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.
Object contour tracking using foreground and background distribution matching Article de journal
Dans: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5856 LNCS, p. 954–961, 2009, ISSN: 03029743, (ISBN: 3642102670; 9783642102677 Place: Guadalajara, Jalisco).
Résumé | Liens | BibTeX | Étiquettes: Active contours, Computer applications, Computer vision, Distribution matching, Distribution parameters, Image matching, Object contour, Tracked objects
@article{allili_object_2009,
title = {Object contour tracking using foreground and background distribution matching},
author = {M. S. Allili},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-78651256419&doi=10.1007%2f978-3-642-10268-4_111&partnerID=40&md5=0852d2cf799d98cff187d1b10b2e5c34},
doi = {10.1007/978-3-642-10268-4_111},
issn = {03029743},
year = {2009},
date = {2009-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {5856 LNCS},
pages = {954–961},
abstract = {In this paper, we propose an effective approach for tracking distribution of objects. The approach uses a competition between a tracked object and background distributions using active contours. Only the segmentation of the object in the first frame is required for initialization. The object contour is tracked by assigning pixels in a way that maximizes the likelihood of the object versus the background. We implement the approach using an EM-like algorithm which evolves the object contour exactly to its boundaries and adapts the distribution parameters of the object and the background to data. © 2009 Springer-Verlag Berlin Heidelberg.},
note = {ISBN: 3642102670; 9783642102677
Place: Guadalajara, Jalisco},
keywords = {Active contours, Computer applications, Computer vision, Distribution matching, Distribution parameters, Image matching, Object contour, Tracked objects},
pubstate = {published},
tppubtype = {article}
}
In this paper, we propose an effective approach for tracking distribution of objects. The approach uses a competition between a tracked object and background distributions using active contours. Only the segmentation of the object in the first frame is required for initialization. The object contour is tracked by assigning pixels in a way that maximizes the likelihood of the object versus the background. We implement the approach using an EM-like algorithm which evolves the object contour exactly to its boundaries and adapts the distribution parameters of the object and the background to data. © 2009 Springer-Verlag Berlin Heidelberg.
2.
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.
3.
Allili, M. S.; Ziou, D.
Object tracking in videos using adaptive mixture models and active contours Article de journal
Dans: Neurocomputing, vol. 71, no 10-12, p. 2001–2011, 2008, ISSN: 09252312.
Résumé | Liens | BibTeX | Étiquettes: Active contours, algorithm, Algorithms, article, controlled study, Image analysis, Image processing, imaging system, Level set method, Mathematical models, motion analysis system, Object recognition, priority journal, Set theory, statistical model, Video cameras, Video sequences, videorecording, visual information
@article{allili_object_2008,
title = {Object tracking in videos using adaptive mixture models and active contours},
author = {M. S. Allili and D. Ziou},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-44649197137&doi=10.1016%2fj.neucom.2007.10.019&partnerID=40&md5=a2aef677fae1b220f68c9fd720be3fd5},
doi = {10.1016/j.neucom.2007.10.019},
issn = {09252312},
year = {2008},
date = {2008-01-01},
journal = {Neurocomputing},
volume = {71},
number = {10-12},
pages = {2001–2011},
abstract = {In this paper, we propose a novel object tracking algorithm for video sequences, based on active contours. The tracking is based on matching the object appearance model between successive frames of the sequence using active contours. We formulate the tracking as a minimization of an objective function incorporating region, boundary and shape information. Further, in order to handle variation in object appearance due to self-shadowing, changing illumination conditions and camera geometry, we propose an adaptive mixture model for the object representation. The implementation of the method is based on the level set method. We validate our approach on tracking examples using real video sequences, with comparison to two recent state-of-the-art methods. © 2008 Elsevier B.V. All rights reserved.},
keywords = {Active contours, algorithm, Algorithms, article, controlled study, Image analysis, Image processing, imaging system, Level set method, Mathematical models, motion analysis system, Object recognition, priority journal, Set theory, statistical model, Video cameras, Video sequences, videorecording, visual information},
pubstate = {published},
tppubtype = {article}
}
In this paper, we propose a novel object tracking algorithm for video sequences, based on active contours. The tracking is based on matching the object appearance model between successive frames of the sequence using active contours. We formulate the tracking as a minimization of an objective function incorporating region, boundary and shape information. Further, in order to handle variation in object appearance due to self-shadowing, changing illumination conditions and camera geometry, we propose an adaptive mixture model for the object representation. The implementation of the method is based on the level set method. We validate our approach on tracking examples using real video sequences, with comparison to two recent state-of-the-art methods. © 2008 Elsevier B.V. All rights reserved.
4.
Allili, M. S.; Ziou, D.
Using feature selection for object segmentation and tracking Article d'actes
Dans: Proceedings - Fourth Canadian Conference on Computer and Robot Vision, CRV 2007, p. 191–198, Montreal, QC, 2007, ISBN: 0-7695-2786-8 978-0-7695-2786-4.
Résumé | Liens | BibTeX | Étiquettes: Active contours, Algorithms, Feature extraction, Feature relevance, Image segmentation, Maximum likelihood, Mixture models, Negative examples, Object of interest (OOI), Optimization, Target tracking
@inproceedings{allili_using_2007,
title = {Using feature selection for object segmentation and tracking},
author = {M. S. Allili and D. Ziou},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-34548781938&doi=10.1109%2fCRV.2007.67&partnerID=40&md5=3fb26f3fcc7a6f55f705255758fef582},
doi = {10.1109/CRV.2007.67},
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 = {191–198},
address = {Montreal, QC},
abstract = {Most image segmentation algorithms in the past are based on optimizing an objective function that aims to achieve the similarity between several low-level features to build a partition of the image into homogeneous regions. In the present paper, we propose to incorporate the relevance (selection) of the grouping features to enforce the segmentation toward the capturing of objects of interest. The relevance of the features is determined through a set of positive and negative examples of a specific object defined a priori by the user. The calculation of the relevance of the features is performed by maximizing an objective function defined on the mixture likelihoods of the positive and negative object examples sets. The incorporation of the features relevance in the object segmentation is formulated through an energy functional which is minimized by using level set active contours. We show the efficiency of the approach on several examples of object of interest segmentation and tracking where the features relevance was used. © 2007 IEEE.},
keywords = {Active contours, Algorithms, Feature extraction, Feature relevance, Image segmentation, Maximum likelihood, Mixture models, Negative examples, Object of interest (OOI), Optimization, Target tracking},
pubstate = {published},
tppubtype = {inproceedings}
}
Most image segmentation algorithms in the past are based on optimizing an objective function that aims to achieve the similarity between several low-level features to build a partition of the image into homogeneous regions. In the present paper, we propose to incorporate the relevance (selection) of the grouping features to enforce the segmentation toward the capturing of objects of interest. The relevance of the features is determined through a set of positive and negative examples of a specific object defined a priori by the user. The calculation of the relevance of the features is performed by maximizing an objective function defined on the mixture likelihoods of the positive and negative object examples sets. The incorporation of the features relevance in the object segmentation is formulated through an energy functional which is minimized by using level set active contours. We show the efficiency of the approach on several examples of object of interest segmentation and tracking where the features relevance was used. © 2007 IEEE.
5.
Allili, M. S.; Ziou, D.
Object contour tracking in videos by using adaptive mixture models and shape priors Article d'actes
Dans: Proceedings of the International Symposium CompIMAGE 2006 - Computational Modelling of Objects Represented in Images: Fundamentals, Methods and Applications, p. 47–52, Coimbra, 2007, ISBN: 978-0-415-43349-5.
Résumé | Liens | BibTeX | Étiquettes: Active contours, Best fits, Current frames, Image matching, Maximum likelihood, Mixture models, Mixtures, Multi class, Non-static backgrounds, Object contours, Object tracking algorithms, Real video sequences, Robust tracking, Shape informations, Shape priors, Video recording, Video sequences
@inproceedings{allili_object_2007-1,
title = {Object contour tracking in videos by using adaptive mixture models and shape priors},
author = {M. S. Allili and D. Ziou},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-60949085472&partnerID=40&md5=ba63e1abbabcfdd48583b41f700508ef},
isbn = {978-0-415-43349-5},
year = {2007},
date = {2007-01-01},
booktitle = {Proceedings of the International Symposium CompIMAGE 2006 - Computational Modelling of Objects Represented in Images: Fundamentals, Methods and Applications},
pages = {47–52},
address = {Coimbra},
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. © 2007 Taylor & Francis Group.},
keywords = {Active contours, Best fits, Current frames, Image matching, Maximum likelihood, Mixture models, Mixtures, Multi class, Non-static backgrounds, Object contours, Object tracking algorithms, Real video sequences, Robust tracking, Shape informations, Shape priors, Video recording, Video sequences},
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. © 2007 Taylor & Francis Group.
6.
Allili, M. S.; Ziou, D.
Automatic color-texture image segmentation by using active contours Article de journal
Dans: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4153 LNCS, p. 495–504, 2006, ISSN: 03029743, (ISBN: 354037597X; 9783540375975 Place: Xi'an Publisher: Springer Verlag).
Résumé | Liens | BibTeX | Étiquettes: Active contours, Algorithms, Automatic segmentation, Automation, Boundary localization, Color texture segmentation, Color vision, Image segmentation, Information analysis, Textures
@article{allili_automatic_2006,
title = {Automatic color-texture image segmentation by using active contours},
author = {M. S. Allili and D. Ziou},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-33750065117&doi=10.1007%2f11821045_52&partnerID=40&md5=a2eb2582bd6d565ff0c64278e31112a1},
doi = {10.1007/11821045_52},
issn = {03029743},
year = {2006},
date = {2006-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {4153 LNCS},
pages = {495–504},
abstract = {In this paper, we propose a novel method for unsupervised color-texture segmentation. The approach aims at combining color and texture features and active contours to build a fully automatic segmentation algorithm. By fully automatic, we mean the steps of region initialization and calculation of the number of regions are performed automatically by the algorithm. Furthermore, the approach combines boundary and region information for accurate region boundary localization. We validate the approach by examples of synthetic and natural color-texture image segmentation. © Springer-Verlag Berlin Heidelberg 2006.},
note = {ISBN: 354037597X; 9783540375975
Place: Xi'an
Publisher: Springer Verlag},
keywords = {Active contours, Algorithms, Automatic segmentation, Automation, Boundary localization, Color texture segmentation, Color vision, Image segmentation, Information analysis, Textures},
pubstate = {published},
tppubtype = {article}
}
In this paper, we propose a novel method for unsupervised color-texture segmentation. The approach aims at combining color and texture features and active contours to build a fully automatic segmentation algorithm. By fully automatic, we mean the steps of region initialization and calculation of the number of regions are performed automatically by the algorithm. Furthermore, the approach combines boundary and region information for accurate region boundary localization. We validate the approach by examples of synthetic and natural color-texture image segmentation. © Springer-Verlag Berlin Heidelberg 2006.