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.
Globally adaptive region information for automatic color-texture image segmentation Article de journal
Dans: Pattern Recognition Letters, vol. 28, no 15, p. 1946–1956, 2007, ISSN: 01678655.
Résumé | Liens | BibTeX | Étiquettes: Algorithms, Automatic segmentation, Boundary information, Color image processing, Color texture image segmentation, Contour measurement, Image analysis, Image segmentation, Level sets, Polarity, Textures
@article{allili_globally_2007,
title = {Globally adaptive region information for automatic color-texture image segmentation},
author = {M. S. Allili and D. Ziou},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-34548675168&doi=10.1016%2fj.patrec.2007.05.002&partnerID=40&md5=e1338223b9cb99afc35dfbfbf7859b72},
doi = {10.1016/j.patrec.2007.05.002},
issn = {01678655},
year = {2007},
date = {2007-01-01},
journal = {Pattern Recognition Letters},
volume = {28},
number = {15},
pages = {1946–1956},
abstract = {In this paper, we propose an automatic segmentation of color-texture images with arbitrary numbers of regions. The approach combines region and boundary information and uses active contours to build a partition of the image. The segmentation algorithm is initialized automatically by using homogeneous region seeds on the image domain. The partition of the image is formed by evolving the region contours and adaptively updating the region information formulated using a mixture of pdfs. We show the performance of the proposed method on examples of color-texture image segmentation, with comparison to two state-of-the-art methods. © 2007 Elsevier B.V. All rights reserved.},
keywords = {Algorithms, Automatic segmentation, Boundary information, Color image processing, Color texture image segmentation, Contour measurement, Image analysis, Image segmentation, Level sets, Polarity, Textures},
pubstate = {published},
tppubtype = {article}
}
In this paper, we propose an automatic segmentation of color-texture images with arbitrary numbers of regions. The approach combines region and boundary information and uses active contours to build a partition of the image. The segmentation algorithm is initialized automatically by using homogeneous region seeds on the image domain. The partition of the image is formed by evolving the region contours and adaptively updating the region information formulated using a mixture of pdfs. We show the performance of the proposed method on examples of color-texture image segmentation, with comparison to two state-of-the-art methods. © 2007 Elsevier B.V. All rights reserved.
2.
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.
3.
Allili, M. S.; Ziou, D.
Automatic change detection and updating of topographic databases by using satellite imagery: A level set approach Article de journal
Dans: Geomatica, vol. 59, no 3, p. 275–281, 2005, ISSN: 11951036 (ISSN).
Résumé | Liens | BibTeX | Étiquettes: Adaptive control systems, Adaptive segmentation, Color image processing, Computer Simulation, Image analysis, Image segmentation, Level sets, Mixture analysis, Polarity smoothing, Probability density function
@article{allili_automatic_2005,
title = {Automatic change detection and updating of topographic databases by using satellite imagery: A level set approach},
author = {M. S. Allili and D. Ziou},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-28444481044&partnerID=40&md5=e5d147401a402b14084292a2eeb6a792},
doi = {10.1109/CRV.2005.14},
issn = {11951036 (ISSN)},
year = {2005},
date = {2005-01-01},
booktitle = {Proceedings - 2nd Canadian Conference on Computer and Robot Vision, CRV 2005},
journal = {Geomatica},
volume = {59},
number = {3},
pages = {275–281},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {In order to keep up-to-date geospatial data in topographic databases, automatic change detection and data updating is required. In the present paper, we investigate the automatic change detection of geospatial data by using level set active contours. We propose an approach that is based on region comparison between two multi-temporal datasets. Firstly, the regions are extracted from two co-registered images taken apart in time by using level set based active contours segmentation. Then, the change detection is performed by spatially comparing the resulting region segments from the two images. The approach is validated by experiments relating to the change detection of lake surfaces by using Landsat7 multi-spectral imagery.},
keywords = {Adaptive control systems, Adaptive segmentation, Color image processing, Computer Simulation, Image analysis, Image segmentation, Level sets, Mixture analysis, Polarity smoothing, Probability density function},
pubstate = {published},
tppubtype = {article}
}
In order to keep up-to-date geospatial data in topographic databases, automatic change detection and data updating is required. In the present paper, we investigate the automatic change detection of geospatial data by using level set active contours. We propose an approach that is based on region comparison between two multi-temporal datasets. Firstly, the regions are extracted from two co-registered images taken apart in time by using level set based active contours segmentation. Then, the change detection is performed by spatially comparing the resulting region segments from the two images. The approach is validated by experiments relating to the change detection of lake surfaces by using Landsat7 multi-spectral imagery.
4.
Allili, M. S.; Ziou, D.
An automatic segmentation combining mixture analysis and adaptive region information: A level set approach Article d'actes
Dans: Proceedings - 2nd Canadian Conference on Computer and Robot Vision, CRV 2005, p. 73–80, Institute of Electrical and Electronics Engineers Inc., Genova, 2005, ISBN: 0769523196 (ISBN); 978-076952319-4 (ISBN), (Journal Abbreviation: Proc. - Can. Conf. Comput. Robot Vis., CRV).
Résumé | Liens | BibTeX | Étiquettes: Adaptive segmentation, Automatic segmentations, Color image processing, Color image segmentation, Computer vision, Energy functionals, Image segmentation, Level Set, Level sets, Mixture analysis, Mixtures, Polarity smoothing, Posterior probability
@inproceedings{allili_automatic_2005-1,
title = {An automatic segmentation combining mixture analysis and adaptive region information: A level set approach},
author = {M. S. Allili and D. Ziou},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-33845531999&doi=10.1109%2fCRV.2005.14&partnerID=40&md5=c9773a2f28fe00b4171511895b721158},
doi = {10.1109/CRV.2005.14},
isbn = {0769523196 (ISBN); 978-076952319-4 (ISBN)},
year = {2005},
date = {2005-01-01},
booktitle = {Proceedings - 2nd Canadian Conference on Computer and Robot Vision, CRV 2005},
volume = {1},
pages = {73–80},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
address = {Genova},
abstract = {In this paper, we propose a novel automatic framework for variational color image segmentation based on unifying adaptive region information and mixture modelling. We consider a formulation of the region information by using the posterior probability of a mixture of general Gaussian (GG) pdfs, where each region is represented by a pdf. The segmentation is formulated by the minimization of an energy functional according to the region contours and all the mixture parameters respectively. Two main objectives are achieved by the approach. A scheme is provided to extend easily the adaptive segmentation to an arbitrary number of regions and to perform it in a fully automatic fashion. Moreover, the segmentation recovers an accurate and representative mixture of pdfs. In the approach, we couple the boundary and region information of the image to steer the segmentation. We validate the method on the segmentation of real world color images. © 2005 IEEE.},
note = {Journal Abbreviation: Proc. - Can. Conf. Comput. Robot Vis., CRV},
keywords = {Adaptive segmentation, Automatic segmentations, Color image processing, Color image segmentation, Computer vision, Energy functionals, Image segmentation, Level Set, Level sets, Mixture analysis, Mixtures, Polarity smoothing, Posterior probability},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper, we propose a novel automatic framework for variational color image segmentation based on unifying adaptive region information and mixture modelling. We consider a formulation of the region information by using the posterior probability of a mixture of general Gaussian (GG) pdfs, where each region is represented by a pdf. The segmentation is formulated by the minimization of an energy functional according to the region contours and all the mixture parameters respectively. Two main objectives are achieved by the approach. A scheme is provided to extend easily the adaptive segmentation to an arbitrary number of regions and to perform it in a fully automatic fashion. Moreover, the segmentation recovers an accurate and representative mixture of pdfs. In the approach, we couple the boundary and region information of the image to steer the segmentation. We validate the method on the segmentation of real world color images. © 2005 IEEE.