AHCI RESEARCH GROUP
Publications
Papers published in international journals,
proceedings of conferences, workshops and books.
OUR RESEARCH
Scientific Publications
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You can use the tag cloud to select only the papers dealing with specific research topics.
You can expand the Abstract, Links and BibTex record for each paper.
2024
Rausa, Maria; Gaglio, Salvatore; Augello, Agnese; Caggianese, Giuseppe; Franchini, Silvia; Gallo, Luigi; Sabatucci, Luca
Enriching Metaverse with Memories Through Generative AI: A Case Study Proceedings Article
In: 2024 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE), pp. 371–376, Institute of Electrical and Electronics Engineers Inc., St Albans, United Kingdom, 2024.
Abstract | Links | BibTeX | Tags: Artificial intelligence, Metaverse, Modeling, Virtual Reality
@inproceedings{rausa_enriching_2024,
title = {Enriching Metaverse with Memories Through Generative AI: A Case Study},
author = {Maria Rausa and Salvatore Gaglio and Agnese Augello and Giuseppe Caggianese and Silvia Franchini and Luigi Gallo and Luca Sabatucci},
url = {https://ieeexplore.ieee.org/abstract/document/10796338},
doi = {10.1109/MetroXRAINE62247.2024.10796338},
year = {2024},
date = {2024-10-01},
urldate = {2025-01-08},
booktitle = {2024 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE)},
pages = {371–376},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
address = {St Albans, United Kingdom},
abstract = {The paper introduces MetaMemory, an approach to generate 3D models from either textual descriptions or photographs of objects, offering dual input modes for enhanced representation. MetaMemory's architecture is discussed presenting the tools employed in extracting the object from the image, generating the 3D mesh from texts or images, and visualizing the object reconstruction in an immersive scenario. Afterwards, a case study in which we experienced reconstructing memories of ancient crafts is examined together with the achieved results, by highlighting current limitations and potential applications.},
keywords = {Artificial intelligence, Metaverse, Modeling, Virtual Reality},
pubstate = {published},
tppubtype = {inproceedings}
}
The paper introduces MetaMemory, an approach to generate 3D models from either textual descriptions or photographs of objects, offering dual input modes for enhanced representation. MetaMemory's architecture is discussed presenting the tools employed in extracting the object from the image, generating the 3D mesh from texts or images, and visualizing the object reconstruction in an immersive scenario. Afterwards, a case study in which we experienced reconstructing memories of ancient crafts is examined together with the achieved results, by highlighting current limitations and potential applications.
2018
Cascia, Marco La; Vassallo, Giorgio; Gallo, Luigi; Pilato, Giovanni; Vella, Filippo
Automatic Image Annotation Using Random Projection in a Conceptual Space Induced from Data Proceedings Article
In: 2018 14th International Conference on Signal-Image Technology Internet-Based Systems (SITIS), pp. 464–471, 2018.
Abstract | Links | BibTeX | Tags: Feature extraction, Hidden Markov models, Image annotation, Modeling, Semantics, Visualization
@inproceedings{lacasciaAutomaticImageAnnotation2018,
title = {Automatic Image Annotation Using Random Projection in a Conceptual Space Induced from Data},
author = { Marco La Cascia and Giorgio Vassallo and Luigi Gallo and Giovanni Pilato and Filippo Vella},
doi = {10.1109/SITIS.2018.00077},
year = {2018},
date = {2018-11-01},
booktitle = {2018 14th International Conference on Signal-Image Technology Internet-Based Systems (SITIS)},
pages = {464--471},
abstract = {The main drawback of a detailed representation of visual content, whatever is its origin, is that significant features are very high dimensional. To keep the problem tractable while preserving the semantic content, a dimensionality reduction of the data is needed. We propose the Random Projection techniques to reduce the dimensionality. Even though this technique is sub-optimal with respect to Singular Value Decomposition its much lower computational cost make it more suitable for this problem and in particular when computational resources are limited such as in mobile terminals. In this paper we present the use of a ``conceptual'' space, automatically induced from data, to perform automatic image annotation. Images are represented by visual features based on color and texture and arranged as histograms of visual terms and bigrams to partially preserve the spatial information [1]. Using a set of annotated images as training data, the matrix of visual features is built and dimensionality reduction is performed using the Random Projection algorithm. A new unannotated image is then projected into the dimensionally reduced space and the labels of the closest training images are assigned to the unannotated image itself. Experiments on large real collection of images showed that the approach, despite of its low computational cost, is very effective.},
keywords = {Feature extraction, Hidden Markov models, Image annotation, Modeling, Semantics, Visualization},
pubstate = {published},
tppubtype = {inproceedings}
}
The main drawback of a detailed representation of visual content, whatever is its origin, is that significant features are very high dimensional. To keep the problem tractable while preserving the semantic content, a dimensionality reduction of the data is needed. We propose the Random Projection techniques to reduce the dimensionality. Even though this technique is sub-optimal with respect to Singular Value Decomposition its much lower computational cost make it more suitable for this problem and in particular when computational resources are limited such as in mobile terminals. In this paper we present the use of a ``conceptual'' space, automatically induced from data, to perform automatic image annotation. Images are represented by visual features based on color and texture and arranged as histograms of visual terms and bigrams to partially preserve the spatial information [1]. Using a set of annotated images as training data, the matrix of visual features is built and dimensionality reduction is performed using the Random Projection algorithm. A new unannotated image is then projected into the dimensionally reduced space and the labels of the closest training images are assigned to the unannotated image itself. Experiments on large real collection of images showed that the approach, despite of its low computational cost, is very effective.
Cascia, Marco La; Vassallo, Giorgio; Gallo, Luigi; Pilato, Giovanni; Vella, Filippo
Automatic Image Annotation Using Random Projection in a Conceptual Space Induced from Data Proceedings Article
In: 2018 14th International Conference on Signal-Image Technology Internet-Based Systems (SITIS), pp. 464–471, 2018.
Abstract | Links | BibTeX | Tags: Feature extraction, Hidden Markov models, Image annotation, Modeling, Semantics, Visualization
@inproceedings{la_cascia_automatic_2018,
title = {Automatic Image Annotation Using Random Projection in a Conceptual Space Induced from Data},
author = {Marco La Cascia and Giorgio Vassallo and Luigi Gallo and Giovanni Pilato and Filippo Vella},
doi = {10.1109/SITIS.2018.00077},
year = {2018},
date = {2018-11-01},
booktitle = {2018 14th International Conference on Signal-Image Technology Internet-Based Systems (SITIS)},
pages = {464–471},
abstract = {The main drawback of a detailed representation of visual content, whatever is its origin, is that significant features are very high dimensional. To keep the problem tractable while preserving the semantic content, a dimensionality reduction of the data is needed. We propose the Random Projection techniques to reduce the dimensionality. Even though this technique is sub-optimal with respect to Singular Value Decomposition its much lower computational cost make it more suitable for this problem and in particular when computational resources are limited such as in mobile terminals. In this paper we present the use of a “conceptual” space, automatically induced from data, to perform automatic image annotation. Images are represented by visual features based on color and texture and arranged as histograms of visual terms and bigrams to partially preserve the spatial information [1]. Using a set of annotated images as training data, the matrix of visual features is built and dimensionality reduction is performed using the Random Projection algorithm. A new unannotated image is then projected into the dimensionally reduced space and the labels of the closest training images are assigned to the unannotated image itself. Experiments on large real collection of images showed that the approach, despite of its low computational cost, is very effective.},
keywords = {Feature extraction, Hidden Markov models, Image annotation, Modeling, Semantics, Visualization},
pubstate = {published},
tppubtype = {inproceedings}
}
The main drawback of a detailed representation of visual content, whatever is its origin, is that significant features are very high dimensional. To keep the problem tractable while preserving the semantic content, a dimensionality reduction of the data is needed. We propose the Random Projection techniques to reduce the dimensionality. Even though this technique is sub-optimal with respect to Singular Value Decomposition its much lower computational cost make it more suitable for this problem and in particular when computational resources are limited such as in mobile terminals. In this paper we present the use of a “conceptual” space, automatically induced from data, to perform automatic image annotation. Images are represented by visual features based on color and texture and arranged as histograms of visual terms and bigrams to partially preserve the spatial information [1]. Using a set of annotated images as training data, the matrix of visual features is built and dimensionality reduction is performed using the Random Projection algorithm. A new unannotated image is then projected into the dimensionally reduced space and the labels of the closest training images are assigned to the unannotated image itself. Experiments on large real collection of images showed that the approach, despite of its low computational cost, is very effective.
2011
Blandford, Ann; Pietro, Giuseppe De; Gallo, Luigi; Gimblett, Andy; Oladimeji, Patrick; Thimbleby, Harold
Engineering Interactive Computer Systems for Medicine and Healthcare (EICS4Med) Proceedings Article
In: EICS '11 Proceedings of the 3rd ACM SIGCHI Symposium on Engineering Interactive Computing Systems, pp. 341–342, ACM, Pisa, Italy, 2011, ISBN: 978-1-4503-0670-6.
Abstract | Links | BibTeX | Tags: Formal methods, Healthcare, Human computer interaction, Medical devices, Mobile computing, Modeling, Natural User Interfaces, Safety
@inproceedings{blandfordEngineeringInteractiveComputer2011,
title = {Engineering Interactive Computer Systems for Medicine and Healthcare (EICS4Med)},
author = { Ann Blandford and Giuseppe De Pietro and Luigi Gallo and Andy Gimblett and Patrick Oladimeji and Harold Thimbleby},
doi = {http://doi.acm.org/10.1145/1996461.1996556},
isbn = {978-1-4503-0670-6},
year = {2011},
date = {2011-06-01},
booktitle = {EICS '11 Proceedings of the 3rd ACM SIGCHI Symposium on Engineering Interactive Computing Systems},
pages = {341--342},
publisher = {ACM},
address = {Pisa, Italy},
abstract = {This workshop brings together and develops the community of researchers and practitioners concerned with the design and evaluation of interactive medical devices (infusion pumps, etc) and systems (electronic patient records, etc), to deliver a roadmap for future research in this area. The workshop involves researchers and practitioners designing and evaluating dependable systems in a variety of contexts, and those developing innovative interactive computer systems for healthcare. These pose particular challenges because of the inherent variability - of patients, system configurations, and so on. Participants will represent a range of perspectives, including safety engineering and innovative design.},
keywords = {Formal methods, Healthcare, Human computer interaction, Medical devices, Mobile computing, Modeling, Natural User Interfaces, Safety},
pubstate = {published},
tppubtype = {inproceedings}
}
This workshop brings together and develops the community of researchers and practitioners concerned with the design and evaluation of interactive medical devices (infusion pumps, etc) and systems (electronic patient records, etc), to deliver a roadmap for future research in this area. The workshop involves researchers and practitioners designing and evaluating dependable systems in a variety of contexts, and those developing innovative interactive computer systems for healthcare. These pose particular challenges because of the inherent variability - of patients, system configurations, and so on. Participants will represent a range of perspectives, including safety engineering and innovative design.
Blandford, Ann; Pietro, Giuseppe De; Gallo, Luigi; Gimblett, Andy; Oladimeji, Patrick; Thimbleby, Harold
Engineering interactive computer systems for medicine and healthcare (EICS4Med) Proceedings Article
In: EICS '11 Proceedings of the 3rd ACM SIGCHI symposium on Engineering interactive computing systems, pp. 341–342, ACM, Pisa, Italy, 2011, ISBN: 978-1-4503-0670-6.
Abstract | Links | BibTeX | Tags: Formal methods, Healthcare, Human computer interaction, Medical devices, Mobile computing, Modeling, Natural User Interfaces, Safety
@inproceedings{blandford_engineering_2011,
title = {Engineering interactive computer systems for medicine and healthcare (EICS4Med)},
author = {Ann Blandford and Giuseppe De Pietro and Luigi Gallo and Andy Gimblett and Patrick Oladimeji and Harold Thimbleby},
doi = {http://doi.acm.org/10.1145/1996461.1996556},
isbn = {978-1-4503-0670-6},
year = {2011},
date = {2011-06-01},
booktitle = {EICS '11 Proceedings of the 3rd ACM SIGCHI symposium on Engineering interactive computing systems},
pages = {341–342},
publisher = {ACM},
address = {Pisa, Italy},
abstract = {This workshop brings together and develops the community of researchers and practitioners concerned with the design and evaluation of interactive medical devices (infusion pumps, etc) and systems (electronic patient records, etc), to deliver a roadmap for future research in this area. The workshop involves researchers and practitioners designing and evaluating dependable systems in a variety of contexts, and those developing innovative interactive computer systems for healthcare. These pose particular challenges because of the inherent variability - of patients, system configurations, and so on. Participants will represent a range of perspectives, including safety engineering and innovative design.},
keywords = {Formal methods, Healthcare, Human computer interaction, Medical devices, Mobile computing, Modeling, Natural User Interfaces, Safety},
pubstate = {published},
tppubtype = {inproceedings}
}
This workshop brings together and develops the community of researchers and practitioners concerned with the design and evaluation of interactive medical devices (infusion pumps, etc) and systems (electronic patient records, etc), to deliver a roadmap for future research in this area. The workshop involves researchers and practitioners designing and evaluating dependable systems in a variety of contexts, and those developing innovative interactive computer systems for healthcare. These pose particular challenges because of the inherent variability - of patients, system configurations, and so on. Participants will represent a range of perspectives, including safety engineering and innovative design.