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.
Automatic change detection and updating of topographic databases by using satellite imagery: A level set approach Journal Article
In: Geomatica, vol. 59, no. 3, pp. 275–281, 2005, ISSN: 11951036 (ISSN).
Abstract | Links | BibTeX | Tags: 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.
2.
Allili, M. S.; Ziou, D.
An automatic segmentation combining mixture analysis and adaptive region information: A level set approach Proceedings Article
In: Proceedings - 2nd Canadian Conference on Computer and Robot Vision, CRV 2005, pp. 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).
Abstract | Links | BibTeX | Tags: 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.
3.
Allili, M. S.; Ziou, D.
An automatic segmentation combining mixture analysis and adaptive region information: A level set approach Proceedings Article
In: Proceedings - 2nd Canadian Conference on Computer and Robot Vision, CRV 2005, pp. 73–80, Institute of Electrical and Electronics Engineers Inc., 2005, ISBN: 0769523196 (ISBN); 978-076952319-4 (ISBN), (Journal Abbreviation: Proc. - Can. Conf. Comput. Robot Vis., CRV).
Abstract | Links | BibTeX | Tags: 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{alliliAutomaticSegmentationCombining2005,
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},
pages = {73–80},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
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.