de Recherche et d’Innovation
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Allili, M. S.; Ziou, D.; Bouguila, N.; Boutemedjet, S.
Image and video segmentation by combining unsupervised generalized Gaussian mixture modeling and feature selection Article de journal
Dans: IEEE Transactions on Circuits and Systems for Video Technology, vol. 20, no 10, p. 1373–1377, 2010, ISSN: 10518215.
Résumé | Liens | BibTeX | Étiquettes: Clustering model, Feature extraction, Feature selection, Gaussian distribution, Generalized Gaussian, Heavy-tailed, High dimensional spaces, Image and video segmentation, Image segmentation, image/video segmentation, Minimum message lengths, Real-world image, Video cameras
@article{allili_image_2010,
title = {Image and video segmentation by combining unsupervised generalized Gaussian mixture modeling and feature selection},
author = {M. S. Allili and D. Ziou and N. Bouguila and S. Boutemedjet},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-77957964550&doi=10.1109%2fTCSVT.2010.2077483&partnerID=40&md5=d888c7fe52eff37a5744bccd6a4d3d9e},
doi = {10.1109/TCSVT.2010.2077483},
issn = {10518215},
year = {2010},
date = {2010-01-01},
journal = {IEEE Transactions on Circuits and Systems for Video Technology},
volume = {20},
number = {10},
pages = {1373–1377},
abstract = {In this letter, we propose a clustering model that efficiently mitigates image and video under/over-segmentation by combining generalized Gaussian mixture modeling and feature selection. The model has flexibility to accurately represent heavy-tailed image/video histograms, while automatically discarding uninformative features, leading to better discrimination and localization of regions in high-dimensional spaces. Experimental results on a database of real-world images and videos showed us the effectiveness of the proposed approach. © 2010 IEEE.},
keywords = {Clustering model, Feature extraction, Feature selection, Gaussian distribution, Generalized Gaussian, Heavy-tailed, High dimensional spaces, Image and video segmentation, Image segmentation, image/video segmentation, Minimum message lengths, Real-world image, Video cameras},
pubstate = {published},
tppubtype = {article}
}
Allili, M. S.; Ziou, D.; Bouguila, N.; Boutemedjet, S.
Unsupervised feature selection and learning for image segmentation Article d'actes
Dans: CRV 2010 - 7th Canadian Conference on Computer and Robot Vision, p. 285–292, Ottawa, ON, 2010, ISBN: 978-0-7695-4040-5.
Résumé | Liens | BibTeX | Étiquettes: Clustering algorithms, Computer vision, Evolutionary algorithms, Feature extraction, Feature selection, Gaussian distribution, Generalized Gaussian, Generalized Gaussian Distributions, Heavy-tailed, High dimensional spaces, Image distributions, Image segmentation, Large database, Over-estimation, Real-world image, Unsupervised feature selection
@inproceedings{allili_unsupervised_2010,
title = {Unsupervised feature selection and learning for image segmentation},
author = {M. S. Allili and D. Ziou and N. Bouguila and S. Boutemedjet},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-77954407977&doi=10.1109%2fCRV.2010.44&partnerID=40&md5=a7d8e3147216429f18ef7af3167acb42},
doi = {10.1109/CRV.2010.44},
isbn = {978-0-7695-4040-5},
year = {2010},
date = {2010-01-01},
booktitle = {CRV 2010 - 7th Canadian Conference on Computer and Robot Vision},
pages = {285–292},
address = {Ottawa, ON},
abstract = {In this paper we investigate the integration of feature selection in segmentation through an unsupervised learning approach. We propose a clustering algorithm that efficiently mitigates image under/over-segmentation, by combining generalized Gaussian mixture modeling and feature selection. The algorithm is based on generalized Gaussian mixture modeling which is less prone to region number over-estimation in case of noisy and heavy-tailed image distributions. On the other hand, our feature selection mechanism allows to automatically discard uninformative features, which leads to better discrimination and localization of regions in high-dimensional spaces. Experimental results on a large database of real-world images showed us the effectiveness of the proposed approach. © 2010 IEEE.},
keywords = {Clustering algorithms, Computer vision, Evolutionary algorithms, Feature extraction, Feature selection, Gaussian distribution, Generalized Gaussian, Generalized Gaussian Distributions, Heavy-tailed, High dimensional spaces, Image distributions, Image segmentation, Large database, Over-estimation, Real-world image, Unsupervised feature selection},
pubstate = {published},
tppubtype = {inproceedings}
}
Floyd, M. W.; Davoust, A.; Esfandiari, B.
Considerations for real-time spatially-aware case-based reasoning: A case study in robotic soccer imitation Article de journal
Dans: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5239 LNAI, p. 195–209, 2008, ISSN: 03029743, (ISBN: 3540855017; 9783540855019 Place: Trier).
Résumé | Liens | BibTeX | Étiquettes: Aware systems, Case based reasoning, Case bases, Case studies, Case-base reasoning, European, Feature selection, Mobile robotic, Prototyping, Real-time constraints, RoboCup soccer, Robotic soccer, Robotics, Spatial environments, Time frames
@article{floyd_considerations_2008,
title = {Considerations for real-time spatially-aware case-based reasoning: A case study in robotic soccer imitation},
author = {M. W. Floyd and A. Davoust and B. Esfandiari},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-52449121574&doi=10.1007%2f978-3-540-85502-6_13&partnerID=40&md5=cba3038fb365eb8aa7088030a41dde83},
doi = {10.1007/978-3-540-85502-6_13},
issn = {03029743},
year = {2008},
date = {2008-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {5239 LNAI},
pages = {195–209},
abstract = {Case-base reasoning in a real-time context requires the system to output the solution to a given problem in a predictable and usually very fast time frame. As the number of cases that can be processed is limited by the real-time constraint, we explore ways of selecting the most important cases and ways of speeding up case comparisons by optimizing the representation of each case. We focus on spatially-aware systems such as mobile robotic applications and the particular challenges in representing the systems' spatial environment. We select and combine techniques for feature selection, clustering and prototyping that are applicable in this particular context and report results from a case study with a simulated RoboCup soccer-playing agent. Our results demonstrate that preprocessing such case bases can significantly improve the imitative ability of an agent. © Springer-Verlag Berlin Heidelberg 2008.},
note = {ISBN: 3540855017; 9783540855019
Place: Trier},
keywords = {Aware systems, Case based reasoning, Case bases, Case studies, Case-base reasoning, European, Feature selection, Mobile robotic, Prototyping, Real-time constraints, RoboCup soccer, Robotic soccer, Robotics, Spatial environments, Time frames},
pubstate = {published},
tppubtype = {article}
}