Typology
operator:
and / or
Language
operator:
and / or
Date
operator:
and / or
Rights
operator:
and / or
2026
Other
Open Access
Tecniche di IA Generativa per l'arricchimento di dataset di immagini
Torbidoni Giacomo, Del Corso Giulio, Papini Oscar, Tortorella Domenico
Negli ultimi anni, i sistemi di Deep Learning hanno assunto un ruolo centrale in numerosi ambiti. L'evoluzione delle architetture neurali ha portato allo sviluppo di modelli sempre più complessi e performanti, i quali tuttavia richiedono dataset di addestramento composti anche da decine di milioni di immagini In molti contesti reali non è possibile raggiungere tali numerosità, sia per i costi elevati in termini di tempo e denaro per l'acquisizione e l'annotazione, sia talvolta per la natura specifica del dominio applicativo. Negli ultimi anni, l'emergere dei modelli generativi ha aperto nuove prospettive nell'ambito della data augmentation, consentendo la produzione di dati sintetici realistici e coerenti. Questa tesi, che costituisce la relazione del tirocinio svolto da novembre 2025 a febbraio 2026 presso l'Istituto di Scienza e Tecnologie dell'Informazione del Consiglio Nazionale delle Ricerche (ISTI-CNR) di Pisa nell'ambito del progetto europeo FAITH, di cui ISTI-CNR è il leader del sottoprogetto pilota sul trasporto pubblico, si concentra sulla progettazione, implementazione e validazione di un processo di data augmentation avanzata basato su modelli di intelligenza artificiale generativa, con il duplice obiettivo di ampliare numericamente un dataset esistente e al contempo sviluppare una metodologia controllata e riproducibile per la generazione di immagini sintetiche realistiche e coerenti da integrare in contesti visivi reali.
Torbidoni Giacomo, Del Corso Giulio, Papini Oscar, Tortorella Domenico
Negli ultimi anni, i sistemi di Deep Learning hanno assunto un ruolo centrale in numerosi ambiti. L'evoluzione delle architetture neurali ha portato allo sviluppo di modelli sempre più complessi e performanti, i quali tuttavia richiedono dataset di addestramento composti anche da decine di milioni di immagini In molti contesti reali non è possibile raggiungere tali numerosità, sia per i costi elevati in termini di tempo e denaro per l'acquisizione e l'annotazione, sia talvolta per la natura specifica del dominio applicativo. Negli ultimi anni, l'emergere dei modelli generativi ha aperto nuove prospettive nell'ambito della data augmentation, consentendo la produzione di dati sintetici realistici e coerenti. Questa tesi, che costituisce la relazione del tirocinio svolto da novembre 2025 a febbraio 2026 presso l'Istituto di Scienza e Tecnologie dell'Informazione del Consiglio Nazionale delle Ricerche (ISTI-CNR) di Pisa nell'ambito del progetto europeo FAITH, di cui ISTI-CNR è il leader del sottoprogetto pilota sul trasporto pubblico, si concentra sulla progettazione, implementazione e validazione di un processo di data augmentation avanzata basato su modelli di intelligenza artificiale generativa, con il duplice obiettivo di ampliare numericamente un dataset esistente e al contempo sviluppare una metodologia controllata e riproducibile per la generazione di immagini sintetiche realistiche e coerenti da integrare in contesti visivi reali.
2026
Conference article
Open Access
Remind me of something? Zero-Shot learning for trustworthy image comparison in rolling stock
Papini Oscar, Del Corso Giulio, Bulotta Davide, Carboni Andrea, Gravili Silvia, Leone Giuseppe Riccardo, Pascali Maria Antonietta, Moroni Davide, Colantonio Sara
This paper discusses the need for trustworthy AI in urban mobility, focusing on high-stakes security applications such as anomaly detection in public transportation. Because the accuracy required to identify potentially dangerous objects often surpasses the capabilities of current models, there is an unavoidable incidence of false positives. We suggest a "learning to defer" approach as a solution. Our technique uses the deep features and label relative importance of a pre-trained classifier (DenseNet/ImageNET-1k) to create a unique item "fingerprint". We then employ a zero-shot meta-learning approach to calibrate the system, enabling it to distinguish between normal background items and genuine anomalies by assigning a similarity score. This method significantly reduces the false "new object" alarms that would otherwise overwhelm human operators. Our proof-of-concept demonstrates that the system is computationally light and can be easily adapted to specific environments and integrated into existing classification modules.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 16170, pp. 323-334. Roma, Italy, 15-19/09/2025
DOI: 10.1007/978-3-032-11381-8_28
Project(s): FAITH
Metrics:
Papini Oscar, Del Corso Giulio, Bulotta Davide, Carboni Andrea, Gravili Silvia, Leone Giuseppe Riccardo, Pascali Maria Antonietta, Moroni Davide, Colantonio Sara
This paper discusses the need for trustworthy AI in urban mobility, focusing on high-stakes security applications such as anomaly detection in public transportation. Because the accuracy required to identify potentially dangerous objects often surpasses the capabilities of current models, there is an unavoidable incidence of false positives. We suggest a "learning to defer" approach as a solution. Our technique uses the deep features and label relative importance of a pre-trained classifier (DenseNet/ImageNET-1k) to create a unique item "fingerprint". We then employ a zero-shot meta-learning approach to calibrate the system, enabling it to distinguish between normal background items and genuine anomalies by assigning a similarity score. This method significantly reduces the false "new object" alarms that would otherwise overwhelm human operators. Our proof-of-concept demonstrates that the system is computationally light and can be easily adapted to specific environments and integrated into existing classification modules.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 16170, pp. 323-334. Roma, Italy, 15-19/09/2025
DOI: 10.1007/978-3-032-11381-8_28
Project(s): FAITH
Metrics:
See at:
CNR IRIS 
| link.springer.com | CNR IRIS 
| CNR IRIS
2026
Other
Open Access
Progettazione di un dataset multi-modale di oggetti abbandonati per applicazioni in domini con scarsità di dati
Disperati Alessio, Del Corso Giulio, Leone Giuseppe Riccardo, Papini Oscar, Ceni Andrea
Le moderne soluzioni per la classificazione e l'identificazione di oggetti e persone tramite telecamere si appoggiano su reti neurali addestrate su milioni di immagini naturali. Sebbene efficienti per l'uso quotidiano, le performance di questi metodi degradano in domini applicativi specifici con oggetti poco o per niente rappresentati nei dataset di addestramento. Questa tesi, che costituisce la relazione del tirocinio svolto da ottobre 2025 a gennaio 2026 presso l'Istituto di Scienza e Tecnologie dell'Informazione del Consiglio Nazionale delle Ricerche (ISTI-CNR) di Pisa nell'ambito del progetto europeo FAITH, di cui ISTI-CNR è il leader del sottoprogetto pilota sul trasporto pubblico, si pone l'obiettivo di sopperire a tale mancanza di informazioni mediante la creazione di un dataset multimodale personalizzato nell'ambito del trasporto pubblico, chiamato CTOD (Common Train Objects Dataset). Nel dettaglio, in essa sono illustrate tutte le attività svolte e le scelte progettuali, a partire da quella della telecamera utilizzata per acquisire i dati e la sua validazione, fino ad arrivare alla presentazione del dataset, descrivendone dettagliatamente la definizione, l'organizzazione strutturale e l'implementazione, compreso il modulo associato ad esso per permetterne l'interazione con l'utilizzatore.
Disperati Alessio, Del Corso Giulio, Leone Giuseppe Riccardo, Papini Oscar, Ceni Andrea
Le moderne soluzioni per la classificazione e l'identificazione di oggetti e persone tramite telecamere si appoggiano su reti neurali addestrate su milioni di immagini naturali. Sebbene efficienti per l'uso quotidiano, le performance di questi metodi degradano in domini applicativi specifici con oggetti poco o per niente rappresentati nei dataset di addestramento. Questa tesi, che costituisce la relazione del tirocinio svolto da ottobre 2025 a gennaio 2026 presso l'Istituto di Scienza e Tecnologie dell'Informazione del Consiglio Nazionale delle Ricerche (ISTI-CNR) di Pisa nell'ambito del progetto europeo FAITH, di cui ISTI-CNR è il leader del sottoprogetto pilota sul trasporto pubblico, si pone l'obiettivo di sopperire a tale mancanza di informazioni mediante la creazione di un dataset multimodale personalizzato nell'ambito del trasporto pubblico, chiamato CTOD (Common Train Objects Dataset). Nel dettaglio, in essa sono illustrate tutte le attività svolte e le scelte progettuali, a partire da quella della telecamera utilizzata per acquisire i dati e la sua validazione, fino ad arrivare alla presentazione del dataset, descrivendone dettagliatamente la definizione, l'organizzazione strutturale e l'implementazione, compreso il modulo associato ad esso per permetterne l'interazione con l'utilizzatore.
2025
Other
Open Access
SI-Lab Annual Research Report 2024
Awais Ch Muhammad, Baiamonte A., Benassi A., Berti A., Bertini G., Buongiorno R, Bulotta D., 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 Ali Reza, 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, Baiamonte A., Benassi A., Berti A., Bertini G., Buongiorno R, Bulotta D., 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 Ali Reza, 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
Conference article
Open Access
Deep learning–based detection of Nephrops norvegicus burrows
Papini O., Cecapolli E., Domenichetti F., Pieri G., Reggiannini M., Zacchetti L., Martinelli M.
This work presents a methodology to support the assessment of a benthic species of great commercial importance (Nephrops norvegicus) taking advantage of a combination of machine learning and computer vision methods. Up to the present, abundance indices based on the density of this species are evaluated by visual inspection of underwater imagery and through manual counting of the observed burrows. A novel approach is proposed, based on the integration in the processing pipeline of a supervised learning model in charge of detecting the burrows. The model is trained exploiting underwater videos that experts annotate by identifying the frames where burrows are present and specifying the related features. To pursue such a goal, the proposed automated procedure must cope with several environmental issues, such as high underwater turbidity, uneven illumination, heavy colour distortions, as well as complexities arising from the presence of ambiguous objects and morphological features that may affect the misclassification rate. The proposed method was developed on video material collected in a specific area, but has the potential to be applied throughout the species' distribution range. Preliminary results concerning the analysis of data captured in the central Adriatic Sea are presented and discussed.DOI: 10.1109/metrosea66681.2025.11245690
Project(s): NAUTILOS
Metrics:
Papini O., Cecapolli E., Domenichetti F., Pieri G., Reggiannini M., Zacchetti L., Martinelli M.
This work presents a methodology to support the assessment of a benthic species of great commercial importance (Nephrops norvegicus) taking advantage of a combination of machine learning and computer vision methods. Up to the present, abundance indices based on the density of this species are evaluated by visual inspection of underwater imagery and through manual counting of the observed burrows. A novel approach is proposed, based on the integration in the processing pipeline of a supervised learning model in charge of detecting the burrows. The model is trained exploiting underwater videos that experts annotate by identifying the frames where burrows are present and specifying the related features. To pursue such a goal, the proposed automated procedure must cope with several environmental issues, such as high underwater turbidity, uneven illumination, heavy colour distortions, as well as complexities arising from the presence of ambiguous objects and morphological features that may affect the misclassification rate. The proposed method was developed on video material collected in a specific area, but has the potential to be applied throughout the species' distribution range. Preliminary results concerning the analysis of data captured in the central Adriatic Sea are presented and discussed.DOI: 10.1109/metrosea66681.2025.11245690
Project(s): NAUTILOS
Metrics:
See at:
CNR IRIS | ieeexplore.ieee.org | doi.org | CNR IRIS | 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 Zoe, Miori V., Tolomei Gabriele, Waheed T., Marchetti E., Calabrò Antonello., Rossetti G., Stella Massimo, Cazabet Rémy, 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 Alejandro, Sebastiani F., Sperduti G., Nguyen Dong, Broccia G., Ter Beek M. H., Ferrari A., Massink M., Belmonte Gina, 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 Jonas, Meyer Thomas, 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 Dongjae, Di Giandomenico F., Jee Eunkyoung, 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 Gabriele
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 Zoe, Miori V., Tolomei Gabriele, Waheed T., Marchetti E., Calabrò Antonello., Rossetti G., Stella Massimo, Cazabet Rémy, 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 Alejandro, Sebastiani F., Sperduti G., Nguyen Dong, Broccia G., Ter Beek M. H., Ferrari A., Massink M., Belmonte Gina, 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 Jonas, Meyer Thomas, 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 Dongjae, Di Giandomenico F., Jee Eunkyoung, 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 Gabriele
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
Machine learning approaches for automated detection of nephrops norvegicus burrows in underwater surveys
Papini O., Cecapolli E., Domenichetti F., Martinelli M., Pieri G., Reggiannini M., Zacchetti L.
This paper presents an analysis of computer vision methods designed to automate the detection, recognition, and classification of Nephrops norvegicus burrows in underwater videos. The proposed approach seeks to evaluate the accuracy, minimise human error, and standardise the existing manual video analysis process. By leveraging machine learning techniques, the system described in this paper autonomously processes video streams and identifies N. norvegicus burrow openings on the seabed. Additionally, this study investigates data augmentation algorithms to expand an annotated dataset and evaluates the performances of the first results under different configurations.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 15616, pp. 285-294. Kolkata, India, 01-05/12/2024
DOI: 10.1007/978-3-031-87663-9_24
Project(s): NAUTILOS
Metrics:
Papini O., Cecapolli E., Domenichetti F., Martinelli M., Pieri G., Reggiannini M., Zacchetti L.
This paper presents an analysis of computer vision methods designed to automate the detection, recognition, and classification of Nephrops norvegicus burrows in underwater videos. The proposed approach seeks to evaluate the accuracy, minimise human error, and standardise the existing manual video analysis process. By leveraging machine learning techniques, the system described in this paper autonomously processes video streams and identifies N. norvegicus burrow openings on the seabed. Additionally, this study investigates data augmentation algorithms to expand an annotated dataset and evaluates the performances of the first results under different configurations.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 15616, pp. 285-294. Kolkata, India, 01-05/12/2024
DOI: 10.1007/978-3-031-87663-9_24
Project(s): NAUTILOS
Metrics:
See at:
CNR IRIS | link.springer.com | CNR IRIS | CNR IRIS
2025
Conference article
Open Access
Machine learning for the evaluation of the Nephrops norvegicus Population
Reggiannini M., Martinelli M., Papini O., Zacchetti L., Domenichetti F., Pieri G.
This paper introduces computer vision methods for detecting, recognising, and estimating Nephrops norvegicus (Norway lobster) burrow density via Underwater Television surveys. The current manual approach involves human operators visually assessing videos, which is prone to errors and subjectivity. Automated machine learning systems show promise in identifying and counting burrows, potentially standardising recognition and reducing operator errors. However, challenges exist in implementing computer vision techniques. An automated system aims to process video streams, detect seabed openings, extract visual features, and classify N. norvegicus burrows, significantly advancing the automation of underwater video reading. The primary processing presented in the paper lies in a boosting algorithm capable of extending the original annotated ground truth and assessing the improved performance of the extended data set with respect to the original one.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 15509, pp. 282-295. Riva del Sole Resort & SPA - Castiglione della Pescaia, Toscana, 22-25/09/2024
DOI: 10.1007/978-3-031-82484-5_21
Project(s): NAUTILOS
Metrics:
Reggiannini M., Martinelli M., Papini O., Zacchetti L., Domenichetti F., Pieri G.
This paper introduces computer vision methods for detecting, recognising, and estimating Nephrops norvegicus (Norway lobster) burrow density via Underwater Television surveys. The current manual approach involves human operators visually assessing videos, which is prone to errors and subjectivity. Automated machine learning systems show promise in identifying and counting burrows, potentially standardising recognition and reducing operator errors. However, challenges exist in implementing computer vision techniques. An automated system aims to process video streams, detect seabed openings, extract visual features, and classify N. norvegicus burrows, significantly advancing the automation of underwater video reading. The primary processing presented in the paper lies in a boosting algorithm capable of extending the original annotated ground truth and assessing the improved performance of the extended data set with respect to the original one.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 15509, pp. 282-295. Riva del Sole Resort & SPA - Castiglione della Pescaia, Toscana, 22-25/09/2024
DOI: 10.1007/978-3-031-82484-5_21
Project(s): NAUTILOS
Metrics:
See at:
CNR IRIS | link.springer.com | doi.org | CNR IRIS | CNR IRIS
2025
Journal article
Open Access
Advancing automated detection of Nephrops norvegicus burrows in underwater television surveys through machine learning
Papini O., Cecapolli E., Domenichetti F., Martinelli M., Pieri G., Reggiannini M., Zacchetti L.
The paper introduces computer vision methods for automating the detection, recognition, and classification of Nephrops norvegicus burrows in underwater videos. This approach aims to improve accuracy, reduce human errors, and standardize the current manual video analysis process. By using machine learning techniques, the system can automatically process video streams and detect N. norvegicus burrow openings on the seabed. The work also explores the use of data augmentation algorithms to extend the annotated data set, enhancing the performance of the automated system compared to the original manual annotations.Source: PATTERN RECOGNITION AND IMAGE ANALYSIS, vol. 34 (issue 4), pp. 1030-1036
DOI: 10.1134/s1054661824701062
Project(s): NAUTILOS
Metrics:
Papini O., Cecapolli E., Domenichetti F., Martinelli M., Pieri G., Reggiannini M., Zacchetti L.
The paper introduces computer vision methods for automating the detection, recognition, and classification of Nephrops norvegicus burrows in underwater videos. This approach aims to improve accuracy, reduce human errors, and standardize the current manual video analysis process. By using machine learning techniques, the system can automatically process video streams and detect N. norvegicus burrow openings on the seabed. The work also explores the use of data augmentation algorithms to extend the annotated data set, enhancing the performance of the automated system compared to the original manual annotations.Source: PATTERN RECOGNITION AND IMAGE ANALYSIS, vol. 34 (issue 4), pp. 1030-1036
DOI: 10.1134/s1054661824701062
Project(s): NAUTILOS
Metrics:
See at:
CNR IRIS | link.springer.com | CNR IRIS | CNR IRIS
2025
Other
Open Access
Guidelines for the annotation of Nephrops norvegicus UWTV videos
Papini O., Cecapolli E., Domenichetti F., Martinelli M., Pieri G., Reggiannini M., Zacchetti L.
This document describes a methodology conceived to create ground truth datasets that may be exploited in the implementation of object detection and classification algorithms tailored on the Nephrops norvegicus. In fact, supervised machine learning algorithms usually require considerable amounts of annotated data to carry out the training stage. The greater the size of the annotated dataset, the stronger the required effort from the annotators.DOI: 10.32079/isti-tr-2025/009
DOI: 10.5281/zenodo.14973160
DOI: 10.5281/zenodo.14973159
Project(s): NAUTILOS
Metrics:
Papini O., Cecapolli E., Domenichetti F., Martinelli M., Pieri G., Reggiannini M., Zacchetti L.
This document describes a methodology conceived to create ground truth datasets that may be exploited in the implementation of object detection and classification algorithms tailored on the Nephrops norvegicus. In fact, supervised machine learning algorithms usually require considerable amounts of annotated data to carry out the training stage. The greater the size of the annotated dataset, the stronger the required effort from the annotators.DOI: 10.32079/isti-tr-2025/009
DOI: 10.5281/zenodo.14973160
DOI: 10.5281/zenodo.14973159
Project(s): NAUTILOS
Metrics:
2025
Journal article
Open Access
An isomorphism between projective models of toric and hyperplane graphic arrangements
Gaiffi G., Papini O., Siconolfi V.
This paper presents a bridge between the theories of wonderful models associated with toric arrangements and wonderful models associated with hyperplane arrangements. In a previous work, the same authors noticed that the model of the toric arrangement of type A_{n−1} associated with the minimal building set is isomorphic to the one of the hyperplane arrangement of type A_n associated again with the minimal building set; it is natural to ask if there exist similar isomorphisms between other families of arrangements. The aim of this paper is to study one such family, namely the family of arrangements defined by graphs. The main result states that there is indeed an isomorphism between the model of the toric arrangement defined by a graph Γ and the model of the hyperplane arrangement defined by the cone of Γ, provided that a suitable building set is chosen.Source: NOTE DI MATEMATICA, vol. 45 (issue 1), pp. 15-37
DOI: 10.1285/i15900932v45n1p15
Metrics:
Gaiffi G., Papini O., Siconolfi V.
This paper presents a bridge between the theories of wonderful models associated with toric arrangements and wonderful models associated with hyperplane arrangements. In a previous work, the same authors noticed that the model of the toric arrangement of type A_{n−1} associated with the minimal building set is isomorphic to the one of the hyperplane arrangement of type A_n associated again with the minimal building set; it is natural to ask if there exist similar isomorphisms between other families of arrangements. The aim of this paper is to study one such family, namely the family of arrangements defined by graphs. The main result states that there is indeed an isomorphism between the model of the toric arrangement defined by a graph Γ and the model of the hyperplane arrangement defined by the cone of Γ, provided that a suitable building set is chosen.Source: NOTE DI MATEMATICA, vol. 45 (issue 1), pp. 15-37
DOI: 10.1285/i15900932v45n1p15
Metrics:
See at:
CNR IRIS | siba-ese.unisalento.it | CNR IRIS
2024
Conference article
Open Access
A mesoscale events classifier for sea surface temperature data
Reggiannini M., Papini O., Pieri G.
The identification of mesoscale phenomena, such as upwelling, countercurrents and filaments, is an important task for oceanographers. Indeed, the occurrence of such processes involves variations in the density of nutrients which, in turn, influences the biological parameters of the habitat. In this work, we describe a novel method for an automatic classification system, the Mesoscale Events Classifier (MEC), dedicated to recognising marine mesoscale events. MEC is devoted to the study of these phenomena through the analysis of Sea Surface Temperature (SST) images captured by satellite missions.Source: MISCELLANEA INGV, vol. 80, pp. 317-319. Bergen, Norvegia, 27-29/05/2024
DOI: 10.13127/misc/80/122
DOI: https://commons.datacite.org/doi.org/10.13127/misc/80/122
Project(s): NAUTILOS
Metrics:
Reggiannini M., Papini O., Pieri G.
The identification of mesoscale phenomena, such as upwelling, countercurrents and filaments, is an important task for oceanographers. Indeed, the occurrence of such processes involves variations in the density of nutrients which, in turn, influences the biological parameters of the habitat. In this work, we describe a novel method for an automatic classification system, the Mesoscale Events Classifier (MEC), dedicated to recognising marine mesoscale events. MEC is devoted to the study of these phenomena through the analysis of Sea Surface Temperature (SST) images captured by satellite missions.Source: MISCELLANEA INGV, vol. 80, pp. 317-319. Bergen, Norvegia, 27-29/05/2024
DOI: 10.13127/misc/80/122
DOI: https://commons.datacite.org/doi.org/10.13127/misc/80/122
Project(s): NAUTILOS
Metrics:
2024
Conference article
Open Access
Advancing sustainability: research initiatives at the Signals and Images Lab
Bruno A., Caudai C., Conti F., Leone G. R., Magrini M., Martinelli M., Moroni D., Muhammad A. Ch, Papini O., Pascali M. A., Pieri G., Reggiannini M., Righi M., Salerno E., Scozzari A., Tampucci M.
In this paper, we aim to briefly survey the relations of the work conducted at the Signals and Images Lab of CNR-ISTI, Pisa, with the themes of sustainability. We explore both the broader implications and the application-specific aspects of our work, highlighting references to published research and collaborative projects undertaken with key stakeholders and industrial partners.Source: CEUR WORKSHOP PROCEEDINGS, vol. 3762, pp. 499-504. Napoli, Italy, 29-30/05/2024
Bruno A., Caudai C., Conti F., Leone G. R., Magrini M., Martinelli M., Moroni D., Muhammad A. Ch, Papini O., Pascali M. A., Pieri G., Reggiannini M., Righi M., Salerno E., Scozzari A., Tampucci M.
In this paper, we aim to briefly survey the relations of the work conducted at the Signals and Images Lab of CNR-ISTI, Pisa, with the themes of sustainability. We explore both the broader implications and the application-specific aspects of our work, highlighting references to published research and collaborative projects undertaken with key stakeholders and industrial partners.Source: CEUR WORKSHOP PROCEEDINGS, vol. 3762, pp. 499-504. Napoli, Italy, 29-30/05/2024
See at:
ceur-ws.org | CNR IRIS | CNR IRIS
2024
Other
Open Access
A mesoscale events classifier for sea surface temperature data
Reggiannini M., Papini O., Pieri G.
The Mesoscale Events Classifier (MEC) is a tool that has been developed to detect and classify patterns of mesoscale events in an upwelling ecosystem by analysing Sea Surface Temperature (SST) maps coming from satellite data.Source: MISCELLANEA INGV, vol. 80. Bergen, Norvegia, 27-29/05/2024
Project(s): NAUTILOS
Reggiannini M., Papini O., Pieri G.
The Mesoscale Events Classifier (MEC) is a tool that has been developed to detect and classify patterns of mesoscale events in an upwelling ecosystem by analysing Sea Surface Temperature (SST) maps coming from satellite data.Source: MISCELLANEA INGV, vol. 80. Bergen, Norvegia, 27-29/05/2024
Project(s): NAUTILOS
See at:
CNR IRIS | share.ifremer.fr | CNR IRIS
2024
Journal article
Open Access
A basis for the cohomology of compact models of toric arrangements
Gaiffi G, Papini O., Siconolfi V.
In this paper we find monomial bases for the integer cohomology rings of compact wonderful models of toric arrangements. In the description of the monomials various combinatorial objects come into play: building sets, nested sets, and the fan of a suitable toric variety. We provide some examples computed via a SageMath program and then we focus on the case of the toric arrangements associated with root systems of type A. Here the combinatorial description of our basis offers a geometrical point of view on the relation between some Eulerian statistics on the symmetric group.Source: PURE AND APPLIED MATHEMATICS QUARTERLY, vol. 20 (issue 1), pp. 427-470
DOI: 10.4310/pamq.2024.v20.n1.a9
DOI: 10.48550/arxiv.2205.00443
Metrics:
Gaiffi G, Papini O., Siconolfi V.
In this paper we find monomial bases for the integer cohomology rings of compact wonderful models of toric arrangements. In the description of the monomials various combinatorial objects come into play: building sets, nested sets, and the fan of a suitable toric variety. We provide some examples computed via a SageMath program and then we focus on the case of the toric arrangements associated with root systems of type A. Here the combinatorial description of our basis offers a geometrical point of view on the relation between some Eulerian statistics on the symmetric group.Source: PURE AND APPLIED MATHEMATICS QUARTERLY, vol. 20 (issue 1), pp. 427-470
DOI: 10.4310/pamq.2024.v20.n1.a9
DOI: 10.48550/arxiv.2205.00443
Metrics:
See at:
arXiv.org e-Print Archive | IRIS Cnr | IRIS Cnr | IRIS Cnr | Archivio della Ricerca - Università di Pisa | Pure and Applied Mathematics Quarterly | doi.org | Archivio della Ricerca - Università di Pisa | Archivio Istituzionale della Ricerca - Politecnico di Bari | CNR IRIS | CNR IRIS
2023
Contribution to book
Open Access
Mesoscale events classification in sea surface temperature imagery
Reggiannini M, Janeiro J, Martins F, Papini O, Pieri G
Sea observation through remote sensing technologies plays an essential role in understanding the health status of marine fauna species and their future behaviour. Accurate knowledge of the marine habitat and the factors affecting faunal variations allows to perform predictions and adopt proper decisions. This is even more relevant nowadays, with policymakers needing increased environmental awareness, aiming to implement sustainable policies. There is a connection between the biogeochemical and physical processes taking place within a biological system and the variations observed in its faunal populations. Mesoscale phenomena, such as upwelling, countercurrents and filaments, are essential processes to analyse because their arousal entails, among other things, variations in the density of nutrient substances, in turn affecting the biological parameters of the habitat. This paper concerns the proposal of a classification system devoted to recognising marine mesoscale events. These phenomena are studied and monitored by analysing Sea Surface Temperature images captured by satellite missions, such as Metop and MODIS Terra/Aqua. Classification of such images is pursued through dedicated algorithms that extract temporal and spatial features from the data and apply a set of rules to the extracted features, in order to discriminate between different observed scenarios. The results presented in this work have been obtained by applying the proposed approach to images captured over the south-western region of the Iberian Peninsula.DOI: 10.1007/978-3-031-25599-1_38
Project(s): NAUTILOS
Metrics:
Reggiannini M, Janeiro J, Martins F, Papini O, Pieri G
Sea observation through remote sensing technologies plays an essential role in understanding the health status of marine fauna species and their future behaviour. Accurate knowledge of the marine habitat and the factors affecting faunal variations allows to perform predictions and adopt proper decisions. This is even more relevant nowadays, with policymakers needing increased environmental awareness, aiming to implement sustainable policies. There is a connection between the biogeochemical and physical processes taking place within a biological system and the variations observed in its faunal populations. Mesoscale phenomena, such as upwelling, countercurrents and filaments, are essential processes to analyse because their arousal entails, among other things, variations in the density of nutrient substances, in turn affecting the biological parameters of the habitat. This paper concerns the proposal of a classification system devoted to recognising marine mesoscale events. These phenomena are studied and monitored by analysing Sea Surface Temperature images captured by satellite missions, such as Metop and MODIS Terra/Aqua. Classification of such images is pursued through dedicated algorithms that extract temporal and spatial features from the data and apply a set of rules to the extracted features, in order to discriminate between different observed scenarios. The results presented in this work have been obtained by applying the proposed approach to images captured over the south-western region of the Iberian Peninsula.DOI: 10.1007/978-3-031-25599-1_38
Project(s): NAUTILOS
Metrics:
See at:
CNR IRIS | link.springer.com | ISTI Repository | CNR IRIS | CNR IRIS
2023
Conference article
Open Access
Automated image processing for remote sensing data classification
Reggiannini M, Papini O, Pieri G
Remote sensing technologies allow for continuous and valuable monitoring of the Earth's various environments. In particular, coastal and ocean monitoring presents an intrinsic complexity that makes such monitoring the main source of information available. Oceans, being the largest but least observed habitat, have many different factors affecting theirs faunal variations. Enhancing the capabilities to monitor and understand the changes occurring allows us to perform predictions and adopt proper decisions. This paper proposes an automated classification tool to recognise specific marine mesoscale events. Typically, human experts monitor and analyse these events visually through remote sensing imagery, specifically addressing Sea Surface Temperature data. The extended availability of this kind of remote sensing data transforms this activity into a time-consuming and subjective interpretation of the information. For this reason, there is an increased need for automated or at least semi-automated tools to perform this task. The results presented in this work have been obtained by applying the proposed approach to images captured over the southwestern region of the Iberian Peninsula.DOI: 10.1007/978-3-031-37742-6_43
Project(s): NAUTILOS
Metrics:
Reggiannini M, Papini O, Pieri G
Remote sensing technologies allow for continuous and valuable monitoring of the Earth's various environments. In particular, coastal and ocean monitoring presents an intrinsic complexity that makes such monitoring the main source of information available. Oceans, being the largest but least observed habitat, have many different factors affecting theirs faunal variations. Enhancing the capabilities to monitor and understand the changes occurring allows us to perform predictions and adopt proper decisions. This paper proposes an automated classification tool to recognise specific marine mesoscale events. Typically, human experts monitor and analyse these events visually through remote sensing imagery, specifically addressing Sea Surface Temperature data. The extended availability of this kind of remote sensing data transforms this activity into a time-consuming and subjective interpretation of the information. For this reason, there is an increased need for automated or at least semi-automated tools to perform this task. The results presented in this work have been obtained by applying the proposed approach to images captured over the southwestern region of the Iberian Peninsula.DOI: 10.1007/978-3-031-37742-6_43
Project(s): NAUTILOS
Metrics:
See at:
CNR IRIS | link.springer.com | ISTI Repository | CNR IRIS | CNR IRIS
2023
Journal article
Open Access
MEC: a Mesoscale Events Classifier for oceanographic imagery
Pieri G, Janeiro J, Martins F, Papini O, Reggiannini M
The observation of the sea through remote sensing technologies plays a fundamental role in understanding the state of health of marine fauna species and their behaviour. Mesoscale phenomena, such as upwelling, countercurrents, and filaments, are essential processes to be analysed because their occurrence involves, among other things, variations in the density of nutrients, which, in turn, influence the biological parameters of the habitat. Indeed, there is a connection between the biogeochemical and physical processes that occur within a biological system and the variations observed in its faunal populations. This paper concerns the proposal of an automatic classification system, namely the Mesoscale Events Classifier, dedicated to the recognition of marine mesoscale events. The proposed system is devoted to the study of these phenomena through the analysis of sea surface temperature images captured by satellite missions, such as EUMETSAT's Metop and NASA's Earth Observing System programmes. The classification of these images is obtained through (i) a preprocessing stage with the goal to provide a simultaneous representation of the spatial and temporal properties of the data and enhance the salient features of the sought phenomena, (ii) the extraction of temporal and spatial characteristics from the data and, finally, (iii) the application of a set of rules to discriminate between different observed scenarios. The results presented in this work were obtained by applying the proposed approach to images acquired in the southwestern region of the Iberian peninsula.Source: APPLIED SCIENCES, vol. 13 (issue 3)
DOI: 10.3390/app13031565
Project(s): NAUTILOS
Metrics:
Pieri G, Janeiro J, Martins F, Papini O, Reggiannini M
The observation of the sea through remote sensing technologies plays a fundamental role in understanding the state of health of marine fauna species and their behaviour. Mesoscale phenomena, such as upwelling, countercurrents, and filaments, are essential processes to be analysed because their occurrence involves, among other things, variations in the density of nutrients, which, in turn, influence the biological parameters of the habitat. Indeed, there is a connection between the biogeochemical and physical processes that occur within a biological system and the variations observed in its faunal populations. This paper concerns the proposal of an automatic classification system, namely the Mesoscale Events Classifier, dedicated to the recognition of marine mesoscale events. The proposed system is devoted to the study of these phenomena through the analysis of sea surface temperature images captured by satellite missions, such as EUMETSAT's Metop and NASA's Earth Observing System programmes. The classification of these images is obtained through (i) a preprocessing stage with the goal to provide a simultaneous representation of the spatial and temporal properties of the data and enhance the salient features of the sought phenomena, (ii) the extraction of temporal and spatial characteristics from the data and, finally, (iii) the application of a set of rules to discriminate between different observed scenarios. The results presented in this work were obtained by applying the proposed approach to images acquired in the southwestern region of the Iberian peninsula.Source: APPLIED SCIENCES, vol. 13 (issue 3)
DOI: 10.3390/app13031565
Project(s): NAUTILOS
Metrics:
See at:
CNR IRIS | ISTI Repository | www.mdpi.com | CNR IRIS
2023
Conference article
Open Access
Analysis of sea surface temperature maps via topological machine learning
Conti F, Papini O, Moroni D, Pieri G, Reggiannini M, Pascali M A
Computational methods to leverage topological features occurring in signals and images are currently one of the most innovative trends in applied mathematics. In this paper a pipeline of topological machine learning is applied to the challenging task of classifying four specific marine mesoscale patterns from remote sensing data, i.e., Sea Surface Temperature maps of the southwestern region of the Iberian Peninsula. Our preliminary study achieves an accuracy of 56% in the 4-label classification. Such results are encouraging, especially considering that the data are affected by noise and that there are low-quality/missing data. Also, the paper devises directions for future improvements.DOI: 10.1109/itnt57377.2023.10139044
Project(s): NAUTILOS
Metrics:
Conti F, Papini O, Moroni D, Pieri G, Reggiannini M, Pascali M A
Computational methods to leverage topological features occurring in signals and images are currently one of the most innovative trends in applied mathematics. In this paper a pipeline of topological machine learning is applied to the challenging task of classifying four specific marine mesoscale patterns from remote sensing data, i.e., Sea Surface Temperature maps of the southwestern region of the Iberian Peninsula. Our preliminary study achieves an accuracy of 56% in the 4-label classification. Such results are encouraging, especially considering that the data are affected by noise and that there are low-quality/missing data. Also, the paper devises directions for future improvements.DOI: 10.1109/itnt57377.2023.10139044
Project(s): NAUTILOS
Metrics:
See at:
CNR IRIS | ieeexplore.ieee.org | ISTI Repository | CNR IRIS | CNR IRIS
2023
Conference article
Open Access
Evaluation of a marine mesoscale events classifier
Reggiannini M, Papini O, Pieri G
Marine mesoscale phenomena are relevant oceanographic processes that impact on fishery, biodiversity and climate variation. In previous literature, their analysis has been tackled by processing instantaneous remote sensing observations and returning a classification of the observed event. Indeed, these phenomena occur within an extended time range, thus an analysis including time dependence is desirable. Mesoscale Events Classifier (MEC) is an algorithm devoted to the classification of marine mesoscale events in sea surface temperature imagery. By processing time series of satellite temperature observations MEC recognizes the considered area of interest as the domain of one out of a given number of possible events and returns the corresponding label. Objective of this work is to discuss the performance of the MEC pipeline in terms of its capability of correctly capturing the nature of the observed mesoscale process. The evaluation process exploited satellite remote sensing data collected in front of the Portuguese coast.DOI: 10.1109/icasspw59220.2023.10193234
Project(s): NAUTILOS
Metrics:
Reggiannini M, Papini O, Pieri G
Marine mesoscale phenomena are relevant oceanographic processes that impact on fishery, biodiversity and climate variation. In previous literature, their analysis has been tackled by processing instantaneous remote sensing observations and returning a classification of the observed event. Indeed, these phenomena occur within an extended time range, thus an analysis including time dependence is desirable. Mesoscale Events Classifier (MEC) is an algorithm devoted to the classification of marine mesoscale events in sea surface temperature imagery. By processing time series of satellite temperature observations MEC recognizes the considered area of interest as the domain of one out of a given number of possible events and returns the corresponding label. Objective of this work is to discuss the performance of the MEC pipeline in terms of its capability of correctly capturing the nature of the observed mesoscale process. The evaluation process exploited satellite remote sensing data collected in front of the Portuguese coast.DOI: 10.1109/icasspw59220.2023.10193234
Project(s): NAUTILOS
Metrics:
See at:
CNR IRIS | ieeexplore.ieee.org | ISTI Repository | CNR IRIS | CNR IRIS