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.; Ziou, D.
An approach for dynamic combination of region and boundary information in segmentation Proceedings Article
In: Proceedings - International Conference on Pattern Recognition, Institute of Electrical and Electronics Engineers Inc., 2008, ISBN: 10514651 (ISSN); 978-142442175-6 (ISBN), (Journal Abbreviation: Proc. Int. Conf. Pattern Recognit.).
Abstract | Links | BibTeX | Tags: Arbitrary weighting, Bayesian formulation, Bayesian networks, Boundary information, Dynamic combination, Energy functionals, Hyper-parameter, Image segmentation, New approaches, Parameter estimation, Pattern Recognition, Region information
@inproceedings{allili_approach_2008,
title = {An approach for dynamic combination of region and boundary information in segmentation},
author = {M. S. Allili and D. Ziou},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-77957954657&doi=10.1109%2ficpr.2008.4761384&partnerID=40&md5=92c6dc032da4938d5c1c3ced6af1671c},
doi = {10.1109/icpr.2008.4761384},
isbn = {10514651 (ISSN); 978-142442175-6 (ISBN)},
year = {2008},
date = {2008-01-01},
booktitle = {Proceedings - International Conference on Pattern Recognition},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Image segmentation combining boundary and region information has been the subject of numerous research works in the past. This combination is usually subject to arbitrary weighting parameters (hyper-parameters) that control the contribution of boundary and region features during segmentation. In this work, we investigate a new approach for estimating the hyper-parameters adaptively to segmentation. The approach takes its roots from the physical properties of the energy functional controlling segmentation and a Bayesian formulation of segmentation and hyper-parameters estimation. © 2008 IEEE.},
note = {Journal Abbreviation: Proc. Int. Conf. Pattern Recognit.},
keywords = {Arbitrary weighting, Bayesian formulation, Bayesian networks, Boundary information, Dynamic combination, Energy functionals, Hyper-parameter, Image segmentation, New approaches, Parameter estimation, Pattern Recognition, Region information},
pubstate = {published},
tppubtype = {inproceedings}
}
Image segmentation combining boundary and region information has been the subject of numerous research works in the past. This combination is usually subject to arbitrary weighting parameters (hyper-parameters) that control the contribution of boundary and region features during segmentation. In this work, we investigate a new approach for estimating the hyper-parameters adaptively to segmentation. The approach takes its roots from the physical properties of the energy functional controlling segmentation and a Bayesian formulation of segmentation and hyper-parameters estimation. © 2008 IEEE.
2.
Allili, M. S.; Ziou, D.
Automatic colour-texture image segmentation using active contours Journal Article
In: International Journal of Computer Mathematics, vol. 84, no. 9, pp. 1325–1338, 2007, ISSN: 00207160.
Abstract | Links | BibTeX | Tags: Automatic segmentation, Computation theory, Image segmentation, Optimization, Parameter estimation, Texture image segmentation, Textures
@article{allili_automatic_2007,
title = {Automatic colour-texture image segmentation using active contours},
author = {M. S. Allili and D. Ziou},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-34548354750&doi=10.1080%2f00207160701250501&partnerID=40&md5=69002e599b1c570571b04367ec08d2ac},
doi = {10.1080/00207160701250501},
issn = {00207160},
year = {2007},
date = {2007-01-01},
journal = {International Journal of Computer Mathematics},
volume = {84},
number = {9},
pages = {1325–1338},
abstract = {In this paper we propose a fully automatic segmentation method for colour/texture images. By fully automatic, we mean that the steps of region initialization and calculation of the number of regions are performed automatically by the method. The region information is formulated using a mixture of pdfs for the combination of colour and texture features. The segmentation is obtained by minimizing an energy functional combining boundary and region information, which evolves the initial region contours towards the real region boundaries and adapts the mixture parameters to the region data. The method is implemented using the level sets that permit automatic handling of topology changes and stable numerical schemes. We validate the approach using examples of synthetic and natural colour-texture image segmentation.},
keywords = {Automatic segmentation, Computation theory, Image segmentation, Optimization, Parameter estimation, Texture image segmentation, Textures},
pubstate = {published},
tppubtype = {article}
}
In this paper we propose a fully automatic segmentation method for colour/texture images. By fully automatic, we mean that the steps of region initialization and calculation of the number of regions are performed automatically by the method. The region information is formulated using a mixture of pdfs for the combination of colour and texture features. The segmentation is obtained by minimizing an energy functional combining boundary and region information, which evolves the initial region contours towards the real region boundaries and adapts the mixture parameters to the region data. The method is implemented using the level sets that permit automatic handling of topology changes and stable numerical schemes. We validate the approach using examples of synthetic and natural colour-texture image segmentation.
3.
Allili, M. S.; Bouguila, N.; Ziou, D.
Online video foreground segmentation using general Gaussian mixture modeling Proceedings Article
In: ICSPC 2007 Proceedings - 2007 IEEE International Conference on Signal Processing and Communications, pp. 959–962, Dubai, 2007, ISBN: 978-1-4244-1236-5.
Abstract | Links | BibTeX | Tags: 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.
4.
Allili, M. S.; Ziou, D.
A Bayesian approach for weighting boundary and region information for segmentation Journal Article
In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3708 LNCS, pp. 468–475, 2005, ISSN: 03029743, (ISBN: 354029032X; 9783540290322 Place: Antwerp).
Abstract | Links | BibTeX | Tags: Adaptive systems, Bayesian approach, Boundary localization, Boundary value problems, decision making, Image segmentation, Lighting, Parameter estimation, Segmentation, Textures, Variational image segmentation
@article{allili_bayesian_2005,
title = {A Bayesian approach for weighting boundary and region information for segmentation},
author = {M. S. Allili and D. Ziou},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-33646174288&doi=10.1007%2f11558484_59&partnerID=40&md5=e98b02fbcf69f8acda0b171ce009d5ff},
doi = {10.1007/11558484_59},
issn = {03029743},
year = {2005},
date = {2005-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {3708 LNCS},
pages = {468–475},
abstract = {Variational image segmentation combining boundary and region information was and still is the subject of many recent works. This combination is usually subject to arbitrary weighting parameters that control the boundary and region features contribution during the segmentation. However, since the objective functions of the boundary and the region features is different in nature, their arbitrary combination may conduct to local conflicts that stem principally from abrupt illumination changes or the presence of texture inside the regions. In the present paper, we investigate an adaptive estimation of the weighting parameters (hyper-parameters) on the regions data during the segmentation by using a Bayesian method. This permits to give adequate contributions of the boundary and region features to segmentation decision making for pixels and, therefore, improving the accuracy of region boundary localization. We validated the approach on examples of real world images. © Springer-Verlag Berlin Heidelberg 2005.},
note = {ISBN: 354029032X; 9783540290322
Place: Antwerp},
keywords = {Adaptive systems, Bayesian approach, Boundary localization, Boundary value problems, decision making, Image segmentation, Lighting, Parameter estimation, Segmentation, Textures, Variational image segmentation},
pubstate = {published},
tppubtype = {article}
}
Variational image segmentation combining boundary and region information was and still is the subject of many recent works. This combination is usually subject to arbitrary weighting parameters that control the boundary and region features contribution during the segmentation. However, since the objective functions of the boundary and the region features is different in nature, their arbitrary combination may conduct to local conflicts that stem principally from abrupt illumination changes or the presence of texture inside the regions. In the present paper, we investigate an adaptive estimation of the weighting parameters (hyper-parameters) on the regions data during the segmentation by using a Bayesian method. This permits to give adequate contributions of the boundary and region features to segmentation decision making for pixels and, therefore, improving the accuracy of region boundary localization. We validated the approach on examples of real world images. © Springer-Verlag Berlin Heidelberg 2005.