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2023
Journal article
Open Access
Quantifying the loss of coral from a bleaching event using underwater photogrammetry and AI-Assisted Image Segmentation
Kopecky K. L., Pavoni G., Nocerino E., Brooks A. J., Corsini M., Menna F., Gallagher J. P., Capra A., Castagnetti C., Rossi P., Gruen A., Neyer F., Muntoni A., Ponchio F., Cignoni P., Troyer M., Holbrook S. J., Schmitt R. J.
Detecting the impacts of natural and anthropogenic disturbances that cause declines in organisms or changes in community composition has long been a focus of ecology. However, a tradeoff often exists between the spatial extent over which relevant data can be collected, and the resolution of those data. Recent advances in underwater photogrammetry, as well as computer vision and machine learning tools that employ artificial intelligence (AI), offer potential solutions with which to resolve this tradeoff. Here, we coupled a rigorous photogrammetric survey method with novel AI-assisted image segmentation software in order to quantify the impact of a coral bleaching event on a tropical reef, both at an ecologically meaningful spatial scale and with high spatial resolution. In addition to outlining our workflow, we highlight three key results: (1) dramatic changes in the three-dimensional surface areas of live and dead coral, as well as the ratio of live to dead colonies before and after bleaching; (2) a size-dependent pattern of mortality in bleached corals, where the largest corals were disproportionately affected, and (3) a significantly greater decline in the surface area of live coral, as revealed by our approximation of the 3D shape compared to the more standard planar area (2D) approach. The technique of photogrammetry allows us to turn 2D images into approximate 3D models in a flexible and efficient way. Increasing the resolution, accuracy, spatial extent, and efficiency with which we can quantify effects of disturbances will improve our ability to understand the ecological consequences that cascade from small to large scales, as well as allow more informed decisions to be made regarding the mitigation of undesired impacts.Source: Remote sensing (Basel) 15 (2023). doi:10.3390/rs15164077
DOI: 10.3390/rs15164077
Metrics:
Kopecky K. L., Pavoni G., Nocerino E., Brooks A. J., Corsini M., Menna F., Gallagher J. P., Capra A., Castagnetti C., Rossi P., Gruen A., Neyer F., Muntoni A., Ponchio F., Cignoni P., Troyer M., Holbrook S. J., Schmitt R. J.
Detecting the impacts of natural and anthropogenic disturbances that cause declines in organisms or changes in community composition has long been a focus of ecology. However, a tradeoff often exists between the spatial extent over which relevant data can be collected, and the resolution of those data. Recent advances in underwater photogrammetry, as well as computer vision and machine learning tools that employ artificial intelligence (AI), offer potential solutions with which to resolve this tradeoff. Here, we coupled a rigorous photogrammetric survey method with novel AI-assisted image segmentation software in order to quantify the impact of a coral bleaching event on a tropical reef, both at an ecologically meaningful spatial scale and with high spatial resolution. In addition to outlining our workflow, we highlight three key results: (1) dramatic changes in the three-dimensional surface areas of live and dead coral, as well as the ratio of live to dead colonies before and after bleaching; (2) a size-dependent pattern of mortality in bleached corals, where the largest corals were disproportionately affected, and (3) a significantly greater decline in the surface area of live coral, as revealed by our approximation of the 3D shape compared to the more standard planar area (2D) approach. The technique of photogrammetry allows us to turn 2D images into approximate 3D models in a flexible and efficient way. Increasing the resolution, accuracy, spatial extent, and efficiency with which we can quantify effects of disturbances will improve our ability to understand the ecological consequences that cascade from small to large scales, as well as allow more informed decisions to be made regarding the mitigation of undesired impacts.Source: Remote sensing (Basel) 15 (2023). doi:10.3390/rs15164077
DOI: 10.3390/rs15164077
Metrics:
See at:
ISTI Repository 
| www.mdpi.com | CNR ExploRA
2022
Journal article
Open Access
On assisting and automatizing the semantic segmentation of masonry walls
Pavoni G., Giuliani F., De Falco A., Corsini M., Ponchio F., Callieri M., Cignoni P.
In Architectural Heritage, the masonry's interpretation is an essential instrument for analysing the construction phases, the assessment of structural properties, and the monitoring of its state of conservation. This work is generally carried out by specialists that, based on visual observation and their knowledge, manually annotate ortho-images of the masonry generated by photogrammetric surveys. This results in vector thematic maps segmented according to their construction technique (isolating areas of homogeneous materials/structure/texture or each individual constituting block of the masonry) or state of conservation, including degradation areas and damaged parts. This time-consuming manual work, often done with tools that have not been designed for this purpose, represents a bottleneck in the documentation and management workflow and is a severely limiting factor in monitoring large-scale monuments (e.g., city walls). This article explores the potential of AI-based solutions to improve the efficiency of masonry annotation in Architectural Heritage. This experimentation aims at providing interactive tools that support and empower the current workflow, benefiting from specialists' expertise.Source: Journal on computing and cultural heritage (Online) 15 (2022). doi:10.1145/3477400
DOI: 10.1145/3477400
Metrics:
Pavoni G., Giuliani F., De Falco A., Corsini M., Ponchio F., Callieri M., Cignoni P.
In Architectural Heritage, the masonry's interpretation is an essential instrument for analysing the construction phases, the assessment of structural properties, and the monitoring of its state of conservation. This work is generally carried out by specialists that, based on visual observation and their knowledge, manually annotate ortho-images of the masonry generated by photogrammetric surveys. This results in vector thematic maps segmented according to their construction technique (isolating areas of homogeneous materials/structure/texture or each individual constituting block of the masonry) or state of conservation, including degradation areas and damaged parts. This time-consuming manual work, often done with tools that have not been designed for this purpose, represents a bottleneck in the documentation and management workflow and is a severely limiting factor in monitoring large-scale monuments (e.g., city walls). This article explores the potential of AI-based solutions to improve the efficiency of masonry annotation in Architectural Heritage. This experimentation aims at providing interactive tools that support and empower the current workflow, benefiting from specialists' expertise.Source: Journal on computing and cultural heritage (Online) 15 (2022). doi:10.1145/3477400
DOI: 10.1145/3477400
Metrics:
See at:
ISTI Repository | dl.acm.org 
| Journal on Computing and Cultural Heritage | CNR ExploRA
2021
Journal article
Open Access
Needs and gaps in optical underwater technologies and methods for the investigation of marine animal forest 3D-structural complexity
Rossi P., Ponti M., Righi S., Castagnetti C., Simonini R., Mancini F., Agrafiotis P., Bassani L., Bruno F., Cerrano C., Cignoni P., Corsini M., Drap P., Dubbini M., Garrabou J., Gori A., Gracias N., Ledoux J. B., Linares C., Mantas T. P., Menna F., Nocerino E., Palma M., Pavoni G., Ridolfi A., Rossi S., Skarlatos D., Treibitz T., Turicchia E., Yuval M., Capra A.
Marine animal forests are benthic communities dominated by sessile suspension feeders (such as sponges, corals, and bivalves) able to generate three-dimensional (3D) frameworks with high structural complexity. The biodiversity and functioning of marine animal forests are strictly related to their 3D complexity. The present paper aims at providing new perspectives in underwater optical surveys. Starting from the current gaps in data collection and analysis that critically limit the study and conservation of marine animal forests, we discuss the main technological and methodological needs for the investigation of their 3D structural complexity at different spatial and temporal scales. Despite recent technological advances, it seems that several issues in data acquisition and processing need to be solved, to properly map the different benthic habitats in which marine animal forests are present, their health status and to measure structural complexity. Proper precision and accuracy should be chosen and assured in relation to the biological and ecological processes investigated. Besides, standardized methods and protocols are strictly necessary to meet the FAIR (findability, accessibility, interoperability, and reusability) data principles for the stewardship of habitat mapping and biodiversity, biomass, and growth data.Source: Frontiers in Marine Science 8 (2021). doi:10.3389/fmars.2021.591292
DOI: 10.3389/fmars.2021.591292
Metrics:
Rossi P., Ponti M., Righi S., Castagnetti C., Simonini R., Mancini F., Agrafiotis P., Bassani L., Bruno F., Cerrano C., Cignoni P., Corsini M., Drap P., Dubbini M., Garrabou J., Gori A., Gracias N., Ledoux J. B., Linares C., Mantas T. P., Menna F., Nocerino E., Palma M., Pavoni G., Ridolfi A., Rossi S., Skarlatos D., Treibitz T., Turicchia E., Yuval M., Capra A.
Marine animal forests are benthic communities dominated by sessile suspension feeders (such as sponges, corals, and bivalves) able to generate three-dimensional (3D) frameworks with high structural complexity. The biodiversity and functioning of marine animal forests are strictly related to their 3D complexity. The present paper aims at providing new perspectives in underwater optical surveys. Starting from the current gaps in data collection and analysis that critically limit the study and conservation of marine animal forests, we discuss the main technological and methodological needs for the investigation of their 3D structural complexity at different spatial and temporal scales. Despite recent technological advances, it seems that several issues in data acquisition and processing need to be solved, to properly map the different benthic habitats in which marine animal forests are present, their health status and to measure structural complexity. Proper precision and accuracy should be chosen and assured in relation to the biological and ecological processes investigated. Besides, standardized methods and protocols are strictly necessary to meet the FAIR (findability, accessibility, interoperability, and reusability) data principles for the stewardship of habitat mapping and biodiversity, biomass, and growth data.Source: Frontiers in Marine Science 8 (2021). doi:10.3389/fmars.2021.591292
DOI: 10.3389/fmars.2021.591292
Metrics:
See at:
Frontiers in Marine Science | Recolector de Ciencia Abierta, RECOLECTA | Archivio istituzionale della ricerca - Alma Mater Studiorum Università di Bologna | Flore (Florence Research Repository) | Diposit Digital de la Universitat de Barcelona | Ktisis | ISTI Repository | Frontiers in Marine Science | Frontiers in Marine Science | CNR ExploRA
2021
Journal article
Open Access
TagLab: AI-assisted annotation for the fast and accurate semantic segmentation of coral reef orthoimages
Pavoni G., Corsini M., Ponchio F., Muntoni A., Edwards C., Pedersen N., Sandin S., Cignoni P.
Semantic segmentation is a widespread image analysis task; in some applications, it requires such high accuracy that it still has to be done manually, taking a long time. Deep learning-based approaches can significantly reduce such times, but current automated solutions may produce results below expert standards. We propose agLab, an interactive tool for the rapid labelling and analysis of orthoimages that speeds up semantic segmentation. TagLab follows a human-centered artificial intelligence approach that, by integrating multiple degrees of automation, empowers human capabilities. We evaluated TagLab's efficiency in annotation time and accuracy through a user study based on a highly challenging task: the semantic segmentation of coral communities in marine ecology. In the assisted labelling of corals, TagLab increased the annotation speed by approximately 90% for nonexpert annotators while preserving the labelling accuracy. Furthermore, human-machine interaction has improved the accuracy of fully automatic predictions by about 7% on average and by 14% when the model generalizes poorly. Considering the experience done through the user study, TagLab has been improved, and preliminary investigations suggest a further significant reduction in annotation times.Source: Journal of field robotics (2021). doi:10.1002/rob.22049
DOI: 10.1002/rob.22049
Metrics:
Pavoni G., Corsini M., Ponchio F., Muntoni A., Edwards C., Pedersen N., Sandin S., Cignoni P.
Semantic segmentation is a widespread image analysis task; in some applications, it requires such high accuracy that it still has to be done manually, taking a long time. Deep learning-based approaches can significantly reduce such times, but current automated solutions may produce results below expert standards. We propose agLab, an interactive tool for the rapid labelling and analysis of orthoimages that speeds up semantic segmentation. TagLab follows a human-centered artificial intelligence approach that, by integrating multiple degrees of automation, empowers human capabilities. We evaluated TagLab's efficiency in annotation time and accuracy through a user study based on a highly challenging task: the semantic segmentation of coral communities in marine ecology. In the assisted labelling of corals, TagLab increased the annotation speed by approximately 90% for nonexpert annotators while preserving the labelling accuracy. Furthermore, human-machine interaction has improved the accuracy of fully automatic predictions by about 7% on average and by 14% when the model generalizes poorly. Considering the experience done through the user study, TagLab has been improved, and preliminary investigations suggest a further significant reduction in annotation times.Source: Journal of field robotics (2021). doi:10.1002/rob.22049
DOI: 10.1002/rob.22049
Metrics:
See at:
onlinelibrary.wiley.com | ISTI Repository | CNR ExploRA
2021
Conference article
Open Access
TagLab: A human-centric AI system for interactive semantic segmentation
Pavoni G., Corsini M., Ponchio F., Muntoni A., Cignoni P.
Fully automatic semantic segmentation of highly specific semantic classes and complex shapes may not meet the accuracy standards demanded by scientists. In such cases, human-centered AI solutions, able to assist operators while preserving human control over complex tasks, are a good trade-off to speed up image labeling while maintaining high accuracy levels. TagLab is an open-source AI-assisted software for annotating large orthoimages which takes advantage of different degrees of automation; it speeds up image annotation from scratch through assisted tools, creates custom fully automatic semantic segmentation models, and, finally, allows the quick edits of automatic predictions. Since the orthoimages analysis applies to several scientific disciplines, TagLab has been designed with a flexible labeling pipeline. We report our results in two different scenarios, marine ecology, and architectural heritage.Source: Human Centered AI Workshop at NeurIPS 2021 - Thirty-fifth Conference on Neural Information Processing Systems, Online event, 13/12/2021
DOI: 10.48550/arxiv.2112.12702
Metrics:
Pavoni G., Corsini M., Ponchio F., Muntoni A., Cignoni P.
Fully automatic semantic segmentation of highly specific semantic classes and complex shapes may not meet the accuracy standards demanded by scientists. In such cases, human-centered AI solutions, able to assist operators while preserving human control over complex tasks, are a good trade-off to speed up image labeling while maintaining high accuracy levels. TagLab is an open-source AI-assisted software for annotating large orthoimages which takes advantage of different degrees of automation; it speeds up image annotation from scratch through assisted tools, creates custom fully automatic semantic segmentation models, and, finally, allows the quick edits of automatic predictions. Since the orthoimages analysis applies to several scientific disciplines, TagLab has been designed with a flexible labeling pipeline. We report our results in two different scenarios, marine ecology, and architectural heritage.Source: Human Centered AI Workshop at NeurIPS 2021 - Thirty-fifth Conference on Neural Information Processing Systems, Online event, 13/12/2021
DOI: 10.48550/arxiv.2112.12702
Metrics:
See at:
arXiv.org e-Print Archive | ISTI Repository | doi.org | CNR ExploRA
2020
Journal article
Open Access
A State of the Art Technology in Large Scale Underwater Monitoring
Pavoni G., Corsini M., Cignoni P.
In recent decades, benthic populations have been subjected to recurrent episodes of mass mortality. These events have been blamed in part on declining water quality and elevated water temperatures (see Figure 1) correlated to global climate change. Ecosystems are enhanced by the presence of species with three-dimensional growth. The study of the growth, resilience, and recovery capability of those species provides valuable information on the conservation status of entire habitats. We discuss here a state-of-the art solution to speed up the monitoring of benthic population through the automatic or assisted analysis of underwater visual data.Source: ERCIM news 2020 (2020): 17–18.
Pavoni G., Corsini M., Cignoni P.
In recent decades, benthic populations have been subjected to recurrent episodes of mass mortality. These events have been blamed in part on declining water quality and elevated water temperatures (see Figure 1) correlated to global climate change. Ecosystems are enhanced by the presence of species with three-dimensional growth. The study of the growth, resilience, and recovery capability of those species provides valuable information on the conservation status of entire habitats. We discuss here a state-of-the art solution to speed up the monitoring of benthic population through the automatic or assisted analysis of underwater visual data.Source: ERCIM news 2020 (2020): 17–18.
See at:
ercim-news.ercim.eu | ISTI Repository | CNR ExploRA
2020
Journal article
Open Access
On improving the training of models for the semantic segmentation of benthic communities from orthographic imagery
Pavoni G., Corsini M., Callieri M., Fiameni G., Edwards C., Cignoni P.
The semantic segmentation of underwater imagery is an important step in the ecological analysis of coral habitats. To date, scientists produce fine-scale area annotations manually, an exceptionally time-consuming task that could be efficiently automatized by modern CNNs. This paper extends our previous work presented at the 3DUW'19 conference, outlining the workflow for the automated annotation of imagery from the first step of dataset preparation, to the last step of prediction reassembly. In particular, we propose an ecologically inspired strategy for an efficient dataset partition, an over-sampling methodology targeted on ortho-imagery, and a score fusion strategy. We also investigate the use of different loss functions in the optimization of a Deeplab V3+ model, to mitigate the class-imbalance problem and improve prediction accuracy on coral instance boundaries. The experimental results demonstrate the effectiveness of the ecologically inspired split in improving model performance, and quantify the advantages and limitations of the proposed over-sampling strategy. The extensive comparison of the loss functions gives numerous insights on the segmentation task; the Focal Tversky, typically used in the context of medical imaging (but not in remote sensing), results in the most convenient choice. By improving the accuracy of automated ortho image processing, the results presented here promise to meet the fundamental challenge of increasing the spatial and temporal scale of coral reef research, allowing researchers greater predictive ability to better manage coral reef resilience in the context of a changing environment.Source: Remote sensing (Basel) 12 (2020). doi:10.3390/RS12183106
DOI: 10.3390/rs12183106
Metrics:
Pavoni G., Corsini M., Callieri M., Fiameni G., Edwards C., Cignoni P.
The semantic segmentation of underwater imagery is an important step in the ecological analysis of coral habitats. To date, scientists produce fine-scale area annotations manually, an exceptionally time-consuming task that could be efficiently automatized by modern CNNs. This paper extends our previous work presented at the 3DUW'19 conference, outlining the workflow for the automated annotation of imagery from the first step of dataset preparation, to the last step of prediction reassembly. In particular, we propose an ecologically inspired strategy for an efficient dataset partition, an over-sampling methodology targeted on ortho-imagery, and a score fusion strategy. We also investigate the use of different loss functions in the optimization of a Deeplab V3+ model, to mitigate the class-imbalance problem and improve prediction accuracy on coral instance boundaries. The experimental results demonstrate the effectiveness of the ecologically inspired split in improving model performance, and quantify the advantages and limitations of the proposed over-sampling strategy. The extensive comparison of the loss functions gives numerous insights on the segmentation task; the Focal Tversky, typically used in the context of medical imaging (but not in remote sensing), results in the most convenient choice. By improving the accuracy of automated ortho image processing, the results presented here promise to meet the fundamental challenge of increasing the spatial and temporal scale of coral reef research, allowing researchers greater predictive ability to better manage coral reef resilience in the context of a changing environment.Source: Remote sensing (Basel) 12 (2020). doi:10.3390/RS12183106
DOI: 10.3390/rs12183106
Metrics:
See at:
Remote Sensing | ISTI Repository | Remote Sensing | Remote Sensing | CNR ExploRA
2020
Conference article
Open Access
Another Brick in the Wall: Improving the Assisted Semantic Segmentation of Masonry Walls
Pavoni G., Giuliani F., De Falco A., Corsini M., Ponchio F., Callieri M., Cignoni P.
In Architectural Heritage, the masonry's interpretation is an essential instrument for analyzing the construction phases, the assessment of structural properties, and the monitoring of its state of conservation. This work is generally carried out by specialists that, based on visual observation and their knowledge, manually annotate ortho-images of the masonry generated by photogrammetric surveys. This results in vectorial thematic maps segmented according to their construction technique (isolating areas of homogeneous materials/structure/texture) or state of conservation, including degradation areas and damaged parts. This time-consuming manual work, often done with tools that have not been designed for this purpose, represents a bottleneck in the documentation and management workflow and is a severely limiting factor in monitoring large-scale monuments (e.g.city walls). This paper explores the potential of AI-based solutions to improve the efficiency of masonry annotation in Architectural Heritage. This experimentation aims at providing interactive tools that support and empower the current workflow, benefiting from specialists' expertise.Source: 18th Eurographics Workshop on Graphics and Cultural Heritage, pp. 43–51, Online event, 18-19/11/2020
DOI: 10.2312/gch.20201291
Metrics:
Pavoni G., Giuliani F., De Falco A., Corsini M., Ponchio F., Callieri M., Cignoni P.
In Architectural Heritage, the masonry's interpretation is an essential instrument for analyzing the construction phases, the assessment of structural properties, and the monitoring of its state of conservation. This work is generally carried out by specialists that, based on visual observation and their knowledge, manually annotate ortho-images of the masonry generated by photogrammetric surveys. This results in vectorial thematic maps segmented according to their construction technique (isolating areas of homogeneous materials/structure/texture) or state of conservation, including degradation areas and damaged parts. This time-consuming manual work, often done with tools that have not been designed for this purpose, represents a bottleneck in the documentation and management workflow and is a severely limiting factor in monitoring large-scale monuments (e.g.city walls). This paper explores the potential of AI-based solutions to improve the efficiency of masonry annotation in Architectural Heritage. This experimentation aims at providing interactive tools that support and empower the current workflow, benefiting from specialists' expertise.Source: 18th Eurographics Workshop on Graphics and Cultural Heritage, pp. 43–51, Online event, 18-19/11/2020
DOI: 10.2312/gch.20201291
Metrics:
See at:
ISTI Repository | CNR ExploRA
2020
Journal article
Closed Access
Challenges in the deep learning-based semantic segmentation of benthic communities from Ortho-images
Pavoni G., Corsini M., Pedersen N., Petrovic V., Cignoni P.
Since the early days of the low-cost camera development, the collection of visual data has become a common practice in the underwater monitoring field. Nevertheless, video and image sequences are a trustworthy source of knowledge that remains partially untapped. Human-based image analysis is a time-consuming task that creates a bottleneck between data collection and extrapolation. Nowadays, the annotation of biologically meaningful information from imagery can be efficiently automated or accelerated by convolutional neural networks (CNN). Presenting our case studies, we offer an overview of the potentialities and difficulties of accurate automatic recognition and segmentation of benthic species. This paper focuses on the application of deep learning techniques to multi-view stereo reconstruction by-products (registered images, point clouds, ortho-projections), considering the proliferation of these techniques among the marine science community. Of particular importance is the need to semantically segment imagery in order to generate demographic data vital to understand and explore the changes happening within marine communities.Source: Applied geomatics (Print) (2020). doi:10.1007/s12518-020-00331-6
DOI: 10.1007/s12518-020-00331-6
Metrics:
Pavoni G., Corsini M., Pedersen N., Petrovic V., Cignoni P.
Since the early days of the low-cost camera development, the collection of visual data has become a common practice in the underwater monitoring field. Nevertheless, video and image sequences are a trustworthy source of knowledge that remains partially untapped. Human-based image analysis is a time-consuming task that creates a bottleneck between data collection and extrapolation. Nowadays, the annotation of biologically meaningful information from imagery can be efficiently automated or accelerated by convolutional neural networks (CNN). Presenting our case studies, we offer an overview of the potentialities and difficulties of accurate automatic recognition and segmentation of benthic species. This paper focuses on the application of deep learning techniques to multi-view stereo reconstruction by-products (registered images, point clouds, ortho-projections), considering the proliferation of these techniques among the marine science community. Of particular importance is the need to semantically segment imagery in order to generate demographic data vital to understand and explore the changes happening within marine communities.Source: Applied geomatics (Print) (2020). doi:10.1007/s12518-020-00331-6
DOI: 10.1007/s12518-020-00331-6
Metrics:
See at:
Applied Geomatics | link.springer.com | CNR ExploRA
2019
Conference article
Open Access
Visual acquisition system for georeferenced monitoring and reconstruction of the sea bottom using audio for data synchronisation
Costanzi R., Gasparri A., Pacienza F., Pollini L., Caiti A., Manzari V., Terracciano D., Stifani M., Pavoni G., Callieri M., Barbieri C., Colonna F., Gaino F., Moretto P., Pepe C. E., Romanelli A.
This work describes a Smart Dive Scooter (SDS) to be used as a support tool for monitoring application of different marine species. Professional divers of Environmental Protection Agencies are periodically involved in monitoring activities. Ligurian Regional Agency for the Environmental Protection (ARPAL) and the University of Pisa (UNIPI) are collaborating towards the integration of classical methodologies with ICT tools to support the work of divers in terms of safety, cost effectiveness and time effectiveness. The SDS is the first step in this direction. It is a classical Dive Scooter, used for rapid movements underwater, that is integrated with sensors for environment monitoring (a set of cameras) and for data georeferencing (acoustic localisation system). The SDS will be used by ARPAL divers to quickly acquire images of the bottom of a target area. Processing of optical and positioning data will allow to build a virtual model on which perform all the analysis and measurement activities. This approach results in limiting the time underwater for operators increasing the area mapper per each dive. The paper focuses on the technique used for the synchronization of optical data among the various cameras and of them with the acoustic position measurements. This goal is obtained exploiting the audio tracks acquired by the cameras avoiding the necessity of bulky and energy expensive dedicated computers. Results of the validation based on experimental data collected at sea are reported.Source: OCEANS 2019, Marseille, 17-20/6/2019
DOI: 10.1109/oceanse.2019.8867123
Metrics:
Costanzi R., Gasparri A., Pacienza F., Pollini L., Caiti A., Manzari V., Terracciano D., Stifani M., Pavoni G., Callieri M., Barbieri C., Colonna F., Gaino F., Moretto P., Pepe C. E., Romanelli A.
This work describes a Smart Dive Scooter (SDS) to be used as a support tool for monitoring application of different marine species. Professional divers of Environmental Protection Agencies are periodically involved in monitoring activities. Ligurian Regional Agency for the Environmental Protection (ARPAL) and the University of Pisa (UNIPI) are collaborating towards the integration of classical methodologies with ICT tools to support the work of divers in terms of safety, cost effectiveness and time effectiveness. The SDS is the first step in this direction. It is a classical Dive Scooter, used for rapid movements underwater, that is integrated with sensors for environment monitoring (a set of cameras) and for data georeferencing (acoustic localisation system). The SDS will be used by ARPAL divers to quickly acquire images of the bottom of a target area. Processing of optical and positioning data will allow to build a virtual model on which perform all the analysis and measurement activities. This approach results in limiting the time underwater for operators increasing the area mapper per each dive. The paper focuses on the technique used for the synchronization of optical data among the various cameras and of them with the acoustic position measurements. This goal is obtained exploiting the audio tracks acquired by the cameras avoiding the necessity of bulky and energy expensive dedicated computers. Results of the validation based on experimental data collected at sea are reported.Source: OCEANS 2019, Marseille, 17-20/6/2019
DOI: 10.1109/oceanse.2019.8867123
Metrics:
See at:
ISTI Repository | doi.org | ieeexplore.ieee.org | CNR ExploRA
2019
Journal article
Open Access
Semantic segmentation of Benthic communities from ortho-mosaic maps
Pavoni G., Corsini M., Callieri M., Palma M., Scopigno R.
Visual sampling techniques represent a valuable resource for a rapid, non-invasive data acquisition for underwater monitoring purposes. Long-term monitoring projects usually requires the collection of large quantities of data, and the visual analysis of a human expert operator remains, in this context, a very time consuming task. It has been estimated that only the 1-2% of the acquired images are later analyzed by scientists (Beijbom et al., 2012). Strategies for the automatic recognition of benthic communities are required to effectively exploit all the information contained in visual data. Supervised learning methods, the most promising classification techniques in this field, are commonly affected by two recurring issues: the wide diversity of marine organism, and the small amount of labeled data. In this work, we discuss the advantages offered by the use of annotated high resolution ortho-mosaics of seabed to classify and segment the investigated specimens, and we suggest several strategies to obtain a considerable per-pixel classification performance although the use of a reduced training dataset composed by a single ortho-mosaic. The proposed methodology can be applied to a large number of different species, making the procedure of marine organism identification an highly adaptable task.Source: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences (ISPRS Annals) 42 (2019): 151–158. doi:10.5194/isprs-archives-XLII-2-W10-151-2019
DOI: 10.5194/isprs-archives-xlii-2-w10-151-2019
Project(s): GreenBubbles
Metrics:
Pavoni G., Corsini M., Callieri M., Palma M., Scopigno R.
Visual sampling techniques represent a valuable resource for a rapid, non-invasive data acquisition for underwater monitoring purposes. Long-term monitoring projects usually requires the collection of large quantities of data, and the visual analysis of a human expert operator remains, in this context, a very time consuming task. It has been estimated that only the 1-2% of the acquired images are later analyzed by scientists (Beijbom et al., 2012). Strategies for the automatic recognition of benthic communities are required to effectively exploit all the information contained in visual data. Supervised learning methods, the most promising classification techniques in this field, are commonly affected by two recurring issues: the wide diversity of marine organism, and the small amount of labeled data. In this work, we discuss the advantages offered by the use of annotated high resolution ortho-mosaics of seabed to classify and segment the investigated specimens, and we suggest several strategies to obtain a considerable per-pixel classification performance although the use of a reduced training dataset composed by a single ortho-mosaic. The proposed methodology can be applied to a large number of different species, making the procedure of marine organism identification an highly adaptable task.Source: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences (ISPRS Annals) 42 (2019): 151–158. doi:10.5194/isprs-archives-XLII-2-W10-151-2019
DOI: 10.5194/isprs-archives-xlii-2-w10-151-2019
Project(s): GreenBubbles
Metrics:
See at:
ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences | ISTI Repository | ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences | ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences | www.int-arch-photogramm-remote-sens-spatial-inf-sci.net | CNR ExploRA
2019
Conference article
Open Access
A Validation Tool For Improving Semantic Segmentation of Complex Natural Structures
Pavoni G., Corsini M., Palma M., Scopigno R.
The automatic recognition of natural structures is a challenging task in the supervised learning field. Complex morphologies are difficult to detect both from the networks, that may suffer from generalization issues, and from human operators, affecting the consistency of training datasets. The task of manual annotating biological structures is not comparable to a generic task of detecting an object (a car, a cat, or a flower) within an image. Biological structures are more similar to textures, and specimen borders exhibit intricate shapes. In this specific context, manual labelling is very sensitive to human error. The interactive validation of the predictions is a valuable resource to improve the network performance and address the inaccuracy caused by the lack of annotation consistency of human operators reported in literature. The proposed tool, inspired by the Yes/No Answer paradigm, integrates the semantic segmentation results coming from a CNN with the previous human labeling, allowing a more accurate annotation of thousands of instances in a short time. At the end of the validation, it is possible to obtain corrected statistics or export the integrated dataset and re-train the network.Source: Eurographics 2019, pp. 57–60, Genova, 6/5/2019-10/5/2019
DOI: 10.2312/egs.20191014
Metrics:
Pavoni G., Corsini M., Palma M., Scopigno R.
The automatic recognition of natural structures is a challenging task in the supervised learning field. Complex morphologies are difficult to detect both from the networks, that may suffer from generalization issues, and from human operators, affecting the consistency of training datasets. The task of manual annotating biological structures is not comparable to a generic task of detecting an object (a car, a cat, or a flower) within an image. Biological structures are more similar to textures, and specimen borders exhibit intricate shapes. In this specific context, manual labelling is very sensitive to human error. The interactive validation of the predictions is a valuable resource to improve the network performance and address the inaccuracy caused by the lack of annotation consistency of human operators reported in literature. The proposed tool, inspired by the Yes/No Answer paradigm, integrates the semantic segmentation results coming from a CNN with the previous human labeling, allowing a more accurate annotation of thousands of instances in a short time. At the end of the validation, it is possible to obtain corrected statistics or export the integrated dataset and re-train the network.Source: Eurographics 2019, pp. 57–60, Genova, 6/5/2019-10/5/2019
DOI: 10.2312/egs.20191014
Metrics:
See at:
diglib.eg.org | ISTI Repository | CNR ExploRA
2018
Journal article
Open Access
Quasi-orthorectified projection for the measurement of red gorgonian colonies
Pavoni G., Palma M., Callieri M., Dellepiane M., Cerrano C., Pantaleo U., Scopigno R.
This study presents a practical method to estimate dimensions of Paramuricea clavata colonies using generic photographic datasets collected across wide areas. Paramuricea clavata is a non-rigid, tree-like octocoral; this morphology greatly affects the quality of the sea fans multi-view stereo matching reconstruction, resulting in hazy and incoherent clouds, full of "false" points with random orientation. Therefore, the standard procedure to take measurements over a reconstructed textured surface in 3D space is impractical. Our method overcomes this problem by using quasi-orthorectified images, produced by projecting registered photos on the plane that best fits the point cloud of the colony. The assessments of the measures collected have been performed comparing ground truth data set and time series images of the same set of colonies. The measurement errors fall below the requirements for this type of ecological observations. Compared to previous works, the presented method does not require a detailed reconstruction of individual colonies, but relies on a global multi-view stereo reconstruction performed through a comprehensive photographic coverage of the area of interest, using a low-cost pre-calibrated camera. This approach drastically reduces the time spent working on the field, helping practitioners and scientists in improving efficiency and accuracy in their monitoring plans.Source: The international archives of the photogrammetry, remote sensing and spatial information sciences (Print) 42 (2018): 853–860. doi:10.5194/isprs-archives-XLII-2-853-2018
DOI: 10.5194/isprs-archives-xlii-2-853-2018
Metrics:
Pavoni G., Palma M., Callieri M., Dellepiane M., Cerrano C., Pantaleo U., Scopigno R.
This study presents a practical method to estimate dimensions of Paramuricea clavata colonies using generic photographic datasets collected across wide areas. Paramuricea clavata is a non-rigid, tree-like octocoral; this morphology greatly affects the quality of the sea fans multi-view stereo matching reconstruction, resulting in hazy and incoherent clouds, full of "false" points with random orientation. Therefore, the standard procedure to take measurements over a reconstructed textured surface in 3D space is impractical. Our method overcomes this problem by using quasi-orthorectified images, produced by projecting registered photos on the plane that best fits the point cloud of the colony. The assessments of the measures collected have been performed comparing ground truth data set and time series images of the same set of colonies. The measurement errors fall below the requirements for this type of ecological observations. Compared to previous works, the presented method does not require a detailed reconstruction of individual colonies, but relies on a global multi-view stereo reconstruction performed through a comprehensive photographic coverage of the area of interest, using a low-cost pre-calibrated camera. This approach drastically reduces the time spent working on the field, helping practitioners and scientists in improving efficiency and accuracy in their monitoring plans.Source: The international archives of the photogrammetry, remote sensing and spatial information sciences (Print) 42 (2018): 853–860. doi:10.5194/isprs-archives-XLII-2-853-2018
DOI: 10.5194/isprs-archives-xlii-2-853-2018
Metrics:
See at:
ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences | ISTI Repository | ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences | CNR ExploRA
2018
Journal article
Open Access
SfM-based method to assess gorgonian forests (Paramuricea clavata (Cnidaria, Octocorallia))
Palma M., Casado M. R., Pantaleo U., Pavoni G., Pica D., Cerrano C.
Animal forests promote marine habitats morphological complexity and functioning. The red gorgonian, Paramuricea clavata, is a key structuring species of the Mediterranean coralligenous habitat and an indicator species of climate effects on habitat functioning. P. clavata metrics such as population structure, morphology and biomass inform on the overall health of coralligenous habitats, but the estimation of these metrics is time and cost consuming, and often requires destructive sampling. As a consequence, the implementation of long-term and wide-area monitoring programmes is limited. This study proposes a novel and transferable Structure from Motion (SfM) based method for the estimation of gorgonian population structure (i.e., maximal height, density, abundance), morphometries (i.e., maximal width, fan surface) and biomass (i.e., coenenchymal Dry Weight, Ash Free DriedWeight). The method includes the estimation of a novel metric (3D canopy surface) describing the gorgonian forest as a mosaic of planes generated by fitting multiple 5 cm × 5 cm facets to a SfM generated point cloud. The performance of the method is assessed for two different cameras (GoPro Hero4 and Sony NEX7). Results showed that for highly dense populations (17 colonies/m2), the SfM-method had lower accuracies in estimating the gorgonians density for both cameras (60% to 89%) than for medium to low density populations (14 and 7 colonies/m2) (71% to 100%). Results for the validation of the method showed that the correlation between ground truth and SfM estimates for maximal height, maximal width and fan surface were between R2 = 0.63 and R2 = 0.9, and R2 = 0.99 for coenenchymal surface estimation. The methodological approach was used to estimate the biomass of the gorgonian population within the study area and across the coralligenous habitat between -25 to -40 m depth in the Portofino Marine Protected Area. For that purpose, the coenenchymal surface of sampled colonies was obtained and used for the calculations. Results showed biomass values of dry weight and ash free dry weight of 220 g and 32 g for the studied area and to 365 kg and 55 Kg for the coralligenous habitat in the Marine Protected Area. This study highlighted the feasibility of the methodology for the quantification of P. clavata metrics as well as the potential of the SfM-method to improve current predictions of the status of the coralligenous habitat in the Mediterranean sea and overall management of threatened ecosystems. © 2018 by the authors.Source: Remote sensing (Basel) 10 (2018): 1–21. doi:10.3390/rs10071154
DOI: 10.3390/rs10071154
Metrics:
Palma M., Casado M. R., Pantaleo U., Pavoni G., Pica D., Cerrano C.
Animal forests promote marine habitats morphological complexity and functioning. The red gorgonian, Paramuricea clavata, is a key structuring species of the Mediterranean coralligenous habitat and an indicator species of climate effects on habitat functioning. P. clavata metrics such as population structure, morphology and biomass inform on the overall health of coralligenous habitats, but the estimation of these metrics is time and cost consuming, and often requires destructive sampling. As a consequence, the implementation of long-term and wide-area monitoring programmes is limited. This study proposes a novel and transferable Structure from Motion (SfM) based method for the estimation of gorgonian population structure (i.e., maximal height, density, abundance), morphometries (i.e., maximal width, fan surface) and biomass (i.e., coenenchymal Dry Weight, Ash Free DriedWeight). The method includes the estimation of a novel metric (3D canopy surface) describing the gorgonian forest as a mosaic of planes generated by fitting multiple 5 cm × 5 cm facets to a SfM generated point cloud. The performance of the method is assessed for two different cameras (GoPro Hero4 and Sony NEX7). Results showed that for highly dense populations (17 colonies/m2), the SfM-method had lower accuracies in estimating the gorgonians density for both cameras (60% to 89%) than for medium to low density populations (14 and 7 colonies/m2) (71% to 100%). Results for the validation of the method showed that the correlation between ground truth and SfM estimates for maximal height, maximal width and fan surface were between R2 = 0.63 and R2 = 0.9, and R2 = 0.99 for coenenchymal surface estimation. The methodological approach was used to estimate the biomass of the gorgonian population within the study area and across the coralligenous habitat between -25 to -40 m depth in the Portofino Marine Protected Area. For that purpose, the coenenchymal surface of sampled colonies was obtained and used for the calculations. Results showed biomass values of dry weight and ash free dry weight of 220 g and 32 g for the studied area and to 365 kg and 55 Kg for the coralligenous habitat in the Marine Protected Area. This study highlighted the feasibility of the methodology for the quantification of P. clavata metrics as well as the potential of the SfM-method to improve current predictions of the status of the coralligenous habitat in the Mediterranean sea and overall management of threatened ecosystems. © 2018 by the authors.Source: Remote sensing (Basel) 10 (2018): 1–21. doi:10.3390/rs10071154
DOI: 10.3390/rs10071154
Metrics:
See at:
Remote Sensing | Cranfield CERES | ISTI Repository | Remote Sensing | Remote Sensing | CNR ExploRA
2016
Conference article
Restricted
A virtual experience across the buried history
Canzoneri A., Pavoni G., Callieri M., Dellepiane M., Pingi P., De Giorgi M., Scopigno R.
The Sant'Angelo cave church is an underground medieval Benedictine complex in the south of Italy, affected by serious structural and chemical degradation. In the context of a documentation campaign promoted by the local Superintendence and supported by the IPERIONCH. it project, we carried out an accurate 3D and photographic survey, and reconstructed a detailed 3D model of the site (encoding shape and colour). While the primary purpose of this large amount of collected data was to provide a metric documentation of the site, the completeness and the high detail of the survey suggested also a possible use for dissemination and virtual presentation. Thus, we exploited the 3D digital models to design and build a virtual visit of the church, oriented to scholars, museums and tourists. This paper describes the design and implementation of this educational experience, closely related to the bibliographic sources of the artistic heritage, fully enriched with hyper-textual information, intuitive and easy to use for all users regardless of their level of familiarity with the 3D medium.Source: AVR 2016 - Augmented Reality, Virtual Reality, and Computer Graphics. Third International Conference, pp. 158–171, Lecce, Italy, 15-18 June 2016
DOI: 10.1007/978-3-319-40651-0_13
Metrics:
Canzoneri A., Pavoni G., Callieri M., Dellepiane M., Pingi P., De Giorgi M., Scopigno R.
The Sant'Angelo cave church is an underground medieval Benedictine complex in the south of Italy, affected by serious structural and chemical degradation. In the context of a documentation campaign promoted by the local Superintendence and supported by the IPERIONCH. it project, we carried out an accurate 3D and photographic survey, and reconstructed a detailed 3D model of the site (encoding shape and colour). While the primary purpose of this large amount of collected data was to provide a metric documentation of the site, the completeness and the high detail of the survey suggested also a possible use for dissemination and virtual presentation. Thus, we exploited the 3D digital models to design and build a virtual visit of the church, oriented to scholars, museums and tourists. This paper describes the design and implementation of this educational experience, closely related to the bibliographic sources of the artistic heritage, fully enriched with hyper-textual information, intuitive and easy to use for all users regardless of their level of familiarity with the 3D medium.Source: AVR 2016 - Augmented Reality, Virtual Reality, and Computer Graphics. Third International Conference, pp. 158–171, Lecce, Italy, 15-18 June 2016
DOI: 10.1007/978-3-319-40651-0_13
Metrics:
See at:
doi.org | link.springer.com | CNR ExploRA
2016
Conference article
Restricted
Automatic selection of video frames for path regularization and 3D reconstruction
Pavoni G., Dellepiane M., Callieri M., Scopigno R.
Video sequences can be a valuable source to document the state of objects and sites. They are easy to acquire and they usually ensure a complete coverage of the object of interest. One of their possible uses is to recover the acquisition path, or the 3D shape of the scene. This can be done by applying structure-from-motion techniques to a representative set of frames extracted from the video. This paper presents an automatic method for the extraction of a predefined number of representative frames that ensures an accurate reconstruction of the sequence path, and possibly enhances the 3D reconstruction of the scene. The automatic extraction is obtained by analyzing adjacent frames in a starting subset, and adding/removing frames so that the distance between them remains constant. This ensures the reconstruction of a regularized path and an optimized coverage of all the scene. Finally, more frames are added in the portions of the sequence when more detailed objects are framed. This ensures a better description of the sequence, and a more accurate dense reconstruction. The method is automatic, fast and independent from any assumption about the acquired object or the acquisition strategy. It was tested on a variety of different video sequences, showing that a satisfying result can be obtained regardless of the length and quality of the input.Source: GCH 2016 - Eurographics Workshop on Graphics and Cultural Heritage, Genova, Italy, 5-7 October 2016
DOI: 10.2312/gch.20161376
Metrics:
Pavoni G., Dellepiane M., Callieri M., Scopigno R.
Video sequences can be a valuable source to document the state of objects and sites. They are easy to acquire and they usually ensure a complete coverage of the object of interest. One of their possible uses is to recover the acquisition path, or the 3D shape of the scene. This can be done by applying structure-from-motion techniques to a representative set of frames extracted from the video. This paper presents an automatic method for the extraction of a predefined number of representative frames that ensures an accurate reconstruction of the sequence path, and possibly enhances the 3D reconstruction of the scene. The automatic extraction is obtained by analyzing adjacent frames in a starting subset, and adding/removing frames so that the distance between them remains constant. This ensures the reconstruction of a regularized path and an optimized coverage of all the scene. Finally, more frames are added in the portions of the sequence when more detailed objects are framed. This ensures a better description of the sequence, and a more accurate dense reconstruction. The method is automatic, fast and independent from any assumption about the acquired object or the acquisition strategy. It was tested on a variety of different video sequences, showing that a satisfying result can be obtained regardless of the length and quality of the input.Source: GCH 2016 - Eurographics Workshop on Graphics and Cultural Heritage, Genova, Italy, 5-7 October 2016
DOI: 10.2312/gch.20161376
Metrics:
See at:
diglib.eg.org | CNR ExploRA
2015
Conference article
Restricted
Alchemy in 3D: a digitization for a journey through matter
Callieri M., Pingi P., Potenziani M., Dellepiane M., Pavoni G., Lureau A., Scopigno R.
In this work, we will present the outcomes of the 3D diagnostic investigations carried out on the painting Alchemy by Jackson Pollock. Thanks to an accurate digitization and a careful processing, we were able to generate a very precise high-resolution 3D model that proved to be useful in different stages of the diagnostic and conservation campaign. The 3D model was integrated in the conservation process, along with the other diagnostic investigations; the geometric data was also used to produce images and video sequences for dissemination purposes. The most interesting aspect of the work, however, was the idea of going beyond photo-realism and the use of the scanner-measured geometry to try to interpret and understand the traces and signs on the surface of the painting, in relation with the gestures and techniques used by Pollock while painting this masterpiece. Combining the knowledge of the curators and the metric data gathered in the digitization, we were able to discover and validate several interesting aspects of the painting, in the direction of trying to better understanding the painting process which was, in the idea of the artist, an essential part of the artwork. The 3D model of the artwork played a central role also in the temporary exhibition created for the dissemination of the conservation and the diagnostic campaign to the museum visitors. This was also done following the idea of using the geometry to explain the gestures, actions and techniques of Jackson Pollock at work. The 3D model was used to create an interactive kiosk, to have the visitors navigate the model and access explanations of relevant geometrical details and to produce a 1:1 physical reproduction to give the public the possibility to physically interact with the artwork.Source: International Congress on Digital Heritage, pp. 223–231, Granada, Spain, 28/08/2015-02/10/2015
DOI: 10.1109/digitalheritage.2015.7413875
Metrics:
Callieri M., Pingi P., Potenziani M., Dellepiane M., Pavoni G., Lureau A., Scopigno R.
In this work, we will present the outcomes of the 3D diagnostic investigations carried out on the painting Alchemy by Jackson Pollock. Thanks to an accurate digitization and a careful processing, we were able to generate a very precise high-resolution 3D model that proved to be useful in different stages of the diagnostic and conservation campaign. The 3D model was integrated in the conservation process, along with the other diagnostic investigations; the geometric data was also used to produce images and video sequences for dissemination purposes. The most interesting aspect of the work, however, was the idea of going beyond photo-realism and the use of the scanner-measured geometry to try to interpret and understand the traces and signs on the surface of the painting, in relation with the gestures and techniques used by Pollock while painting this masterpiece. Combining the knowledge of the curators and the metric data gathered in the digitization, we were able to discover and validate several interesting aspects of the painting, in the direction of trying to better understanding the painting process which was, in the idea of the artist, an essential part of the artwork. The 3D model of the artwork played a central role also in the temporary exhibition created for the dissemination of the conservation and the diagnostic campaign to the museum visitors. This was also done following the idea of using the geometry to explain the gestures, actions and techniques of Jackson Pollock at work. The 3D model was used to create an interactive kiosk, to have the visitors navigate the model and access explanations of relevant geometrical details and to produce a 1:1 physical reproduction to give the public the possibility to physically interact with the artwork.Source: International Congress on Digital Heritage, pp. 223–231, Granada, Spain, 28/08/2015-02/10/2015
DOI: 10.1109/digitalheritage.2015.7413875
Metrics:
See at:
doi.org | ieeexplore.ieee.org | CNR ExploRA
2015
Other
Unknown
Jackson Pollock: Alchemy in 3D
Callieri M., Pingi P., Potenziani M., Dellepiane M., Pavoni G., Lureau A., Scopigno R.
For the exhibition "Alchemy of Jackson Pollock. Discovering the Artist at Work" (February - October 2015, Peggy Guggenheim Collection, Venice), the ISTI-CNR Visual Computing Laboratory created an interactive kiosk to explore the geometry of the painting Alchemy by Jackson Pollock. The kiosk created aims to show the technique and secrets of the artist at work, and enriches an innovative and successful exhibition (more than 180,000 visitors in eight months), replicated shortly afterwards in the same form at the Guggenheim Collection Museum in New York, and adapted for the traveling exhibition "The Art of Science" held in 2021 in Mexico and 2022 in Chile). The interactive kiosk, entirely developed by ISTI-CNR Visual Computing Laboratory, allows you to view the high-resolution 3D model of the entire painting through a touch-screen. The visitor can closely observe the geometry and color of the painting, move the light to appreciate its surface, and select the hot-spots that highlight areas of interest. The kiosk was developed with HTML/JavaScript technology, and is based on the HTML5 WebGL API, which makes 2D/3D rendering possible without plug-ins on all major web browsers. The use of web technologies allows installation on a stand-alone machine (as for the exhibition) but at the same time publication on the web. The generation of multiresolution 3D models, and their web visualization, is managed by the Nexus, SpiderGL, and 3DHOP software, developed by ISTI-CNR Visual Computing Laboratory. The multimedia product "Jackson Pollock: Alchemy in 3D" was nominated in the "Best Use of DH For Public Engagement" category at the Digital Humanities 2015 Awards, placing first.
Callieri M., Pingi P., Potenziani M., Dellepiane M., Pavoni G., Lureau A., Scopigno R.
For the exhibition "Alchemy of Jackson Pollock. Discovering the Artist at Work" (February - October 2015, Peggy Guggenheim Collection, Venice), the ISTI-CNR Visual Computing Laboratory created an interactive kiosk to explore the geometry of the painting Alchemy by Jackson Pollock. The kiosk created aims to show the technique and secrets of the artist at work, and enriches an innovative and successful exhibition (more than 180,000 visitors in eight months), replicated shortly afterwards in the same form at the Guggenheim Collection Museum in New York, and adapted for the traveling exhibition "The Art of Science" held in 2021 in Mexico and 2022 in Chile). The interactive kiosk, entirely developed by ISTI-CNR Visual Computing Laboratory, allows you to view the high-resolution 3D model of the entire painting through a touch-screen. The visitor can closely observe the geometry and color of the painting, move the light to appreciate its surface, and select the hot-spots that highlight areas of interest. The kiosk was developed with HTML/JavaScript technology, and is based on the HTML5 WebGL API, which makes 2D/3D rendering possible without plug-ins on all major web browsers. The use of web technologies allows installation on a stand-alone machine (as for the exhibition) but at the same time publication on the web. The generation of multiresolution 3D models, and their web visualization, is managed by the Nexus, SpiderGL, and 3DHOP software, developed by ISTI-CNR Visual Computing Laboratory. The multimedia product "Jackson Pollock: Alchemy in 3D" was nominated in the "Best Use of DH For Public Engagement" category at the Digital Humanities 2015 Awards, placing first.
See at: CNR ExploRA | vcg.isti.cnr.it