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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}
}
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{alliliApproachDynamicCombination2008,
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}
}
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}
}
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}
}