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 expand the Abstract, Links and BibTex record for each paper.
2021
Franchini, Silvia; Vitabile, Salvatore
Geometric Calculus Applications to Medical Imaging: Status and Perspectives Proceedings Article
In: Xambó-Descamps, Sebasti`a (Ed.): Systems, Patterns and Data Engineering with Geometric Calculi, pp. 31–46, Springer International Publishing, Cham, 2021, ISBN: 978-3-030-74486-1.
Abstract | Links | BibTeX | Tags: 3D modeling, Clifford algebra, Deep learning, Geometric algebra, Geometric Calculus, Medical image classification, Medical image registration, Medical image segmentation, Medical Imaging, radiomics
@inproceedings{franchiniGeometricCalculusApplications2021,
title = {Geometric Calculus Applications to Medical Imaging: Status and Perspectives},
author = { Silvia Franchini and Salvatore Vitabile},
editor = { Sebasti{`a} {Xambó-Descamps}},
doi = {10.1007/978-3-030-74486-1_3},
isbn = {978-3-030-74486-1},
year = {2021},
date = {2021-01-01},
booktitle = {Systems, Patterns and Data Engineering with Geometric Calculi},
pages = {31--46},
publisher = {Springer International Publishing},
address = {Cham},
series = {SEMA SIMAI Springer Series},
abstract = {Medical imaging data coming from different acquisition modalities requires automatic tools to extract useful information and support clinicians in the formulation of accurate diagnoses. Geometric Calculus (GC) offers a powerful mathematical and computational model for the development of effective medical imaging algorithms. The practical use of GC-based methods in medical imaging requires fast and efficient implementations to meet real-time processing constraints as well as accuracy and robustness requirements. The purpose of this article is to present the state of the art of the GC-based techniques for medical image analysis and processing. The use of GC-based paradigms in Radiomics and Deep Learning, i.e. a comprehensive quantification of tumor phenotypes by applying a large number of quantitative image features and its classification, is also outlined.},
keywords = {3D modeling, Clifford algebra, Deep learning, Geometric algebra, Geometric Calculus, Medical image classification, Medical image registration, Medical image segmentation, Medical Imaging, radiomics},
pubstate = {published},
tppubtype = {inproceedings}
}
Medical imaging data coming from different acquisition modalities requires automatic tools to extract useful information and support clinicians in the formulation of accurate diagnoses. Geometric Calculus (GC) offers a powerful mathematical and computational model for the development of effective medical imaging algorithms. The practical use of GC-based methods in medical imaging requires fast and efficient implementations to meet real-time processing constraints as well as accuracy and robustness requirements. The purpose of this article is to present the state of the art of the GC-based techniques for medical image analysis and processing. The use of GC-based paradigms in Radiomics and Deep Learning, i.e. a comprehensive quantification of tumor phenotypes by applying a large number of quantitative image features and its classification, is also outlined.
Franchini, Silvia; Vitabile, Salvatore
Geometric Calculus Applications to Medical Imaging: Status and Perspectives Proceedings Article
In: Xambó-Descamps, Sebastià (Ed.): Systems, Patterns and Data Engineering with Geometric Calculi, pp. 31–46, Springer International Publishing, Cham, 2021, ISBN: 978-3-030-74486-1.
Abstract | Links | BibTeX | Tags: 3D modeling, Clifford algebra, Deep learning, Geometric algebra, Geometric Calculus, Medical image classification, Medical image registration, Medical image segmentation, Medical Imaging, radiomics
@inproceedings{franchini_geometric_2021,
title = {Geometric Calculus Applications to Medical Imaging: Status and Perspectives},
author = {Silvia Franchini and Salvatore Vitabile},
editor = {Sebastià Xambó-Descamps},
doi = {10.1007/978-3-030-74486-1_3},
isbn = {978-3-030-74486-1},
year = {2021},
date = {2021-01-01},
booktitle = {Systems, Patterns and Data Engineering with Geometric Calculi},
pages = {31–46},
publisher = {Springer International Publishing},
address = {Cham},
series = {SEMA SIMAI Springer Series},
abstract = {Medical imaging data coming from different acquisition modalities requires automatic tools to extract useful information and support clinicians in the formulation of accurate diagnoses. Geometric Calculus (GC) offers a powerful mathematical and computational model for the development of effective medical imaging algorithms. The practical use of GC-based methods in medical imaging requires fast and efficient implementations to meet real-time processing constraints as well as accuracy and robustness requirements. The purpose of this article is to present the state of the art of the GC-based techniques for medical image analysis and processing. The use of GC-based paradigms in Radiomics and Deep Learning, i.e. a comprehensive quantification of tumor phenotypes by applying a large number of quantitative image features and its classification, is also outlined.},
keywords = {3D modeling, Clifford algebra, Deep learning, Geometric algebra, Geometric Calculus, Medical image classification, Medical image registration, Medical image segmentation, Medical Imaging, radiomics},
pubstate = {published},
tppubtype = {inproceedings}
}
Medical imaging data coming from different acquisition modalities requires automatic tools to extract useful information and support clinicians in the formulation of accurate diagnoses. Geometric Calculus (GC) offers a powerful mathematical and computational model for the development of effective medical imaging algorithms. The practical use of GC-based methods in medical imaging requires fast and efficient implementations to meet real-time processing constraints as well as accuracy and robustness requirements. The purpose of this article is to present the state of the art of the GC-based techniques for medical image analysis and processing. The use of GC-based paradigms in Radiomics and Deep Learning, i.e. a comprehensive quantification of tumor phenotypes by applying a large number of quantitative image features and its classification, is also outlined.
2017
Augello, Agnese; Cipolla, Emanuele; Infantino, Ignazio; Manfr`e, Adriano; Pilato, Giovanni; Vella, Filippo
Creative Robot Dance with Variational Encoder Proceedings Article
In: A., Jordanous A. Pease A. Goel (Ed.): Proceedings of the 8th International Conference on Computational Creativity, ICCC 2017, Georgia Institute of Technology, 2017, ISBN: 978-0-692-89564-1.
Abstract | BibTeX | Tags: Anthropomorphic Robots, Computational Creativity, Creative Agents, Deep learning, Robotics
@inproceedings{augelloCreativeRobotDance2017,
title = {Creative Robot Dance with Variational Encoder},
author = { Agnese Augello and Emanuele Cipolla and Ignazio Infantino and Adriano Manfr{`e} and Giovanni Pilato and Filippo Vella},
editor = { Jordanous A. Pease A. Goel A.},
isbn = {978-0-692-89564-1},
year = {2017},
date = {2017-01-01},
booktitle = {Proceedings of the 8th International Conference on Computational Creativity, ICCC 2017},
publisher = {Georgia Institute of Technology},
abstract = {What we appreciate in dance is the ability of people to spontaneously improvise new movements and choreographies, surrendering to the music rhythm, being inspired by the current perceptions and sensations and by previous experiences, deeply stored in their memory. Like other human abilities, this, of course, is challenging to reproduce in an artificial entity such as a robot. Recent generations of anthropomorphic robots, the so-called humanoids, however, exhibit more and more sophisticated skills and raised the interest in robotic communities to design and experiment systems devoted to automatic dance generation. In this work, we highlight the importance to model a computational creativity behavior in dancing robots to avoid a mere execution of preprogrammed dances. In particular, we exploit a deep learning approach that allows a robot to generate in real time new dancing movements according to to the listened music. textcopyright ICCC 2017.},
keywords = {Anthropomorphic Robots, Computational Creativity, Creative Agents, Deep learning, Robotics},
pubstate = {published},
tppubtype = {inproceedings}
}
What we appreciate in dance is the ability of people to spontaneously improvise new movements and choreographies, surrendering to the music rhythm, being inspired by the current perceptions and sensations and by previous experiences, deeply stored in their memory. Like other human abilities, this, of course, is challenging to reproduce in an artificial entity such as a robot. Recent generations of anthropomorphic robots, the so-called humanoids, however, exhibit more and more sophisticated skills and raised the interest in robotic communities to design and experiment systems devoted to automatic dance generation. In this work, we highlight the importance to model a computational creativity behavior in dancing robots to avoid a mere execution of preprogrammed dances. In particular, we exploit a deep learning approach that allows a robot to generate in real time new dancing movements according to to the listened music. textcopyright ICCC 2017.
Vella, Filippo; Augello, Agnese; Maniscalco, Umberto; Bentivenga, Vincenzo; Gaglio, Salvatore
Classification of Indoor Actions through Deep Neural Networks Proceedings Article
In: G., Dipanda A. Chbeir R. Gallo L. Yetongnon K. De Pietro (Ed.): Proceedings - 12th International Conference on Signal Image Technology and Internet-Based Systems, SITIS 2016, pp. 82–87, Institute of Electrical and Electronics Engineers Inc., 2017, ISBN: 978-1-5090-5698-9.
Abstract | Links | BibTeX | Tags: Action Recognition, Convolutional Neural Networks, Deep learning, RGB-D
@inproceedings{vellaClassificationIndoorActions2017,
title = {Classification of Indoor Actions through Deep Neural Networks},
author = { Filippo Vella and Agnese Augello and Umberto Maniscalco and Vincenzo Bentivenga and Salvatore Gaglio},
editor = { Dipanda A. Chbeir R. Gallo L. Yetongnon K. De Pietro G.},
doi = {10.1109/SITIS.2016.22},
isbn = {978-1-5090-5698-9},
year = {2017},
date = {2017-01-01},
booktitle = {Proceedings - 12th International Conference on Signal Image Technology and Internet-Based Systems, SITIS 2016},
pages = {82--87},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {The raising number of elderly people urges the research of systems able to monitor and support people inside their domestic environment. An automatic system capturing data about the position of a person in the house, through accelerometers and RGBd cameras can monitor the person activities and produce outputs associating the movements to a given tasks or predicting the set of activities that will be executes. We considered, for the task the classification of the activities a Deep Convolutional Neural Network. We compared two different deep network and analyzed their outputs. textcopyright 2016 IEEE.},
keywords = {Action Recognition, Convolutional Neural Networks, Deep learning, RGB-D},
pubstate = {published},
tppubtype = {inproceedings}
}
The raising number of elderly people urges the research of systems able to monitor and support people inside their domestic environment. An automatic system capturing data about the position of a person in the house, through accelerometers and RGBd cameras can monitor the person activities and produce outputs associating the movements to a given tasks or predicting the set of activities that will be executes. We considered, for the task the classification of the activities a Deep Convolutional Neural Network. We compared two different deep network and analyzed their outputs. textcopyright 2016 IEEE.
Vella, Filippo; Augello, Agnese; Maniscalco, Umberto; Bentivenga, Vincenzo; Gaglio, Salvatore
Classification of Indoor Actions through Deep Neural Networks Proceedings Article
In: G., Chbeir R. Dipanda A. De Pietro (Ed.): Proceedings - 12th International Conference on Signal Image Technology and Internet-Based Systems, SITIS 2016, pp. 82–87, Institute of Electrical and Electronics Engineers Inc., 2017, ISBN: 978-1-5090-5698-9.
Abstract | Links | BibTeX | Tags: Action Recognition, Convolutional Neural Networks, Deep learning, RGB-D
@inproceedings{vella_classification_2017,
title = {Classification of Indoor Actions through Deep Neural Networks},
author = {Filippo Vella and Agnese Augello and Umberto Maniscalco and Vincenzo Bentivenga and Salvatore Gaglio},
editor = {Chbeir R. Dipanda A. De Pietro G.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85019213644&doi=10.1109%2fSITIS.2016.22&partnerID=40&md5=329d35941a322add5df73469e33e0f07},
doi = {10.1109/SITIS.2016.22},
isbn = {978-1-5090-5698-9},
year = {2017},
date = {2017-01-01},
booktitle = {Proceedings - 12th International Conference on Signal Image Technology and Internet-Based Systems, SITIS 2016},
pages = {82–87},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {The raising number of elderly people urges the research of systems able to monitor and support people inside their domestic environment. An automatic system capturing data about the position of a person in the house, through accelerometers and RGBd cameras can monitor the person activities and produce outputs associating the movements to a given tasks or predicting the set of activities that will be executes. We considered, for the task the classification of the activities a Deep Convolutional Neural Network. We compared two different deep network and analyzed their outputs. © 2016 IEEE.},
keywords = {Action Recognition, Convolutional Neural Networks, Deep learning, RGB-D},
pubstate = {published},
tppubtype = {inproceedings}
}
The raising number of elderly people urges the research of systems able to monitor and support people inside their domestic environment. An automatic system capturing data about the position of a person in the house, through accelerometers and RGBd cameras can monitor the person activities and produce outputs associating the movements to a given tasks or predicting the set of activities that will be executes. We considered, for the task the classification of the activities a Deep Convolutional Neural Network. We compared two different deep network and analyzed their outputs. © 2016 IEEE.
Augello, Agnese; Cipolla, Emanuele; Infantino, Ignazio; Manfrè, Adriano; Pilato, Giovanni; Vella, Filippo
Creative robot dance with variational encoder Proceedings Article
In: A., Pease A. Jordanous A. Goel (Ed.): Proceedings of the 8th International Conference on Computational Creativity, ICCC 2017, Georgia Institute of Technology, 2017, ISBN: 978-0-692-89564-1.
Abstract | Links | BibTeX | Tags: Anthropomorphic Robots, Computational Creativity, Creative Agents, Deep learning, Robotics
@inproceedings{augello_creative_2017,
title = {Creative robot dance with variational encoder},
author = {Agnese Augello and Emanuele Cipolla and Ignazio Infantino and Adriano Manfrè and Giovanni Pilato and Filippo Vella},
editor = {Pease A. Jordanous A. Goel A.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85109115481&partnerID=40&md5=12395ca05fdbd55430c1b8170a516c15},
isbn = {978-0-692-89564-1},
year = {2017},
date = {2017-01-01},
booktitle = {Proceedings of the 8th International Conference on Computational Creativity, ICCC 2017},
publisher = {Georgia Institute of Technology},
abstract = {What we appreciate in dance is the ability of people to spontaneously improvise new movements and choreographies, surrendering to the music rhythm, being inspired by the current perceptions and sensations and by previous experiences, deeply stored in their memory. Like other human abilities, this, of course, is challenging to reproduce in an artificial entity such as a robot. Recent generations of anthropomorphic robots, the so-called humanoids, however, exhibit more and more sophisticated skills and raised the interest in robotic communities to design and experiment systems devoted to automatic dance generation. In this work, we highlight the importance to model a computational creativity behavior in dancing robots to avoid a mere execution of preprogrammed dances. In particular, we exploit a deep learning approach that allows a robot to generate in real time new dancing movements according to to the listened music. © ICCC 2017.},
keywords = {Anthropomorphic Robots, Computational Creativity, Creative Agents, Deep learning, Robotics},
pubstate = {published},
tppubtype = {inproceedings}
}
What we appreciate in dance is the ability of people to spontaneously improvise new movements and choreographies, surrendering to the music rhythm, being inspired by the current perceptions and sensations and by previous experiences, deeply stored in their memory. Like other human abilities, this, of course, is challenging to reproduce in an artificial entity such as a robot. Recent generations of anthropomorphic robots, the so-called humanoids, however, exhibit more and more sophisticated skills and raised the interest in robotic communities to design and experiment systems devoted to automatic dance generation. In this work, we highlight the importance to model a computational creativity behavior in dancing robots to avoid a mere execution of preprogrammed dances. In particular, we exploit a deep learning approach that allows a robot to generate in real time new dancing movements according to to the listened music. © ICCC 2017.