Publications of Aymen El Ghoul

Result of the query in the list of publications :

• 1 PhD Thesis and Habilitation
• 6 Conference articles

new search | get the bibtex

display : 10 per page | 20 per page | 50 per page

PhD Thesis and Habilitation

1 - Phase fields for network extraction from images. A. El Ghoul. PhD Thesis, Universite de Nice - Sophia-Antipolis, September 2010. Keywords : Shape prior, Higher-order actif contours, Phase Field, Stability analysis, Directed networks, river extraction. @PHDTHESIS{elghoul10c, author = {El Ghoul, A.}, title = {Phase fields for network extraction from images}, year = {2010}, month = {September}, school = {Universite de Nice - Sophia-Antipolis}, url = {http://tel.archives-ouvertes.fr/docs/00/55/01/34/PDF/ThesisMunuscript2010_EL_GHOUL.pdf}, keyword = {Shape prior, Higher-order actif contours, Phase Field, Stability analysis, Directed networks, river extraction} } Résumé : Cette thèse décrit la construction d'un modèle de réseaux non-directionnels (e.g. réseaux routiers), fondé sur les contours actifs d'ordre supérieur (CAOSs) et les champs de phase développés récemment, et introduit une nouvelle famille des CAOSs des champs de phase pour des réseaux directionnels (e.g. réseaux hydrographiques en imagerie de télédétection, vaisseaux sanguins en imagerie médicale). Dans la première partie de cette thèse, nous nous intéressons à l'analyse de stabilité d'une énergie de type CAOSs aboutissant à un ‘diagramme de phase'. Les résultats, qui sont confirmés par des expériences numériques, permettent une bonne sélection des valeurs des paramètres pour la modélisation de réseaux non-directionnels. Au contraire des réseaux routiers, les réseaux hydrographiques sont directionnels, i.e. ils contiennent un ‘flux' monodimensionnel circulant dans chaque branche. Cela implique des propriétés géométriques spécifiques des branches et particulièrement des jonctions, propriétés qu'il est utile de traduire dans un modèle, pour l'extraction de réseaux. Nous développons donc un modèle de champ de phase non-local de réseaux directionnels, qui, en plus du champ de phase scalaire décrivant une région par une fonction caractéristique lisse et qui interagit non-localement afin que des configurations de réseaux linéiques soient favorisées, introduit un champ vectoriel représentant le ‘flux' dans les branches du réseau. Ce champ vectoriel est contraint d'être nul à l'extérieur, et de magnitude égale à 1 à l'intérieur du réseau ; circulant dans le sens longitudinal des branches du réseau ; et de divergence très faible. Cela prolonge les branches du réseau ; contrôle la variation de largeur tout au long une branche ; et forme des jonctions non-symétriques telles que la somme des largeurs entrantes soit approximativement égale à celle des largeurs sortantes. En conjonction avec une nouvelle fonction d'interaction pour le champ de phase scalaire, le modèle assure aussi une vaste gamme de valeurs des largeurs stables des branches. Ce nouveau modèle a été appliqué au problème d'extraction de réseaux hydrographiques à partir d'images satellitaires très haute résolution. Abstract : This thesis describes the construction of an undirected network (e.g. road network) model, based on the recently developed higher-order active contours (HOACs) and phase fields, and introduces a new family of phase field HOACs for directed networks (e.g. hydrographic networks in remote sensing imagery, vascular networks in medical imagery). In the first part of this thesis, we focus on the stability analysis of a HOAC energy leading to a ‘phase diagram'. The results, which are confirmed by numerical experiments, enable the selection of parameter values for the modelling of undirected networks. Hydrographic networks, unlike road networks, are directed, i.e. they carry a unidirectional flow in each branch. This leads to specific geometric properties of the branches and particularly of the junctions, that it is useful to capture in a model, for network extraction purposes. We thus develop a nonlocal phase field model of directed networks, which, in addition to a scalar field representing a region by its smoothed characteristic function, and interacting nonlocally so as to favour network configurations, contains a vector field representing the ‘flow' through the network branches. The vector field is strongly encouraged to be zero outside, and of unit magnitude inside the network; and to have zero divergence. This prolongs network branches; controls width variation along a branch; and produces asymmetric junctions for which total incoming branch width approximately equals total outgoing branch width. In conjunction with a new interaction function for the scalar field, it also allows a broad range of stable branch widths. The new proposed model is applied to the problem of hydrographic network extraction from VHR satellite images, and it outperforms the undirected network model.

top of the page

6 Conference articles

1 - A theoretical and numerical study of a phase field higher-order active contour model of directed networks. A. El Ghoul and I. H. Jermyn and J. Zerubia. In The Tenth Asian Conference on Computer Vision (ACCV), Queenstown, New Zealand, November 2010. Keywords : Phase Field, Shape prior, Directed networks, Stability analysis, river extraction, remote sensing. Copyright : Springer-Verlag GmbH Berlin Heidelberg @INPROCEEDINGS{Elghoul10b, author = {El Ghoul, A. and Jermyn, I. H. and Zerubia, J.}, title = {A theoretical and numerical study of a phase field higher-order active contour model of directed networks}, year = {2010}, month = {November}, booktitle = {The Tenth Asian Conference on Computer Vision (ACCV)}, address = {Queenstown, New Zealand}, pdf = {http://hal.archives-ouvertes.fr/inria-00522443/fr/}, keyword = {Phase Field, Shape prior, Directed networks, Stability analysis, river extraction, remote sensing} } Abstract : We address the problem of quasi-automatic extraction of directed networks, which have characteristic geometric features, from images. To include the necessary prior knowledge about these geometric features, we use a phase field higher-order active contour model of directed networks. The model has a large number of unphysical parameters (weights of energy terms), and can favour different geometric structures for different parameter values. To overcome this problem, we perform a stability analysis of a long, straight bar in order to find parameter ranges that favour networks. The resulting constraints necessary to produce stable networks eliminate some parameters, replace others by physical parameters such as network branch width, and place lower and upper bounds on the values of the rest.We validate the theoretical analysis via numerical experiments, and then apply the model to the problem of hydrographic network extraction from multi-spectral VHR satellite images.

2 - Segmentation of networks from VHR remote sensing images using a directed phase field HOAC model. A. El Ghoul and I. H. Jermyn and J. Zerubia. In Proc. ISPRS Technical Commission III Symposium on Photogrammetry Computer Vision and Image Analysis (PCV), Paris, France, September 2010. Keywords : Phase Field, Shape prior, Directed networks, Road network extraction, river extraction, remote sensing. Copyright : ISPRS @INPROCEEDINGS{Elghoul10a, author = {El Ghoul, A. and Jermyn, I. H. and Zerubia, J.}, title = {Segmentation of networks from VHR remote sensing images using a directed phase field HOAC model}, year = {2010}, month = {September}, booktitle = {Proc. ISPRS Technical Commission III Symposium on Photogrammetry Computer Vision and Image Analysis (PCV)}, address = {Paris, France}, pdf = {https://hal.inria.fr/inria-00491017}, keyword = {Phase Field, Shape prior, Directed networks, Road network extraction, river extraction, remote sensing} } Abstract : We propose a new algorithm for network segmentation from VHR remote sensing images. The algorithm performs this task quasi-automatically, that is, with no human intervention except to fix some parameters. The task is made difficult by the amount of prior knowledge about network region geometry needed to perform the task, knowledge that is usually provided by a human being. To include such prior knowledge, we make use of methodological advances in region modelling: a phase field higher-order active contour of directed networks is used as the prior model for region geometry. By adjoining an approximately conserved flow to a phase field model encouraging network shapes (i.e. regions composed of branches meeting at junctions), the model favours network regions in which different branches may have very different widths, but in which width change along a branch is slow; in which branches do not come to an end, hence tending to close gaps in the network; and in which junctions show approximate ‘conservation of width’. We also introduce image models for network and background, which are validated using maximum likelihood segmentation against other possibilities. We then test the full model on VHR optical and multispectral satellite images.

3 - A phase field higher-order active contour model of directed networks. A. El Ghoul and I. H. Jermyn and J. Zerubia. In 2nd IEEE Workshop on Non-Rigid Shape Analysis and Deformable Image Alignment, at ICCV, Kyoto, Japan, September 2009. Keywords : Geometric prior, Shape, Higher-order actif contours, Phase Field, Directed networks. Copyright : ©2009 IEEE. @INPROCEEDINGS{ElGhoul09b, author = {El Ghoul, A. and Jermyn, I. H. and Zerubia, J.}, title = {A phase field higher-order active contour model of directed networks}, year = {2009}, month = {September}, booktitle = {2nd IEEE Workshop on Non-Rigid Shape Analysis and Deformable Image Alignment, at ICCV}, address = {Kyoto, Japan}, url = {https://hal.inria.fr/inria-00409910}, pdf = {http://hal.inria.fr/docs/00/40/99/10/PDF/nordia09aymenelghoul.pdf}, keyword = {Geometric prior, Shape, Higher-order actif contours, Phase Field, Directed networks} } Abstract : The segmentation of directed networks is an important problem in many domains, e.g. medical imaging (vascular networks) and remote sensing (river networks). Directed networks carry a unidirectional flow in each branch, which leads to characteristic geometric properties. In this paper, we present a nonlocal phase field model of directed networks. In addition to a scalar field representing a region by its smoothed characteristic function and interacting nonlocally so as to favour network configurations, the model contains a vector field representing the ‘flow’ through the network branches. The vector field is strongly encouraged to be zero outside, and of unit magnitude inside the region; and to have zero divergence. This prolongs network branches; controls width variation along a branch; and produces asymmetric junctions for which total incoming branch width approximately equals total outgoing branch width. In conjunction with a new interaction function, it also allows a broad range of stable branch widths. We analyse the energy to constrain the parameters, and show geometric experiments confirming the above behaviour. We also show a segmentation result on a synthetic river image.

4 - Inflection point model under phase field higher-order active contours for network extraction from VHR satellite images. A. El Ghoul and I. H. Jermyn and J. Zerubia. In Proc. European Signal Processing Conference (EUSIPCO), Glasgow, Scotland, August 2009. Keywords : Geometric prior, Shape, Higher-order active contour, Phase Field, remote sensing. Copyright : EURASIP @INPROCEEDINGS{ElGhoul09a, author = {El Ghoul, A. and Jermyn, I. H. and Zerubia, J.}, title = {Inflection point model under phase field higher-order active contours for network extraction from VHR satellite images}, year = {2009}, month = {August}, booktitle = {Proc. European Signal Processing Conference (EUSIPCO)}, address = {Glasgow, Scotland}, url = {http://hal.inria.fr/inria-00390446/fr/}, pdf = {http://hal.inria.fr/docs/00/39/04/46/PDF/eusipco09aymenelghoul.pdf}, keyword = {Geometric prior, Shape, Higher-order active contour, Phase Field, remote sensing} } Abstract : The segmentation of networks is important in several imaging domains, and models incorporating prior shape knowledge are often essential for the automatic performance of this task. We incorporate such knowledge via phase fields and higher-order active contours (HOACs). In this paper: we introduce an improved prior model, the phase field HOAC ‘inflection point’ model of a network; we present an improved data term for the segmentation of road networks; we confirm the robustness of the resulting model to choice of gradient descent initialization; and we illustrate these points via road network extraction results on VHR satellite images.

5 - Phase diagram of a long bar under a higher-order active contour energy: application to hydrographic network extraction from VHR satellite images. A. El Ghoul and I. H. Jermyn and J. Zerubia. In International Conference on Pattern Recognition (ICPR), Tampa, Florida, December 2008. Keywords : Phase diagram, Higher-order actif contours, Shape, river extraction. @INPROCEEDINGS{ElGhoul08b, author = {El Ghoul, A. and Jermyn, I. H. and Zerubia, J.}, title = {Phase diagram of a long bar under a higher-order active contour energy: application to hydrographic network extraction from VHR satellite images}, year = {2008}, month = {December}, booktitle = {International Conference on Pattern Recognition (ICPR)}, address = {Tampa, Florida}, url = {https://hal.inria.fr/inria-00316619}, pdf = {http://hal.inria.fr/docs/00/31/66/19/PDF/icpr08aymenelghoul.pdf}, keyword = {Phase diagram, Higher-order actif contours, Shape, river extraction} } Abstract : The segmentation of networks is important in several imaging domains, and models incorporating prior shape knowledge are often essential for the automatic performance of this task. Higher-order active contours provide a way to include such knowledge, but their behaviour can vary significantly with parameter values: e.g. the same energy can model networks or a ‘gas of circles’. In this paper, we present a stability analysis of a HOAC energy leading to the phase diagram of a long bar. The results, which are confirmed by numerical experiments, enable the selection of parameter values for the modelling of network shapes using the energy. We apply the resulting model to the problem of hydrographic network extraction from VHR satellite images.

6 - Diagramme de phase d'une énergie de type contours actifs d'ordre supérieur : le cas d'une barre longue. A. El Ghoul and I. H. Jermyn and J. Zerubia. In 16ème congrès francophone AFRIF-AFIA Reconnaissance des Formes et Intelligence Artificielle (RFIA), Amiens, France, January 2008. Keywords : Diagramme de phase, Contours actifs d'ordre supérieur, Shape, geometric prior, Télédétection. @INPROCEEDINGS{ElGhoul08, author = {El Ghoul, A. and Jermyn, I. H. and Zerubia, J.}, title = {Diagramme de phase d'une énergie de type contours actifs d'ordre supérieur : le cas d'une barre longue}, year = {2008}, month = {January}, booktitle = {16ème congrès francophone AFRIF-AFIA Reconnaissance des Formes et Intelligence Artificielle (RFIA)}, address = {Amiens, France}, url = {https://hal.inria.fr/inria-00319575}, pdf = {http://hal.inria.fr/docs/00/31/95/75/PDF/rfia08aymenelghoul.pdf}, keyword = {Diagramme de phase, Contours actifs d'ordre supérieur, Shape, geometric prior, Télédétection} } Résumé : Dans cet article, nous présentons l’analyse de stabilité du modèle des “contours actifs d’ordre supérieur” (CAOS), pour l’extraction des réseaux routiers présents dans des images de télédétection. Le modèle énergétique des CAOS à minimiser présente des comportements différents en fonction des valeurs des paramètres du modèle. Il s’est avéré que deux structures géométriques sont favorisées par ce modèle : des structures linéiques et circulaires. Nous nous intéressons ici à la détermination du diagramme de phase, qui définit les gammes des valeurs des paramètres du modèle des CAOS, permettant d’obtenir des structures linéiques. Abstract : In this paper, we present a stability analysis of a “higher-order active contour” (HOAC) model for road network extraction from remotely sensed images. The HOAC energy presents several different behaviours depending on the model parameter values. Two types of geometric structure are favoured, namely line networks and circles. In this work, we derive the phase diagram giving the parameter ranges of the HOAC model that allow stable linear structures.

top of the page

display : 10 per page | 20 per page | 50 per page

These pages were generated by