AHCI RESEARCH GROUP
Publications
Papers published in international journals,
proceedings of conferences, workshops and books.
OUR RESEARCH
Scientific Publications
How to
Here you can find the complete list of our publications.
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.
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.
2023
Dipanda, Albert; Gallo, Luigi; Yetongnon, Kokou (Ed.)
2023 17th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS) Proceedings
IEEE Computer Society, 2023, ISBN: 979-8-3503-7091-1.
Abstract | Links | BibTeX | Tags: Computer graphics, Image processing
@proceedings{dipanda202317thInternational2023,
title = {2023 17th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)},
editor = { Albert Dipanda and Luigi Gallo and Kokou Yetongnon},
url = {https://ieeexplore.ieee.org/servlet/opac?punumber=10472709},
doi = {10.1109/SITIS61268.2023},
isbn = {979-8-3503-7091-1},
year = {2023},
date = {2023-11-10},
urldate = {2024-03-21},
publisher = {IEEE Computer Society},
abstract = {We are pleased to welcome you to SITIS 2023, the seventeenth edition of the IEEE International Conference on Signal-Image Technology & Internet-Based Systems. We thank the authors for their valuable contributions to the conference. SITIS 2023 aims to bring together researchers from the major communities of signal/image processing and information modeling and analysis, and to foster crossdisciplinary collaborations. The conference consists of two tracks: SIVT (Signal & Image and Vision Technology), which focuses on recent developments and evolutions in signal processing, image analysis, vision, coding & authentication, and retrieval techniques; and ISSA (Intelligent Systems Services and Applications), which covers emerging concepts, architectures, protocols, and methodologies for data management on the Web and the Internet of Things technologies that connect unlimited numbers of smart objects. In addition to these tracks, SITIS 2023 also features some workshops that address a wide range of related but more specific topics.},
keywords = {Computer graphics, Image processing},
pubstate = {published},
tppubtype = {proceedings}
}
We are pleased to welcome you to SITIS 2023, the seventeenth edition of the IEEE International Conference on Signal-Image Technology & Internet-Based Systems. We thank the authors for their valuable contributions to the conference. SITIS 2023 aims to bring together researchers from the major communities of signal/image processing and information modeling and analysis, and to foster crossdisciplinary collaborations. The conference consists of two tracks: SIVT (Signal & Image and Vision Technology), which focuses on recent developments and evolutions in signal processing, image analysis, vision, coding & authentication, and retrieval techniques; and ISSA (Intelligent Systems Services and Applications), which covers emerging concepts, architectures, protocols, and methodologies for data management on the Web and the Internet of Things technologies that connect unlimited numbers of smart objects. In addition to these tracks, SITIS 2023 also features some workshops that address a wide range of related but more specific topics.
Dipanda, Albert; Gallo, Luigi; Yetongnon, Kokou (Ed.)
2023 17th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS) Book
IEEE Computer Society, 2023, ISBN: 979-8-3503-7091-1, (tex.referencetype: proceedings).
Abstract | Links | BibTeX | Tags: Computer graphics, Image processing
@book{dipanda_2023_2023,
title = {2023 17th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)},
editor = {Albert Dipanda and Luigi Gallo and Kokou Yetongnon},
url = {https://ieeexplore.ieee.org/servlet/opac?punumber=10472709},
isbn = {979-8-3503-7091-1},
year = {2023},
date = {2023-11-01},
publisher = {IEEE Computer Society},
abstract = {We are pleased to welcome you to SITIS 2023, the seventeenth edition of the IEEE International
Conference on Signal-Image Technology & Internet-Based Systems. We thank the authors for their
valuable contributions to the conference. SITIS 2023 aims to bring together researchers from the major
communities of signal/image processing and information modeling and analysis, and to foster crossdisciplinary
collaborations. The conference consists of two tracks: SIVT (Signal & Image and Vision
Technology), which focuses on recent developments and evolutions in signal processing, image
analysis, vision, coding & authentication, and retrieval techniques; and ISSA (Intelligent Systems
Services and Applications), which covers emerging concepts, architectures, protocols, and
methodologies for data management on the Web and the Internet of Things technologies that connect
unlimited numbers of smart objects. In addition to these tracks, SITIS 2023 also features some
workshops that address a wide range of related but more specific topics.},
note = {tex.referencetype: proceedings},
keywords = {Computer graphics, Image processing},
pubstate = {published},
tppubtype = {book}
}
We are pleased to welcome you to SITIS 2023, the seventeenth edition of the IEEE International
Conference on Signal-Image Technology & Internet-Based Systems. We thank the authors for their
valuable contributions to the conference. SITIS 2023 aims to bring together researchers from the major
communities of signal/image processing and information modeling and analysis, and to foster crossdisciplinary
collaborations. The conference consists of two tracks: SIVT (Signal & Image and Vision
Technology), which focuses on recent developments and evolutions in signal processing, image
analysis, vision, coding & authentication, and retrieval techniques; and ISSA (Intelligent Systems
Services and Applications), which covers emerging concepts, architectures, protocols, and
methodologies for data management on the Web and the Internet of Things technologies that connect
unlimited numbers of smart objects. In addition to these tracks, SITIS 2023 also features some
workshops that address a wide range of related but more specific topics.
Conference on Signal-Image Technology & Internet-Based Systems. We thank the authors for their
valuable contributions to the conference. SITIS 2023 aims to bring together researchers from the major
communities of signal/image processing and information modeling and analysis, and to foster crossdisciplinary
collaborations. The conference consists of two tracks: SIVT (Signal & Image and Vision
Technology), which focuses on recent developments and evolutions in signal processing, image
analysis, vision, coding & authentication, and retrieval techniques; and ISSA (Intelligent Systems
Services and Applications), which covers emerging concepts, architectures, protocols, and
methodologies for data management on the Web and the Internet of Things technologies that connect
unlimited numbers of smart objects. In addition to these tracks, SITIS 2023 also features some
workshops that address a wide range of related but more specific topics.
2013
Franchini, Silvia; Gentile, Antonio; Vassallo, Giorgio; Sorbello, Filippo; Vitabile, Salvatore
A Specialized Architecture for Color Image Edge Detection Based on Clifford Algebra Proceedings Article
In: pp. 128–135, 2013, ISBN: 978-0-7695-4992-7.
Abstract | Links | BibTeX | Tags: Application-specific processors, Clifford algebra, Color image edge detection, Embedded coprocessors, Field Programmable Gate Arrays, FPGA prototyping, Geometric algebra, Image processing, Medical Imaging, Multispectral Magnetic Resonance images
@inproceedings{franchiniSpecializedArchitectureColor2013,
title = {A Specialized Architecture for Color Image Edge Detection Based on Clifford Algebra},
author = { Silvia Franchini and Antonio Gentile and Giorgio Vassallo and Filippo Sorbello and Salvatore Vitabile},
doi = {10.1109/CISIS.2013.29},
isbn = {978-0-7695-4992-7},
year = {2013},
date = {2013-01-01},
pages = {128--135},
abstract = {Edge detection of color images is usually performed by applying the traditional techniques for gray-scale images to the three color channels separately. However, human visual perception does not differentiate colors and processes the image as a whole. Recently, new methods have been proposed that treat RGB color triples as vectors and color images as vector fields. In these approaches, edge detection is obtained extending the classical pattern matching and convolution techniques to vector fields. This paper proposes a hardware implementation of an edge detection method for color images that exploits the definition of geometric product of vectors given in the Clifford algebra framework to extend the convolution operator and the Fourier transform to vector fields. The proposed architecture has been prototyped on the Celoxica RC203E Field Programmable Gate Array (FPGA) board. Experimental tests on the FPGA prototype show that the proposed hardware architecture allows for an average speedup ranging between 6x and 18x for different image sizes against the execution on a conventional general-purpose processor. Clifford algebra based edge detector can be exploited to process not only color images but also multispectral gray-scale images. The proposed hardware architecture has been successfully used for feature extraction of multispectral magnetic resonance (MR) images. textcopyright 2013 IEEE.},
keywords = {Application-specific processors, Clifford algebra, Color image edge detection, Embedded coprocessors, Field Programmable Gate Arrays, FPGA prototyping, Geometric algebra, Image processing, Medical Imaging, Multispectral Magnetic Resonance images},
pubstate = {published},
tppubtype = {inproceedings}
}
Edge detection of color images is usually performed by applying the traditional techniques for gray-scale images to the three color channels separately. However, human visual perception does not differentiate colors and processes the image as a whole. Recently, new methods have been proposed that treat RGB color triples as vectors and color images as vector fields. In these approaches, edge detection is obtained extending the classical pattern matching and convolution techniques to vector fields. This paper proposes a hardware implementation of an edge detection method for color images that exploits the definition of geometric product of vectors given in the Clifford algebra framework to extend the convolution operator and the Fourier transform to vector fields. The proposed architecture has been prototyped on the Celoxica RC203E Field Programmable Gate Array (FPGA) board. Experimental tests on the FPGA prototype show that the proposed hardware architecture allows for an average speedup ranging between 6x and 18x for different image sizes against the execution on a conventional general-purpose processor. Clifford algebra based edge detector can be exploited to process not only color images but also multispectral gray-scale images. The proposed hardware architecture has been successfully used for feature extraction of multispectral magnetic resonance (MR) images. textcopyright 2013 IEEE.
Franchini, Silvia; Gentile, Antonio; Vassallo, Giorgio; Sorbello, Filippo; Vitabile, Salvatore
A specialized architecture for color image edge detection based on Clifford algebra Proceedings Article
In: pp. 128–135, 2013, ISBN: 978-0-7695-4992-7.
Abstract | Links | BibTeX | Tags: Application-specific processors, Clifford algebra, Color image edge detection, Embedded coprocessors, Field Programmable Gate Arrays, FPGA prototyping, Geometric algebra, Image processing, Medical Imaging, Multispectral Magnetic Resonance images
@inproceedings{franchini_specialized_2013,
title = {A specialized architecture for color image edge detection based on Clifford algebra},
author = {Silvia Franchini and Antonio Gentile and Giorgio Vassallo and Filippo Sorbello and Salvatore Vitabile},
doi = {10.1109/CISIS.2013.29},
isbn = {978-0-7695-4992-7},
year = {2013},
date = {2013-01-01},
pages = {128–135},
abstract = {Edge detection of color images is usually performed by applying the traditional techniques for gray-scale images to the three color channels separately. However, human visual perception does not differentiate colors and processes the image as a whole. Recently, new methods have been proposed that treat RGB color triples as vectors and color images as vector fields. In these approaches, edge detection is obtained extending the classical pattern matching and convolution techniques to vector fields. This paper proposes a hardware implementation of an edge detection method for color images that exploits the definition of geometric product of vectors given in the Clifford algebra framework to extend the convolution operator and the Fourier transform to vector fields. The proposed architecture has been prototyped on the Celoxica RC203E Field Programmable Gate Array (FPGA) board. Experimental tests on the FPGA prototype show that the proposed hardware architecture allows for an average speedup ranging between 6x and 18x for different image sizes against the execution on a conventional general-purpose processor. Clifford algebra based edge detector can be exploited to process not only color images but also multispectral gray-scale images. The proposed hardware architecture has been successfully used for feature extraction of multispectral magnetic resonance (MR) images. © 2013 IEEE.},
keywords = {Application-specific processors, Clifford algebra, Color image edge detection, Embedded coprocessors, Field Programmable Gate Arrays, FPGA prototyping, Geometric algebra, Image processing, Medical Imaging, Multispectral Magnetic Resonance images},
pubstate = {published},
tppubtype = {inproceedings}
}
Edge detection of color images is usually performed by applying the traditional techniques for gray-scale images to the three color channels separately. However, human visual perception does not differentiate colors and processes the image as a whole. Recently, new methods have been proposed that treat RGB color triples as vectors and color images as vector fields. In these approaches, edge detection is obtained extending the classical pattern matching and convolution techniques to vector fields. This paper proposes a hardware implementation of an edge detection method for color images that exploits the definition of geometric product of vectors given in the Clifford algebra framework to extend the convolution operator and the Fourier transform to vector fields. The proposed architecture has been prototyped on the Celoxica RC203E Field Programmable Gate Array (FPGA) board. Experimental tests on the FPGA prototype show that the proposed hardware architecture allows for an average speedup ranging between 6x and 18x for different image sizes against the execution on a conventional general-purpose processor. Clifford algebra based edge detector can be exploited to process not only color images but also multispectral gray-scale images. The proposed hardware architecture has been successfully used for feature extraction of multispectral magnetic resonance (MR) images. © 2013 IEEE.
2012
Franchini, Silvia; Gentile, Antonio; Sorbello, Filippo; Vassallo, Giorgio; Vitabile, Salvatore
Clifford Algebra Based Edge Detector for Color Images Proceedings Article
In: pp. 84–91, 2012, ISBN: 978-0-7695-4687-2.
Abstract | Links | BibTeX | Tags: Clifford algebra, Clifford convolution, Clifford Fourier transform, Color image edge detection, Edge detection, Geometric algebra, Image processing, Segmentation
@inproceedings{franchiniCliffordAlgebraBased2012,
title = {Clifford Algebra Based Edge Detector for Color Images},
author = { Silvia Franchini and Antonio Gentile and Filippo Sorbello and Giorgio Vassallo and Salvatore Vitabile},
doi = {10.1109/CISIS.2012.128},
isbn = {978-0-7695-4687-2},
year = {2012},
date = {2012-01-01},
pages = {84--91},
abstract = {Edge detection is one of the most used methods for feature extraction in computer vision applications. Feature extraction is traditionally founded on pattern recognition methods exploiting the basic concepts of convolution and Fourier transform. For color image edge detection the traditional methods used for gray-scale images are usually extended and applied to the three color channels separately. This leads to increased computational requirements and long execution times. In this paper we propose a new, enhanced version of an edge detection algorithm that treats color value triples as vectors and exploits the geometric product of vectors defined in the Clifford algebra framework to extend the traditional concepts of convolution and Fourier transform to vector fields. Experimental results presented in the paper show that the proposed algorithm achieves detection performance comparable to the classical edge detection methods allowing at the same time for a significant reduction (about 33%) of computational times. textcopyright 2012 Crown Copyright.},
keywords = {Clifford algebra, Clifford convolution, Clifford Fourier transform, Color image edge detection, Edge detection, Geometric algebra, Image processing, Segmentation},
pubstate = {published},
tppubtype = {inproceedings}
}
Edge detection is one of the most used methods for feature extraction in computer vision applications. Feature extraction is traditionally founded on pattern recognition methods exploiting the basic concepts of convolution and Fourier transform. For color image edge detection the traditional methods used for gray-scale images are usually extended and applied to the three color channels separately. This leads to increased computational requirements and long execution times. In this paper we propose a new, enhanced version of an edge detection algorithm that treats color value triples as vectors and exploits the geometric product of vectors defined in the Clifford algebra framework to extend the traditional concepts of convolution and Fourier transform to vector fields. Experimental results presented in the paper show that the proposed algorithm achieves detection performance comparable to the classical edge detection methods allowing at the same time for a significant reduction (about 33%) of computational times. textcopyright 2012 Crown Copyright.
Franchini, Silvia; Gentile, Antonio; Sorbello, Filippo; Vassallo, Giorgio; Vitabile, Salvatore
Clifford Algebra based edge detector for color images Proceedings Article
In: pp. 84–91, 2012, ISBN: 978-0-7695-4687-2.
Abstract | Links | BibTeX | Tags: Clifford algebra, Clifford convolution, Clifford Fourier transform, Color image edge detection, Edge detection, Geometric algebra, Image processing, Segmentation
@inproceedings{franchini_clifford_2012,
title = {Clifford Algebra based edge detector for color images},
author = {Silvia Franchini and Antonio Gentile and Filippo Sorbello and Giorgio Vassallo and Salvatore Vitabile},
doi = {10.1109/CISIS.2012.128},
isbn = {978-0-7695-4687-2},
year = {2012},
date = {2012-01-01},
pages = {84–91},
abstract = {Edge detection is one of the most used methods for feature extraction in computer vision applications. Feature extraction is traditionally founded on pattern recognition methods exploiting the basic concepts of convolution and Fourier transform. For color image edge detection the traditional methods used for gray-scale images are usually extended and applied to the three color channels separately. This leads to increased computational requirements and long execution times. In this paper we propose a new, enhanced version of an edge detection algorithm that treats color value triples as vectors and exploits the geometric product of vectors defined in the Clifford algebra framework to extend the traditional concepts of convolution and Fourier transform to vector fields. Experimental results presented in the paper show that the proposed algorithm achieves detection performance comparable to the classical edge detection methods allowing at the same time for a significant reduction (about 33%) of computational times. © 2012 Crown Copyright.},
keywords = {Clifford algebra, Clifford convolution, Clifford Fourier transform, Color image edge detection, Edge detection, Geometric algebra, Image processing, Segmentation},
pubstate = {published},
tppubtype = {inproceedings}
}
Edge detection is one of the most used methods for feature extraction in computer vision applications. Feature extraction is traditionally founded on pattern recognition methods exploiting the basic concepts of convolution and Fourier transform. For color image edge detection the traditional methods used for gray-scale images are usually extended and applied to the three color channels separately. This leads to increased computational requirements and long execution times. In this paper we propose a new, enhanced version of an edge detection algorithm that treats color value triples as vectors and exploits the geometric product of vectors defined in the Clifford algebra framework to extend the traditional concepts of convolution and Fourier transform to vector fields. Experimental results presented in the paper show that the proposed algorithm achieves detection performance comparable to the classical edge detection methods allowing at the same time for a significant reduction (about 33%) of computational times. © 2012 Crown Copyright.