Slide
Centre Interdisciplinaire
de Recherche et d’Innovation
en Cybersécurité et Société
de Recherche et d’Innovation
en Cybersécurité et Société
1.
Allili, M. S.
Effective object tracking by matching object and background models using active contours Article d'actes
Dans: Proceedings - International Conference on Image Processing, ICIP, p. 873–876, IEEE Computer Society, Cairo, 2009, ISBN: 15224880 (ISSN); 978-142445654-3 (ISBN), (Journal Abbreviation: Proc. Int. Conf. Image Process. ICIP).
Résumé | Liens | BibTeX | Étiquettes: Active contours, Algorithms, Background model, EM algorithm, EM algorithms, Finite mixture models, Image matching, Image processing, Imaging systems, Mathematical models, Object contour, Object Tracking
@inproceedings{allili_effective_2009,
title = {Effective object tracking by matching object and background models using active contours},
author = {M. S. Allili},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-77951940408&doi=10.1109%2fICIP.2009.5414279&partnerID=40&md5=6838bb85dbef6c9548684a506df3d2b2},
doi = {10.1109/ICIP.2009.5414279},
isbn = {15224880 (ISSN); 978-142445654-3 (ISBN)},
year = {2009},
date = {2009-01-01},
booktitle = {Proceedings - International Conference on Image Processing, ICIP},
pages = {873–876},
publisher = {IEEE Computer Society},
address = {Cairo},
abstract = {In this paper, we propose an effective approach for tracking distribution of objects. The approach uses a competition between a tracked objet and background distributions using active contours. Only the segmentation of the object in the first frame is required for initialization. We evolve the object contour by assigning pixels in a fashion that maximizes the likelihood of the object versus the background. This maximization is implemented using an EM-like algorithm, which evolves the object contour exactly to its boundaries, and adapts the parameters of the object and background distributions. ©2009 IEEE.},
note = {Journal Abbreviation: Proc. Int. Conf. Image Process. ICIP},
keywords = {Active contours, Algorithms, Background model, EM algorithm, EM algorithms, Finite mixture models, Image matching, Image processing, Imaging systems, Mathematical models, Object contour, Object Tracking},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper, we propose an effective approach for tracking distribution of objects. The approach uses a competition between a tracked objet and background distributions using active contours. Only the segmentation of the object in the first frame is required for initialization. We evolve the object contour by assigning pixels in a fashion that maximizes the likelihood of the object versus the background. This maximization is implemented using an EM-like algorithm, which evolves the object contour exactly to its boundaries, and adapts the parameters of the object and background distributions. ©2009 IEEE.
2.
Allili, M. S.; Bouguila, N.; Ziou, D.
Finite general Gaussian mixture modeling and application to image and video foreground segmentation Article de journal
Dans: Journal of Electronic Imaging, vol. 17, no 1, 2008, ISSN: 10179909.
Résumé | Liens | BibTeX | Étiquettes: Finite mixture models, Foreground segmentation, Gaussian distribution, Gaussian mixture modeling, Gaussian mixtures, Gaussians, General Gaussian distribution, Image segmentation, Information theory, Information-theoretic approach, Maximum likelihood estimation, Mixture model, Mixtures, Noisy data, Overfitting
@article{allili_finite_2008,
title = {Finite general Gaussian mixture modeling and application to image and video foreground segmentation},
author = {M. S. Allili and N. Bouguila and D. Ziou},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-78149489170&doi=10.1117%2f1.2898125&partnerID=40&md5=8a9b3060dda2366f17b22a06606a9f09},
doi = {10.1117/1.2898125},
issn = {10179909},
year = {2008},
date = {2008-01-01},
journal = {Journal of Electronic Imaging},
volume = {17},
number = {1},
abstract = {We propose a new finite mixture model based on the formalism of general Gaussian distribution (GGD). Because it has the flexibility to adapt to the shape of the data better than the Gaussian, the GGD is less prone to overfitting the number of mixture classes when dealing with noisy data. In the first part of this work, we propose a derivation of the maximum likelihood estimation for the parameters of the new mixture model, and elaborate an information-theoretic approach for the selection of the number of classes. In the second part, we validate the proposed model by comparing it to the Gaussian mixture in applications related to image and video foreground segmentation © 2008 SPIE and IS&T.},
keywords = {Finite mixture models, Foreground segmentation, Gaussian distribution, Gaussian mixture modeling, Gaussian mixtures, Gaussians, General Gaussian distribution, Image segmentation, Information theory, Information-theoretic approach, Maximum likelihood estimation, Mixture model, Mixtures, Noisy data, Overfitting},
pubstate = {published},
tppubtype = {article}
}
We propose a new finite mixture model based on the formalism of general Gaussian distribution (GGD). Because it has the flexibility to adapt to the shape of the data better than the Gaussian, the GGD is less prone to overfitting the number of mixture classes when dealing with noisy data. In the first part of this work, we propose a derivation of the maximum likelihood estimation for the parameters of the new mixture model, and elaborate an information-theoretic approach for the selection of the number of classes. In the second part, we validate the proposed model by comparing it to the Gaussian mixture in applications related to image and video foreground segmentation © 2008 SPIE and IS&T.
3.
Allili, M. S.; Bouguila, N.; Ziou, D.
Finite generalized Gaussian mixture modeling and applications to image and video foreground segmentation Article d'actes
Dans: Proceedings - Fourth Canadian Conference on Computer and Robot Vision, CRV 2007, p. 183–190, Montreal, QC, 2007, ISBN: 0-7695-2786-8 978-0-7695-2786-4.
Résumé | Liens | BibTeX | Étiquettes: Data structures, Finite mixture models, Foreground segmentation, Image segmentation, Information theory, Mathematical models, Maximum likelihood estimation, Mixture of General Gaussions (MoGG)
@inproceedings{allili_finite_2007,
title = {Finite generalized Gaussian mixture modeling and applications to image and video foreground segmentation},
author = {M. S. Allili and N. Bouguila and D. Ziou},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-34548671710&doi=10.1109%2fCRV.2007.33&partnerID=40&md5=be89ffce30db18d0716df9eba2a197a2},
doi = {10.1109/CRV.2007.33},
isbn = {0-7695-2786-8 978-0-7695-2786-4},
year = {2007},
date = {2007-01-01},
booktitle = {Proceedings - Fourth Canadian Conference on Computer and Robot Vision, CRV 2007},
pages = {183–190},
address = {Montreal, QC},
abstract = {In this paper, we propose a finite mixture model of generalized Gaussian distributions (GDD) for robust segmentation and data modeling in the presence of noise and outliers. The model has more flexibility to adapt the shape of data and less sensibility for over-fitting the number of classes than the Gaussian mixture. In a first part of the present work, we propose a derivation of the Maximum-Likelihood estimation of the parameters of the new mixture model and we propose an information-theory based approach for the selection of the number of classes. In a second part, we propose some applications relating to image, motion and foreground segmentation to measure the performance of the new model in image data modeling with comparison to the Gaussian mixture. © 2007 IEEE.},
keywords = {Data structures, Finite mixture models, Foreground segmentation, Image segmentation, Information theory, Mathematical models, Maximum likelihood estimation, Mixture of General Gaussions (MoGG)},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper, we propose a finite mixture model of generalized Gaussian distributions (GDD) for robust segmentation and data modeling in the presence of noise and outliers. The model has more flexibility to adapt the shape of data and less sensibility for over-fitting the number of classes than the Gaussian mixture. In a first part of the present work, we propose a derivation of the Maximum-Likelihood estimation of the parameters of the new mixture model and we propose an information-theory based approach for the selection of the number of classes. In a second part, we propose some applications relating to image, motion and foreground segmentation to measure the performance of the new model in image data modeling with comparison to the Gaussian mixture. © 2007 IEEE.
4.
Allili, M. S.; Bouguila, N.; Ziou, D.
Online video foreground segmentation using general Gaussian mixture modeling Article d'actes
Dans: ICSPC 2007 Proceedings - 2007 IEEE International Conference on Signal Processing and Communications, p. 959–962, Dubai, 2007, ISBN: 978-1-4244-1236-5.
Résumé | Liens | BibTeX | Étiquettes: Bayesian approaches, Bayesian networks, Finite mixture models, Gaussian, Gaussian mixture modeling, Illumination changes, Image segmentation, Mixture of general gaussians (MoGG), Mixtures, MML, On-line estimations, Online videos, Parameter estimation, Signal processing, Trellis codes, Video foreground segmentation
@inproceedings{allili_online_2007,
title = {Online video foreground segmentation using general Gaussian mixture modeling},
author = {M. S. Allili and N. Bouguila and D. Ziou},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-60349106169&doi=10.1109%2fICSPC.2007.4728480&partnerID=40&md5=85c72d00cc58f61baf5ff006dc44957f},
doi = {10.1109/ICSPC.2007.4728480},
isbn = {978-1-4244-1236-5},
year = {2007},
date = {2007-01-01},
booktitle = {ICSPC 2007 Proceedings - 2007 IEEE International Conference on Signal Processing and Communications},
pages = {959–962},
address = {Dubai},
abstract = {In this paper, we propose a robust video foreground modeling by using a finite mixture model of general Gaussian distributions (GGD). The model has a flexibility to model the video background in the presence of sudden illumination changes and shadows, allowing for an efficient foreground segmentation. In a first part of the present work, we propose a derivation of the online estimation of the parameters of the mixture of GGDs and we propose a Bayesian approach for the selection of the number of classes. In a second part, we show experiments of video foreground segmentation demonstrating the performance of the proposed model. © 2007 IEEE.},
keywords = {Bayesian approaches, Bayesian networks, Finite mixture models, Gaussian, Gaussian mixture modeling, Illumination changes, Image segmentation, Mixture of general gaussians (MoGG), Mixtures, MML, On-line estimations, Online videos, Parameter estimation, Signal processing, Trellis codes, Video foreground segmentation},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper, we propose a robust video foreground modeling by using a finite mixture model of general Gaussian distributions (GGD). The model has a flexibility to model the video background in the presence of sudden illumination changes and shadows, allowing for an efficient foreground segmentation. In a first part of the present work, we propose a derivation of the online estimation of the parameters of the mixture of GGDs and we propose a Bayesian approach for the selection of the number of classes. In a second part, we show experiments of video foreground segmentation demonstrating the performance of the proposed model. © 2007 IEEE.
5.
Allili, M. S.; Ziou, D.
Object contour tracking in videos by matching finite mixture models Article d'actes
Dans: Proceedings - IEEE International Conference on Video and Signal Based Surveillance 2006, AVSS 2006, Sydney, NSW, 2006, ISBN: 0-7695-2688-8 978-0-7695-2688-1.
Résumé | Liens | BibTeX | Étiquettes: Boundary conditions, Color image processing, Contour measurement, Finite mixture models, Image analysis, Level sets, Object contour tracking, Pattern matching, Shape information, Video signal processing
@inproceedings{allili_object_2006,
title = {Object contour tracking in videos by matching finite mixture models},
author = {M. S. Allili and D. Ziou},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-34547433254&doi=10.1109%2fAVSS.2006.83&partnerID=40&md5=aeed9598e325243f03c379766e7ac32c},
doi = {10.1109/AVSS.2006.83},
isbn = {0-7695-2688-8 978-0-7695-2688-1},
year = {2006},
date = {2006-01-01},
booktitle = {Proceedings - IEEE International Conference on Video and Signal Based Surveillance 2006, AVSS 2006},
address = {Sydney, NSW},
abstract = {In this paper, we propose a novel object tracking algorithm in video sequences. The method is based on object mixture matching between successive frames of the sequence by using active contours. Only the segmentation of the objects in the first frame is required for initialization. The evolution of the object contour on a current frame aims to find the maximum fidelity of the mixture likelihood for the same object between successive frames while having the best fit of the mixture parameters to the homogenous parts of the objects. To permit for a precise and robust tracking, region, boundary and shape information are coupled in the model. The method permits for tracking multi-class objects on cluttered and non-static backgrounds. We validate our approach on examples of tracking performed on real video sequences. © 2006 IEEE.},
keywords = {Boundary conditions, Color image processing, Contour measurement, Finite mixture models, Image analysis, Level sets, Object contour tracking, Pattern matching, Shape information, Video signal processing},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper, we propose a novel object tracking algorithm in video sequences. The method is based on object mixture matching between successive frames of the sequence by using active contours. Only the segmentation of the objects in the first frame is required for initialization. The evolution of the object contour on a current frame aims to find the maximum fidelity of the mixture likelihood for the same object between successive frames while having the best fit of the mixture parameters to the homogenous parts of the objects. To permit for a precise and robust tracking, region, boundary and shape information are coupled in the model. The method permits for tracking multi-class objects on cluttered and non-static backgrounds. We validate our approach on examples of tracking performed on real video sequences. © 2006 IEEE.