioana, Nardini F. M., Perego R., Rocchietti G., Lettich F., Renso C., Pugliese C., Casini G., Haldimann J., Meyer T., Assante M., Candela L., Dell'Amico A., Frosini L., Mangiacrapa F., Oliviero A., Pagano P., Panichi G., Peccerillo B., Procaccini M., Mannocci A., Manghi P., Lonetti F., Kang D., Di Giandomenico F., Jee E., Lazzini G., Conti F., Scopigno R., D'Acunto M., Moroni D., Cafiso M., Paradisi P., Callieri M., Pavoni G., Corsini M., De Falco A., Sala F., Saraceni Q., Gattiglia G.
ISTI-Day is an annual information and networking event organized by the Institute of Information Science and Technologies "A. Faedo" (ISTI) of the Italian National Research Council (CNR). This event features an opening talk of the Director of the Dept. DIITET (Emilio F. Campana) as well as an overview of the Institute's activities presented by the ISTI Director (Roberto Scopigno). Those institutional segments are complemented by dedicated presentations and round tables featuring former staff members, as well as internal and external collaborators. To foster a network of knowledge and collaboration among newcomers, the 2025 ISTI Day edition also includes a large poster session that provides a comprehensive overview of current research activities. Each of the 13 laboratories contributes 1–3 posters, highlighting the most innovative work and offering early-career researchers a platform for discussion. Thus these proceedings include the posters selected for ISTI-Day 2025, reflecting the diverse and innovative nature of the Institute's research.
See at:
CNR IRIS | www.isti.cnr.it | CNR IRIS
2025
Conference article
Open Access
Towards trustworthy AI in the public transport domain
Leone G. R., Carboni A., Del Corso G., Gravili S., Moroni D., Pascali M. A., Colantonio S.
In the context of rapidly evolving urban landscapes, the demand for enhanced mobility services has become increasingly critical. Traditional transportation systems struggle to keep pace with the growing complexity of commuting patterns and the diverse needs of urban residents. While AI can play a strong role in addressing these emerging demands, a parallel need for trustworthy services is also arising, which must be adequately met to ultimately provide equitable and ethical services to society. Based on these considerations, we explore the relevant dimensions of AI trustworthiness and propose how they can be transferred and demonstrated in a large-scale pilot focused on public transportation and exploiting advanced visual analytics paradigms based on pervasive computing. To this end, we present the FAITH risk management framework, ongoing activities, and preliminary results towards its implementation in the pilot project.Source: CEUR WORKSHOP PROCEEDINGS, vol. 4121. Trieste, Italy, 23-24/06/2025
Leone G. R., Carboni A., Del Corso G., Gravili S., Moroni D., Pascali M. A., Colantonio S.
In the context of rapidly evolving urban landscapes, the demand for enhanced mobility services has become increasingly critical. Traditional transportation systems struggle to keep pace with the growing complexity of commuting patterns and the diverse needs of urban residents. While AI can play a strong role in addressing these emerging demands, a parallel need for trustworthy services is also arising, which must be adequately met to ultimately provide equitable and ethical services to society. Based on these considerations, we explore the relevant dimensions of AI trustworthiness and propose how they can be transferred and demonstrated in a large-scale pilot focused on public transportation and exploiting advanced visual analytics paradigms based on pervasive computing. To this end, we present the FAITH risk management framework, ongoing activities, and preliminary results towards its implementation in the pilot project.Source: CEUR WORKSHOP PROCEEDINGS, vol. 4121. Trieste, Italy, 23-24/06/2025
See at:
ceur-ws.org | CNR IRIS | CNR IRIS
2025
Other
Open Access
Redundant 2D Code: generation of custom bidimensional codes on the Reed-Solomon error correction algorithm
Cuttano M. L., Del Corso G., Moroni D.
This technical report details Redundant 2D Code, an open-source software written in Python that generates rectangular binary matrices. It allows for great flexibility in terms of the dimensions, number, and shape of positional markers, which are fundamental to the reading phase. Similarly to existing QR-codes, the customized code is built using state-of-the-art the Reed- Solomon error correction algorithm to make it more resistant to damages and information loss. However, this Python package enables full customization of redundancy and shape characteristics, providing flexible tools applicable across multiple domains, particularly in industrial settings, where a one-size-fits-all approach is often unsuitable due to product variability.DOI: 10.32079/isti-tr-2025/016
Metrics:
Cuttano M. L., Del Corso G., Moroni D.
This technical report details Redundant 2D Code, an open-source software written in Python that generates rectangular binary matrices. It allows for great flexibility in terms of the dimensions, number, and shape of positional markers, which are fundamental to the reading phase. Similarly to existing QR-codes, the customized code is built using state-of-the-art the Reed- Solomon error correction algorithm to make it more resistant to damages and information loss. However, this Python package enables full customization of redundancy and shape characteristics, providing flexible tools applicable across multiple domains, particularly in industrial settings, where a one-size-fits-all approach is often unsuitable due to product variability.DOI: 10.32079/isti-tr-2025/016
Metrics:
2025
Conference article
Restricted
Image quality vs performance in super-resolution for SAR ship classification
Awais Ch Muhammad, Reggiannini M., Moroni D.
Synthetic Aperture Radar (SAR) images for ship classification often face the problem of low resolution. Techniques like super-resolution (SR) can help to enhance the images for better ship classification. In this paper, we compared traditional interpolation techniques (bilinear, bicubic, Lanczos, nearest-neighbor) with deep learning SR methods (EDSR, RCAN, CARN) at 2x and 4x resolutions to analyze their effect in terms of image quality and classification performance. The image quality was assessed using metrics like Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM). The findings indicate that while 2x resolution images typically achieved higher image quality scores, the 4x images often performed equally well or better in classification tasks. We utilized two versions of VGG: SR techniques yielded similar scores with a simple VGG, whereas, in the multi-scale VGG (MSVGG), traditional interpolation methods outperformed deep learning methods. Experiments confirm that super-resolved images reach high scores in terms of classical image quality metrics. However, this does not always translate directly into improved performance in SAR ship classification. This highlights the need to select SR techniques by jointly evaluating image quality metrics and classification performance.DOI: 10.1109/iscas56072.2025.11043629
Project(s): National Biodiversity Future Center
Metrics:
Awais Ch Muhammad, Reggiannini M., Moroni D.
Synthetic Aperture Radar (SAR) images for ship classification often face the problem of low resolution. Techniques like super-resolution (SR) can help to enhance the images for better ship classification. In this paper, we compared traditional interpolation techniques (bilinear, bicubic, Lanczos, nearest-neighbor) with deep learning SR methods (EDSR, RCAN, CARN) at 2x and 4x resolutions to analyze their effect in terms of image quality and classification performance. The image quality was assessed using metrics like Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM). The findings indicate that while 2x resolution images typically achieved higher image quality scores, the 4x images often performed equally well or better in classification tasks. We utilized two versions of VGG: SR techniques yielded similar scores with a simple VGG, whereas, in the multi-scale VGG (MSVGG), traditional interpolation methods outperformed deep learning methods. Experiments confirm that super-resolved images reach high scores in terms of classical image quality metrics. However, this does not always translate directly into improved performance in SAR ship classification. This highlights the need to select SR techniques by jointly evaluating image quality metrics and classification performance.DOI: 10.1109/iscas56072.2025.11043629
Project(s): National Biodiversity Future Center
Metrics:
See at:
CNR IRIS | ieeexplore.ieee.org | CNR IRIS
2025
Conference article
Restricted
Improving weed control efficiency in maize fields: a methodological approach to site-specific weed management
Ercolini L., Grossi N., Martinelli M., Moroni D., Berton A., Silvestri N.
Methodologies to enhance precision agriculture.
Ercolini L., Grossi N., Martinelli M., Moroni D., Berton A., Silvestri N.
Methodologies to enhance precision agriculture.
See at:
ecpa2025.upc.edu | CNR IRIS | CNR IRIS
2025
Book
Open Access
Guest Editorial: New Frontiers in Image and Video Processing for Sustainable Agriculture
Moroni D., Kosmopoulos D.
The rapidly evolving landscape of image processing, with the integration of cutting-edge technologies such as deep learning, has expanded its influence across various sectors. Agriculture, being a pillar of sustainable development, is on the cusp of a major technological transformation, necessitating the synergy of advanced sensors, image processing and machine learning. Recognizing the symbiotic relationship between image processing advancements and the agricultural domain's intrinsic challenges, this special issue aims to bring to the fore the innovative applications of advanced image processing methodologies in agriculture to enable sustainable production. The focus is not only on addressing agricultural challenges but also on unraveling new research trajectories in image processing that could ripple into other sectors like remote sensing, robotics and photogrammetry. The current special issue is aligned with the Sustainable Development Goals outlined in the 2030 agenda for sustainable development. Conversely, the agricultural domain provides a fertile ground for research challenges that motivate the exploration of new avenues.Source: IET IMAGE PROCESSING, vol. 19 (issue 1)
DOI: 10.1049/ipr2.70032
Metrics:
Moroni D., Kosmopoulos D.
The rapidly evolving landscape of image processing, with the integration of cutting-edge technologies such as deep learning, has expanded its influence across various sectors. Agriculture, being a pillar of sustainable development, is on the cusp of a major technological transformation, necessitating the synergy of advanced sensors, image processing and machine learning. Recognizing the symbiotic relationship between image processing advancements and the agricultural domain's intrinsic challenges, this special issue aims to bring to the fore the innovative applications of advanced image processing methodologies in agriculture to enable sustainable production. The focus is not only on addressing agricultural challenges but also on unraveling new research trajectories in image processing that could ripple into other sectors like remote sensing, robotics and photogrammetry. The current special issue is aligned with the Sustainable Development Goals outlined in the 2030 agenda for sustainable development. Conversely, the agricultural domain provides a fertile ground for research challenges that motivate the exploration of new avenues.Source: IET IMAGE PROCESSING, vol. 19 (issue 1)
DOI: 10.1049/ipr2.70032
Metrics:
See at:
IET Image Processing | CNR IRIS | ietresearch.onlinelibrary.wiley.com | CNR IRIS
2025
Other
Open Access
Batch-CAM: introduction to better reasoning in convolutional deep learning models
Ignesti G., Moroni D., Martinelli M.
Understanding the inner workings of deep learning models is crucial for advancing artificial intelligence, particularly in high-stakes fields such as healthcare, where accurate explanations are as vital as precision. This paper introduces Batch- CAM, a novel training paradigm that fuses a batch implementation of the Grad- CAM algorithm with a prototypical reconstruction loss. This combination guides the model to focus on salient image features, thereby enhancing its performance across classification tasks. Our results demonstrate that Batch-CAM achieves a simultaneous improvement in accuracy and image reconstruction quality while reducing training and inference times. By ensuring models learn from evidence- relevant information, this approach makes a relevant contribution to building more transparent, explainable, and trustworthy AI systems.DOI: 10.48550/arxiv.2510.00664
Metrics:
Ignesti G., Moroni D., Martinelli M.
Understanding the inner workings of deep learning models is crucial for advancing artificial intelligence, particularly in high-stakes fields such as healthcare, where accurate explanations are as vital as precision. This paper introduces Batch- CAM, a novel training paradigm that fuses a batch implementation of the Grad- CAM algorithm with a prototypical reconstruction loss. This combination guides the model to focus on salient image features, thereby enhancing its performance across classification tasks. Our results demonstrate that Batch-CAM achieves a simultaneous improvement in accuracy and image reconstruction quality while reducing training and inference times. By ensuring models learn from evidence- relevant information, this approach makes a relevant contribution to building more transparent, explainable, and trustworthy AI systems.DOI: 10.48550/arxiv.2510.00664
Metrics:
2025
Journal article
Open Access
Towards the development of explainable machine learning models to recognize the faces of autistic children: a brief report
Omrani Ali R., Lanovaz M. J., Moroni D.
Purpose Machine learning with image classification has shown promise in supporting the detection of autism in children, but the development of explainable models is still lacking. To address this issue, the purpose of this study was to compare the development of explainable models using two different algorithms to identify the facial features that deep neural networks used to classify children as autistic or non-autistic. Design/methodology/approach First, this paper trained and tested different models on the Autistic Children Facial Image Data Set and selected the one that produced the highest accuracy. Following the identification of the best model, the analyses compared two methods to examine explainability: Local Interpretable Model-agnostic Explanations and Randomized Input Sampling for Explanation of black-box models. Findings Overall, the best model, ViT_Huge_14, produced an accuracy of 92%. Moreover, Local Interpretable Model-agnostic Explanations resulted in more explainable models than Randomized Input Sampling for Explanation of black-box models. Albeit promising, researchers must conduct further studies to examine the generalizability of the results and consider ethical issues before recommending facial image classification as a component of a multimethod approach to screening and diagnosis. Originality/value To the best of the authors' knowledge, this study is the first to examine the development of explainable models to detect autism using facial features.Source: ADVANCES IN AUTISM, vol. 11 (issue 4), pp. 283-289
DOI: 10.1108/aia-02-2025-0018
Metrics:
Omrani Ali R., Lanovaz M. J., Moroni D.
Purpose Machine learning with image classification has shown promise in supporting the detection of autism in children, but the development of explainable models is still lacking. To address this issue, the purpose of this study was to compare the development of explainable models using two different algorithms to identify the facial features that deep neural networks used to classify children as autistic or non-autistic. Design/methodology/approach First, this paper trained and tested different models on the Autistic Children Facial Image Data Set and selected the one that produced the highest accuracy. Following the identification of the best model, the analyses compared two methods to examine explainability: Local Interpretable Model-agnostic Explanations and Randomized Input Sampling for Explanation of black-box models. Findings Overall, the best model, ViT_Huge_14, produced an accuracy of 92%. Moreover, Local Interpretable Model-agnostic Explanations resulted in more explainable models than Randomized Input Sampling for Explanation of black-box models. Albeit promising, researchers must conduct further studies to examine the generalizability of the results and consider ethical issues before recommending facial image classification as a component of a multimethod approach to screening and diagnosis. Originality/value To the best of the authors' knowledge, this study is the first to examine the development of explainable models to detect autism using facial features.Source: ADVANCES IN AUTISM, vol. 11 (issue 4), pp. 283-289
DOI: 10.1108/aia-02-2025-0018
Metrics:
See at:
CNR IRIS | www.emerald.com | CNR IRIS
2025
Journal article
Open Access
Topological machine learning for discriminative spectral band identification in raman spectroscopy of pathological samples
Conti F., Moroni D., Pascali M. A.
In the field of Raman spectroscopy (RS), particularly when working with biological samples, identifying the chemical compounds most involved in specific pathologies is of critical importance for pathologists. The correlation between chemical substances present in biological tissue and pathology can contribute not only to a deeper understanding of the disease itself but also to the development of novel artificial intelligence-based diagnostic methodologies. Motivated by these clinical challenges, we propose a method to identify the most discriminative spectral bands by leveraging the synergy between Topological Machine Learning (TML) and Raman spectroscopy. The intrinsic explainability of part of the TML pipeline can indeed play a key role in the detection of such spectral bands, e.g., the proteins most associated with the disease. In order to evaluate the performance of our method, we apply it to three case studies: the RS of biological tissue related to the chondrogenic bone tumors, the RS of cerebrospinal fluid associated with Alzheimer’s disease and the RS of pancreatic tissue. The results obtained with our method are promising in pinpointing which spectral bands are most relevant for diagnosis, but they also highlight the need for further investigation.Source: PROCEEDINGS, vol. 129 (issue 1)
DOI: 10.3390/proceedings2025129053
Metrics:
Conti F., Moroni D., Pascali M. A.
In the field of Raman spectroscopy (RS), particularly when working with biological samples, identifying the chemical compounds most involved in specific pathologies is of critical importance for pathologists. The correlation between chemical substances present in biological tissue and pathology can contribute not only to a deeper understanding of the disease itself but also to the development of novel artificial intelligence-based diagnostic methodologies. Motivated by these clinical challenges, we propose a method to identify the most discriminative spectral bands by leveraging the synergy between Topological Machine Learning (TML) and Raman spectroscopy. The intrinsic explainability of part of the TML pipeline can indeed play a key role in the detection of such spectral bands, e.g., the proteins most associated with the disease. In order to evaluate the performance of our method, we apply it to three case studies: the RS of biological tissue related to the chondrogenic bone tumors, the RS of cerebrospinal fluid associated with Alzheimer’s disease and the RS of pancreatic tissue. The results obtained with our method are promising in pinpointing which spectral bands are most relevant for diagnosis, but they also highlight the need for further investigation.Source: PROCEEDINGS, vol. 129 (issue 1)
DOI: 10.3390/proceedings2025129053
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2025
Other
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Aggiornamento 2/25 Progetto Barilla Agrosat+
Martinelli M., Moroni D.
Aggiornamento modelliProject(s): Barilla Agrosat+
Martinelli M., Moroni D.
Aggiornamento modelliProject(s): Barilla Agrosat+
2025
Conference article
Metadata Only Access
Leveraging AI for Signal and Image Analysis in Medicine and Health
Marco Cafiso, Andrea Carboni, Claudia Caudai, Sara Colantonio, Francesco Conti, Mario D’acunto, Said Daoudagh, Giulio Del Corso, Danila Germanese, Giacomo Ignesti, Gianmarco Lazzini, Giuseppe Riccardo Leone, Massimo Magrini, Davide Moroni, Francesca Pardini, Maria Antonietta Pascali, Paolo Paradisi, Federico Volpini
The integration of artificial intelligence (AI) into the medical domain is driving innovation and progress in healthcare. This paper summarizes the research activities that a multidisciplinary research group within the Signals and Images Lab of the Institute of Information Science and Technologies of the National Research Council of Italy is carrying out to explore the great potential of AI in several applications, e.g., in the analysis of biomedical data, and in the development of tools for enhancing trustworthiness and reliability of AI based systems. From cancer diagnosis and grading, to the analysis of body physiological signals to improve the understanding of dance movement therapy as an approach to healthy aging, this work highlights the paradigm shift that AI has brought into medicine and healthcare.Source: CEUR WORKSHOP PROCEEDINGS, vol. 4121. Trieste, June 23-24, 2025
Marco Cafiso, Andrea Carboni, Claudia Caudai, Sara Colantonio, Francesco Conti, Mario D’acunto, Said Daoudagh, Giulio Del Corso, Danila Germanese, Giacomo Ignesti, Gianmarco Lazzini, Giuseppe Riccardo Leone, Massimo Magrini, Davide Moroni, Francesca Pardini, Maria Antonietta Pascali, Paolo Paradisi, Federico Volpini
The integration of artificial intelligence (AI) into the medical domain is driving innovation and progress in healthcare. This paper summarizes the research activities that a multidisciplinary research group within the Signals and Images Lab of the Institute of Information Science and Technologies of the National Research Council of Italy is carrying out to explore the great potential of AI in several applications, e.g., in the analysis of biomedical data, and in the development of tools for enhancing trustworthiness and reliability of AI based systems. From cancer diagnosis and grading, to the analysis of body physiological signals to improve the understanding of dance movement therapy as an approach to healthy aging, this work highlights the paradigm shift that AI has brought into medicine and healthcare.Source: CEUR WORKSHOP PROCEEDINGS, vol. 4121. Trieste, June 23-24, 2025
See at:
ceur-ws.org | CNR IRIS
2025
Conference article
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A framework for imbalanced SAR ship classification: curriculum learning, weighted loss functions, and a novel evaluation metric
Awais Ch Muhammad, Reggiannini M., Moroni D.
Accurate ship classification is essential for maritime traffic monitoring applications but is significantly hindered by imbalanced datasets. In this paper, we propose a novel methodology that combines curriculum learning with weighted loss functions to address class imbalance in the FUSAR-Ship dataset, facilitating the accurate classification of its nine classes. Our method achieved notable improvements, including a 6.53% average increase in F1-scores compared to baseline models, and successfully identified all classes, including previously misclassified ones. To better evaluate model performance on long-tailed datasets, we introduce a novel evaluation metric that provides a more nuanced assessment of classification ability across underrepresented classes. While demonstrated on the FUSAR-Ship dataset, our approach and metric are broadly applicable to other imbalanced classification problems.DOI: 10.1109/wacvw65960.2025.00171
Project(s): National Biodiversity Future Center
Metrics:
Awais Ch Muhammad, Reggiannini M., Moroni D.
Accurate ship classification is essential for maritime traffic monitoring applications but is significantly hindered by imbalanced datasets. In this paper, we propose a novel methodology that combines curriculum learning with weighted loss functions to address class imbalance in the FUSAR-Ship dataset, facilitating the accurate classification of its nine classes. Our method achieved notable improvements, including a 6.53% average increase in F1-scores compared to baseline models, and successfully identified all classes, including previously misclassified ones. To better evaluate model performance on long-tailed datasets, we introduce a novel evaluation metric that provides a more nuanced assessment of classification ability across underrepresented classes. While demonstrated on the FUSAR-Ship dataset, our approach and metric are broadly applicable to other imbalanced classification problems.DOI: 10.1109/wacvw65960.2025.00171
Project(s): National Biodiversity Future Center
Metrics:
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CNR IRIS | ieeexplore.ieee.org | CNR IRIS
2025
Other
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Reliable and trustworty learning prototype: insight from POCUS
Ignesti G., D’angelo G., Pratali L., Moroni D., Martinelli M.
Deep learning models often lack the interpretability and trustworthiness required for clinical use. This paper proposes a prototype-regularised training method to analyse 1,208 lung ultrasound images, focusing on B-line artefacts. A ConvNeXt- Tiny architecture is used, adding a novel reconstruction loss to the standard classification loss. The model is guided to extract meaningful prototypes and uses them to classify the ultrasound images. To prevent these constraints from hindering generalisation, it is used in pairs with the proposed reconstruction loss, a set of plausible data augmentation of the ideal researched prototypes, and a geometry-aware network, a spatial transformer network, to measure which solutions help the network towards outputting the most reliable outcomes. The resulting models are precise, lightweight and interpretable, indicating that the proposed solution can be embedded in an ultrasound device to assist healthcare specialists in point-of-care applications.Project(s): TiAssisto
Ignesti G., D’angelo G., Pratali L., Moroni D., Martinelli M.
Deep learning models often lack the interpretability and trustworthiness required for clinical use. This paper proposes a prototype-regularised training method to analyse 1,208 lung ultrasound images, focusing on B-line artefacts. A ConvNeXt- Tiny architecture is used, adding a novel reconstruction loss to the standard classification loss. The model is guided to extract meaningful prototypes and uses them to classify the ultrasound images. To prevent these constraints from hindering generalisation, it is used in pairs with the proposed reconstruction loss, a set of plausible data augmentation of the ideal researched prototypes, and a geometry-aware network, a spatial transformer network, to measure which solutions help the network towards outputting the most reliable outcomes. The resulting models are precise, lightweight and interpretable, indicating that the proposed solution can be embedded in an ultrasound device to assist healthcare specialists in point-of-care applications.Project(s): TiAssisto
2025
Other
Open Access
Basics of image processing and analysis
Moroni D., Salvetti O.
Syllabus – Imaging, computer vision and UAVs in the Agritech Domain Lesson 1: Basics of Image Processing and Analysis Date: 18 April Time: 14:00 -20:00 Topics: • Geometric Transformations: Resizing, rotation, translation, and their applications in image pre-processing. • Filtering: Basics of convolution, edge detection, smoothing, and sharpening filters. • Image Enhancement: Task definition; Histogram equalization, contrast adjustment, and noise reduction techniques. • Image Registration: Task definition; Aligning images from different sources or points of view using keypoint detection and matching. • Image Segmentation: Task definition; Introduction to thresholding, region growing, and clustering for image segmentation. Methodology: • Lecture: Introduction to each concept with examples. • Hands-on Tutorials: Use open-source software (e.g., OpenCV, Scikit-Image) for practical exercises.
Moroni D., Salvetti O.
Syllabus – Imaging, computer vision and UAVs in the Agritech Domain Lesson 1: Basics of Image Processing and Analysis Date: 18 April Time: 14:00 -20:00 Topics: • Geometric Transformations: Resizing, rotation, translation, and their applications in image pre-processing. • Filtering: Basics of convolution, edge detection, smoothing, and sharpening filters. • Image Enhancement: Task definition; Histogram equalization, contrast adjustment, and noise reduction techniques. • Image Registration: Task definition; Aligning images from different sources or points of view using keypoint detection and matching. • Image Segmentation: Task definition; Introduction to thresholding, region growing, and clustering for image segmentation. Methodology: • Lecture: Introduction to each concept with examples. • Hands-on Tutorials: Use open-source software (e.g., OpenCV, Scikit-Image) for practical exercises.
2025
Other
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