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 colour-texture image segmentation using active contours Journal Article
In: International Journal of Computer Mathematics, vol. 84, no. 9, pp. 1325–1338, 2007, ISSN: 00207160.
Abstract | Links | BibTeX | Tags: Automatic segmentation, Computation theory, Image segmentation, Optimization, Parameter estimation, Texture image segmentation, Textures
@article{allili_automatic_2007,
title = {Automatic colour-texture image segmentation using active contours},
author = {M. S. Allili and D. Ziou},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-34548354750&doi=10.1080%2f00207160701250501&partnerID=40&md5=69002e599b1c570571b04367ec08d2ac},
doi = {10.1080/00207160701250501},
issn = {00207160},
year = {2007},
date = {2007-01-01},
journal = {International Journal of Computer Mathematics},
volume = {84},
number = {9},
pages = {1325–1338},
abstract = {In this paper we propose a fully automatic segmentation method for colour/texture images. By fully automatic, we mean that the steps of region initialization and calculation of the number of regions are performed automatically by the method. The region information is formulated using a mixture of pdfs for the combination of colour and texture features. The segmentation is obtained by minimizing an energy functional combining boundary and region information, which evolves the initial region contours towards the real region boundaries and adapts the mixture parameters to the region data. The method is implemented using the level sets that permit automatic handling of topology changes and stable numerical schemes. We validate the approach using examples of synthetic and natural colour-texture image segmentation.},
keywords = {Automatic segmentation, Computation theory, Image segmentation, Optimization, Parameter estimation, Texture image segmentation, Textures},
pubstate = {published},
tppubtype = {article}
}
In this paper we propose a fully automatic segmentation method for colour/texture images. By fully automatic, we mean that the steps of region initialization and calculation of the number of regions are performed automatically by the method. The region information is formulated using a mixture of pdfs for the combination of colour and texture features. The segmentation is obtained by minimizing an energy functional combining boundary and region information, which evolves the initial region contours towards the real region boundaries and adapts the mixture parameters to the region data. The method is implemented using the level sets that permit automatic handling of topology changes and stable numerical schemes. We validate the approach using examples of synthetic and natural colour-texture image segmentation.
2.
Allili, M. S.; Ziou, D.
Using feature selection for object segmentation and tracking Proceedings Article
In: Proceedings - Fourth Canadian Conference on Computer and Robot Vision, CRV 2007, pp. 191–198, Montreal, QC, 2007, ISBN: 0-7695-2786-8 978-0-7695-2786-4.
Abstract | Links | BibTeX | Tags: 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.
3.
Allili, M. S.; Ziou, D.
Object of interest segmentation and tracking by using feature selection and active contours Proceedings Article
In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Minneapolis, MN, 2007, ISBN: 1-4244-1180-7 978-1-4244-1180-1, (ISSN: 10636919).
Abstract | Links | BibTeX | Tags: Feature extraction, Image acquisition, Image segmentation, Object recognition, Object segmentation, Objective functions, Optimization
@inproceedings{allili_object_2007,
title = {Object of interest segmentation and tracking by using feature selection and active contours},
author = {M. S. Allili and D. Ziou},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-34948855864&doi=10.1109%2fCVPR.2007.383449&partnerID=40&md5=2429a266190c72bb8fb8d3776c444906},
doi = {10.1109/CVPR.2007.383449},
isbn = {1-4244-1180-7 978-1-4244-1180-1},
year = {2007},
date = {2007-01-01},
booktitle = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
address = {Minneapolis, MN},
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 is used. © 2007 IEEE.},
note = {ISSN: 10636919},
keywords = {Feature extraction, Image acquisition, Image segmentation, Object recognition, Object segmentation, Objective functions, Optimization},
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 is used. © 2007 IEEE.