@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}

}

@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}

}

@inproceedings{allili_robust_2007,

title = {A robust video foreground segmentation by using generalized gaussian mixture modeling},

author = {M. S. Allili and N. Bouguila and D. Ziou},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-34548763871&doi=10.1109%2fCRV.2007.7&partnerID=40&md5=e720d61af262e01677a3e23d8c4e6ad0},

doi = {10.1109/CRV.2007.7},

isbn = {0769527868 (ISBN); 978-076952786-4 (ISBN)},

year  = {2007},

date = {2007-01-01},

booktitle = {Proceedings - Fourth Canadian Conference on Computer and Robot Vision, CRV 2007},

pages = {503–509},

address = {Montreal, QC},

abstract = {In this paper, we propose a robust video foreground modeling by using a finite mixture model of generalized Gaussian distributions (GDD). 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 GDDS 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.},

note = {Journal Abbreviation: Proc. Fourth Can. Conf. Comput. Robot Vis.},

keywords = {Bayesian networks, Gaussian mixtures, Image segmentation, Mathematical models, Mixture of general gaussians (MoGG), MML, Video foreground segmentation, Video signal processing},

pubstate = {published},

tppubtype = {inproceedings}

}