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.
2015
Essmaeel, Kyis; Gallo, Luigi; Damiani, Ernesto; Pietro, Giuseppe De; Dipanda, Albert
Comparative Evaluation of Methods for Filtering Kinect Depth Data Journal Article
In: Multimedia Tools and Applications, vol. 74, no. 17, pp. 7331–7354, 2015, ISSN: 1380-7501.
Abstract | Links | BibTeX | Tags: Comparative evaluation, Depth data, Depth instability, Filtering, Kinect, Median filter, Temporal denoising
@article{essmaeelComparativeEvaluationMethods2015,
title = {Comparative Evaluation of Methods for Filtering Kinect Depth Data},
author = { Kyis Essmaeel and Luigi Gallo and Ernesto Damiani and Giuseppe De Pietro and Albert Dipanda},
doi = {10.1007/s11042-014-1982-6},
issn = {1380-7501},
year = {2015},
date = {2015-09-01},
journal = {Multimedia Tools and Applications},
volume = {74},
number = {17},
pages = {7331--7354},
abstract = {The release of the Kinect has fostered the design of novel methods and techniques in several application domains. It has been tested in different contexts, which span from home entertainment to surgical environments. Nonetheless, to promote its adoption to solve real-world problems, the Kinect should be evaluated in terms of precision and accuracy. Up to now, some filtering approaches have been proposed to enhance the precision and accuracy of the Kinect sensor, and preliminary studies have shown promising results. In this work, we discuss the results of a study in which we have compared the most commonly used filtering approaches for Kinect depth data, in both static and dynamic contexts, by using novel metrics. The experimental results show that each approach can be profitably used to enhance the precision and/or accuracy of Kinect depth data in a specific context, whereas the temporal filtering approach is able to reduce noise in different experimental conditions.},
keywords = {Comparative evaluation, Depth data, Depth instability, Filtering, Kinect, Median filter, Temporal denoising},
pubstate = {published},
tppubtype = {article}
}
The release of the Kinect has fostered the design of novel methods and techniques in several application domains. It has been tested in different contexts, which span from home entertainment to surgical environments. Nonetheless, to promote its adoption to solve real-world problems, the Kinect should be evaluated in terms of precision and accuracy. Up to now, some filtering approaches have been proposed to enhance the precision and accuracy of the Kinect sensor, and preliminary studies have shown promising results. In this work, we discuss the results of a study in which we have compared the most commonly used filtering approaches for Kinect depth data, in both static and dynamic contexts, by using novel metrics. The experimental results show that each approach can be profitably used to enhance the precision and/or accuracy of Kinect depth data in a specific context, whereas the temporal filtering approach is able to reduce noise in different experimental conditions.
Essmaeel, Kyis; Gallo, Luigi; Damiani, Ernesto; Pietro, Giuseppe De; Dipanda, Albert
Comparative evaluation of methods for filtering Kinect depth data Journal Article
In: Multimedia Tools and Applications, vol. 74, no. 17, pp. 7331–7354, 2015, ISSN: 1380-7501.
Abstract | Links | BibTeX | Tags: Comparative evaluation, Depth data, Depth instability, Filtering, Kinect, Median filter, Temporal denoising
@article{essmaeel_comparative_2015,
title = {Comparative evaluation of methods for filtering Kinect depth data},
author = {Kyis Essmaeel and Luigi Gallo and Ernesto Damiani and Giuseppe De Pietro and Albert Dipanda},
doi = {10.1007/s11042-014-1982-6},
issn = {1380-7501},
year = {2015},
date = {2015-09-01},
journal = {Multimedia Tools and Applications},
volume = {74},
number = {17},
pages = {7331–7354},
abstract = {The release of the Kinect has fostered the design of novel methods and techniques in several application domains. It has been tested in different contexts, which span from home entertainment to surgical environments. Nonetheless, to promote its adoption to solve real-world problems, the Kinect should be evaluated in terms of precision and accuracy. Up to now, some filtering approaches have been proposed to enhance the precision and accuracy of the Kinect sensor, and preliminary studies have shown promising results. In this work, we discuss the results of a study in which we have compared the most commonly used filtering approaches for Kinect depth data, in both static and dynamic contexts, by using novel metrics. The experimental results show that each approach can be profitably used to enhance the precision and/or accuracy of Kinect depth data in a specific context, whereas the temporal filtering approach is able to reduce noise in different experimental conditions.},
keywords = {Comparative evaluation, Depth data, Depth instability, Filtering, Kinect, Median filter, Temporal denoising},
pubstate = {published},
tppubtype = {article}
}
The release of the Kinect has fostered the design of novel methods and techniques in several application domains. It has been tested in different contexts, which span from home entertainment to surgical environments. Nonetheless, to promote its adoption to solve real-world problems, the Kinect should be evaluated in terms of precision and accuracy. Up to now, some filtering approaches have been proposed to enhance the precision and accuracy of the Kinect sensor, and preliminary studies have shown promising results. In this work, we discuss the results of a study in which we have compared the most commonly used filtering approaches for Kinect depth data, in both static and dynamic contexts, by using novel metrics. The experimental results show that each approach can be profitably used to enhance the precision and/or accuracy of Kinect depth data in a specific context, whereas the temporal filtering approach is able to reduce noise in different experimental conditions.
2012
Essmaeel, Kyis; Gallo, Luigi; Damiani, Ernesto; Pietro, Giuseppe De; Dipanda, Albert
Temporal Denoising of Kinect Depth Data Proceedings Article
In: SITIS '12: Eighth International Conference on Signal Image Technology and Internet Based Systems, pp. 47–52, IEEE, Sorrento, Italy, 2012, ISBN: 978-1-4673-5152-2.
Abstract | Links | BibTeX | Tags: Depth instability, Kinect, Smoothing, Temporal denoising
@inproceedings{essmaeelTemporalDenoisingKinect2012,
title = {Temporal Denoising of Kinect Depth Data},
author = { Kyis Essmaeel and Luigi Gallo and Ernesto Damiani and Giuseppe De Pietro and Albert Dipanda},
doi = {10.1109/SITIS.2012.18},
isbn = {978-1-4673-5152-2},
year = {2012},
date = {2012-01-01},
booktitle = {SITIS '12: Eighth International Conference on Signal Image Technology and Internet Based Systems},
pages = {47--52},
publisher = {IEEE},
address = {Sorrento, Italy},
abstract = {The release of the Microsoft Kinect has attracted the attention of researchers in a variety of computer science domains. Even though this device is still relatively new, its recent applications have shown some promising results in terms of replacing current conventional methods like the stereo-camera for robotics navigation, multi-camera system for motion detection and laser scanner for 3D reconstruction. While most work around the Kinect is on how to take full advantage of its capabilities, so far only a few studies have been carried out on the limitations of this device and fewer that provide solutions to enhance the precision of its measurements. In this paper, we review and analyse current work in this area, and present and evaluate a temporal denoising algorithm to reduce the instability of the depth measurements provided by the Kinect over different distances.},
keywords = {Depth instability, Kinect, Smoothing, Temporal denoising},
pubstate = {published},
tppubtype = {inproceedings}
}
The release of the Microsoft Kinect has attracted the attention of researchers in a variety of computer science domains. Even though this device is still relatively new, its recent applications have shown some promising results in terms of replacing current conventional methods like the stereo-camera for robotics navigation, multi-camera system for motion detection and laser scanner for 3D reconstruction. While most work around the Kinect is on how to take full advantage of its capabilities, so far only a few studies have been carried out on the limitations of this device and fewer that provide solutions to enhance the precision of its measurements. In this paper, we review and analyse current work in this area, and present and evaluate a temporal denoising algorithm to reduce the instability of the depth measurements provided by the Kinect over different distances.
Essmaeel, Kyis; Gallo, Luigi; Damiani, Ernesto; Pietro, Giuseppe De; Dipanda, Albert
Temporal denoising of Kinect depth data Proceedings Article
In: SITIS '12: Eighth International Conference on Signal Image Technology and Internet Based Systems, pp. 47–52, IEEE, Sorrento, Italy, 2012, ISBN: 978-1-4673-5152-2.
Abstract | Links | BibTeX | Tags: Depth instability, Kinect, Smoothing, Temporal denoising
@inproceedings{essmaeel_temporal_2012,
title = {Temporal denoising of Kinect depth data},
author = {Kyis Essmaeel and Luigi Gallo and Ernesto Damiani and Giuseppe De Pietro and Albert Dipanda},
doi = {10.1109/SITIS.2012.18},
isbn = {978-1-4673-5152-2},
year = {2012},
date = {2012-01-01},
booktitle = {SITIS '12: Eighth International Conference on Signal Image Technology and Internet Based Systems},
pages = {47–52},
publisher = {IEEE},
address = {Sorrento, Italy},
abstract = {The release of the Microsoft Kinect has attracted the attention of researchers in a variety of computer science domains. Even though this device is still relatively new, its recent applications have shown some promising results in terms of replacing current conventional methods like the stereo-camera for robotics navigation, multi-camera system for motion detection and laser scanner for 3D reconstruction. While most work around the Kinect is on how to take full advantage of its capabilities, so far only a few studies have been carried out on the limitations of this device and fewer that provide solutions to enhance the precision of its measurements. In this paper, we review and analyse current work in this area, and present and evaluate a temporal denoising algorithm to reduce the instability of the depth measurements provided by the Kinect over different distances.},
keywords = {Depth instability, Kinect, Smoothing, Temporal denoising},
pubstate = {published},
tppubtype = {inproceedings}
}
The release of the Microsoft Kinect has attracted the attention of researchers in a variety of computer science domains. Even though this device is still relatively new, its recent applications have shown some promising results in terms of replacing current conventional methods like the stereo-camera for robotics navigation, multi-camera system for motion detection and laser scanner for 3D reconstruction. While most work around the Kinect is on how to take full advantage of its capabilities, so far only a few studies have been carried out on the limitations of this device and fewer that provide solutions to enhance the precision of its measurements. In this paper, we review and analyse current work in this area, and present and evaluate a temporal denoising algorithm to reduce the instability of the depth measurements provided by the Kinect over different distances.