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Publications of Véronique Prinet
Result of the query in the list of publications :
2 Articles |
1 - Extended Phase Field Higher-Order Active Contour Models for Networks.
T. Peng and I. H. Jermyn and V. Prinet and J. Zerubia. International Journal of Computer Vision, 88(1): pages 111-128, May 2010.
Keywords : Active contour, Phase Field, Shape prior, Parameter analysis, remote sensing, Road network extraction.
@ARTICLE{Peng09,
|
| author |
= |
{Peng, T. and Jermyn, I. H. and Prinet, V. and Zerubia, J.}, |
| title |
= |
{ Extended Phase Field Higher-Order Active Contour Models for Networks}, |
| year |
= |
{2010}, |
| month |
= |
{May}, |
| journal |
= |
{International Journal of Computer Vision}, |
| volume |
= |
{88}, |
| number |
= |
{1}, |
| pages |
= |
{ 111-128}, |
| url |
= |
{http://www.springerlink.com/content/d3641g2227316w58/}, |
| keyword |
= |
{Active contour, Phase Field, Shape prior, Parameter analysis, remote sensing, Road network extraction} |
| } |
Abstract :
This paper addresses the segmentation from an image of entities that have the form of a ‘network’, i.e. the region in the image corresponding to the entity is composed of branches joining together at junctions, e.g. road or vascular networks. We present new phase field higher-order active contour (HOAC) prior models for network regions, and apply them to the segmentation of road networks from very high resolution satellite images. This is a hard problem for two reasons. First, the images are complex, with much ‘noise’ in the road region due to cars, road markings, etc., while the background is very varied, containing many features that are locally similar to roads. Second, network regions are complex to model, because they may have arbitrary topology. In particular, we address a limitation of a previous model in which network branch width was constrained to be similar to maximum network branch radius of curvature, thereby providing a poor model of networks with straight narrow branches or highly curved, wide branches. We solve this problem by introducing first an additional nonlinear nonlocal HOAC term, and then an additional linear nonlocal HOAC term to improve the computational speed. Both terms allow separate control of branch width and branch curvature, and furnish better prolongation for the same width, but the linear term has several advantages: it is more efficient, and it is able to model multiple widths simultaneously. To cope with the difficulty of parameter selection for these models, we perform a stability analysis of a long bar with a given width, and hence show how to choose the parameters of the energy functions. After adding a likelihood energy, we use both models to extract the road network quasi-automatically from pieces of a QuickBird image, and compare the results to other models in the literature. The state-of-the-art results obtained demonstrate the superiority of our new models, the importance of strong prior knowledge in general, and of the new terms in particular. |
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2 - Incorporating generic and specific prior knowledge in a multi-scale phase field model for road extraction from VHR images.
T. Peng and I. H. Jermyn and V. Prinet and J. Zerubia. IEEE Trans. Geoscience and Remote Sensing, 1(2): pages 139--146, June 2008.
Keywords : Dense urban areas, Geographic Information System (GIS), Multiscale, Road network, Variational methods, Very high resolution.
Copyright : ©2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
@ARTICLE{Peng08b,
|
| author |
= |
{Peng, T. and Jermyn, I. H. and Prinet, V. and Zerubia, J.}, |
| title |
= |
{Incorporating generic and specific prior knowledge in a multi-scale phase field model for road extraction from VHR images}, |
| year |
= |
{2008}, |
| month |
= |
{June}, |
| journal |
= |
{IEEE Trans. Geoscience and Remote Sensing}, |
| volume |
= |
{1}, |
| number |
= |
{2}, |
| pages |
= |
{139--146}, |
| url |
= |
{http://dx.doi.org/10.1109/JSTARS.2008.922318}, |
| pdf |
= |
{http://www-sop.inria.fr/members/Ian.Jermyn/publications/PengetalTGRS08.pdf}, |
| keyword |
= |
{Dense urban areas, Geographic Information System (GIS), Multiscale, Road network, Variational methods, Very high resolution} |
| } |
Abstract :
This paper addresses the problem of updating digital road maps in dense urban areas by extracting the main road network from very high resolution (VHR) satellite images. Building on the work of Rochery et al. (2005), we represent the road region as a 'phase field'. In order to overcome the difficulties due to the complexity of the information contained in VHR images, we propose a multi-scale statistical data model. It enables the integration of segmentation results from coarse resolution, which furnishes a simplified representation of the data, and fine resolution, which provides accurate details. Moreover, an outdated GIS digital map is introduced into the model, providing specific prior knowledge of the road network. This new term balances the effect of the generic prior knowledge describing the geometric shape of road networks (i.e. elongated and of low-curvature) carried by a 'phase field higher-order active contour' term. Promising results on QuickBird panchromatic images and comparisons with several other methods demonstrate the effectiveness of our approach. |
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5 Conference articles |
1 - Conditional mixed-state model for structural change analysis from very high resolution optical images.
B. Belmudez and V. Prinet and J.F. Yao and P. Bouthemy and X. Descombes. In Proc. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Cape Town, South Africa, July 2009.
Keywords : Change detection, mixed Markov models.
@INPROCEEDINGS{bel09,
|
| author |
= |
{Belmudez, B. and Prinet, V. and Yao, J.F. and Bouthemy, P. and Descombes, X.}, |
| title |
= |
{Conditional mixed-state model for structural change analysis from very high resolution optical images}, |
| year |
= |
{2009}, |
| month |
= |
{July}, |
| booktitle |
= |
{IGARSS}, |
| address |
= |
{Cape Town, South Africa}, |
| url |
= |
{http://hal.archives-ouvertes.fr/inria-00398062/}, |
| keyword |
= |
{Change detection, mixed Markov models} |
| } |
Abstract :
The present work concerns the analysis of dynamic scenes from earth observation images. We are interested in building a map which, on one hand locates places of change, on the other hand, reconstructs a unique visual information of the non-change areas. We show in this paper that such a problem can naturally be takled with conditional mixed-state random field modeling (mixed-state CRF), where the ”mixed state” refers to the symbolic or continous nature of the unknown variable. The maximum a posteriori (MAP) estimation of the CRF is, through the Hammersley-Clifford theorem, turned into an energy minimisation problem. We tested the model on several Quickbird images and illustrate the quality of the results. |
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2 - An extended phase field higher-order active contour model for networks and its application to road network extraction from VHR satellite images.
T. Peng and I. H. Jermyn and V. Prinet and J. Zerubia. In Proc. European Conference on Computer Vision (ECCV), Marseille, France, October 2008.
Keywords : Dense urban area, Phase Field, Road network, Variational methods, Very high resolution.
Copyright :
@INPROCEEDINGS{Peng08c,
|
| author |
= |
{Peng, T. and Jermyn, I. H. and Prinet, V. and Zerubia, J.}, |
| title |
= |
{An extended phase field higher-order active contour model for networks and its application to road network extraction from VHR satellite images}, |
| year |
= |
{2008}, |
| month |
= |
{October}, |
| booktitle |
= |
{Proc. European Conference on Computer Vision (ECCV)}, |
| address |
= |
{Marseille, France}, |
| pdf |
= |
{http://link.springer.com/chapter/10.1007%2F978-3-540-88690-7_38}, |
| keyword |
= |
{Dense urban area, Phase Field, Road network, Variational methods, Very high resolution} |
| } |
Abstract :
This paper addresses the segmentation from an image of entities that have the form of a 'network', i.e. the region in the image corresponding to the entity is composed of branches joining together at junctions, e.g. road or vascular networks. We present a new phase field higher-order active contour (HOAC) prior model for network regions, and apply it to the segmentation of road networks from very high resolution satellite images. This is a hard problem for two reasons. First, the images are complex, with much 'noise' in the road region due to cars, road markings, etc., while the background is very varied, containing many features that are locally similar to roads. Second, network regions are complex to model, because they may have arbitrary topology. In particular, we address a severe limitation of a previous model in which network branch width was constrained to be similar to maximum network branch radius of curvature, thereby providing a poor model of networks with straight narrow branches or highly curved, wide branches. To solve this problem, we propose a new HOAC prior energy term, and reformulate it as a nonlocal phase field energy. We analyse the stability of the new model, and find that in addition to solving the above problem by separating the interactions between points on the same and opposite sides of a network branch, the new model permits the modelling of two widths
simultaneously. The analysis also fixes some of the model parameters in terms of network width(s). After adding a likelihood energy, we use the model to extract the road network quasi-automatically from pieces of a QuickBird image, and compare the results to other models in the literature. The results demonstrate the superiority of the new model, the importance of strong prior knowledge in general, and of the new term in particular. |
|
3 - Extraction of main and secondary roads in VHR images using a higher-order phase field model.
T. Peng and I. H. Jermyn and V. Prinet and J. Zerubia. In Proc. XXI ISPRS Congress, Part A, pages 215-22, Beijing, China, July 2008.
Keywords : Road network, Urban areas, Satellite images, Segmentation, Modelling, Variational methods.
Copyright : ISPRS
@INPROCEEDINGS{Peng08a,
|
| author |
= |
{Peng, T. and Jermyn, I. H. and Prinet, V. and Zerubia, J.}, |
| title |
= |
{Extraction of main and secondary roads in VHR images using a higher-order phase field model}, |
| year |
= |
{2008}, |
| month |
= |
{July}, |
| booktitle |
= |
{Proc. XXI ISPRS Congress, Part A}, |
| pages |
= |
{215-22}, |
| address |
= |
{Beijing, China}, |
| pdf |
= |
{http://www.isprs.org/proceedings/XXXVII/congress/3_pdf/33.pdf}, |
| keyword |
= |
{Road network, Urban areas, Satellite images, Segmentation, Modelling, Variational methods} |
| } |
Abstract :
This paper addresses the issue of extracting main and secondary road networks in dense urban areas from very high resolution (VHR, ~0.61m) satellite images. The difficulty with secondary roads lies in the low discriminative power of the grey-level distributions of road regions and the background, and the greater effect of occlusions and other noise on narrower roads. To tackle this problem, we use a previously developed higher-order active contour (HOAC) phase field model and augment it with an additional non-linear nonlocal term. The additional term allows separate control of road width and road curvature; thus more precise prior knowledge can be incorporated, and better road prolongation can be achieved for the same width. Promising results on QuickBird panchromatic images at reduced resolutions and comparisons with other models demonstrate the role and the efficiency of our new model. |
|
4 - A Phase Field Model Incorporating Generic and Specific Prior Knowledge Applied to Road Network Extraction from VHR Satellite Images.
T. Peng and I. H. Jermyn and V. Prinet and J. Zerubia and B. Hu. In Proc. British Machine Vision Conference (BMVC), Warwick, UK, September 2007.
Keywords : Road network, Very high resolution, Higher-order, Active contour, Shape, Prior.
@INPROCEEDINGS{Peng07a,
|
| author |
= |
{Peng, T. and Jermyn, I. H. and Prinet, V. and Zerubia, J. and Hu, B.}, |
| title |
= |
{A Phase Field Model Incorporating Generic and Specific Prior Knowledge Applied to Road Network Extraction from VHR Satellite Images}, |
| year |
= |
{2007}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. British Machine Vision Conference (BMVC)}, |
| address |
= |
{Warwick, UK}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2007_Peng07a.pdf}, |
| keyword |
= |
{Road network, Very high resolution, Higher-order, Active contour, Shape, Prior} |
| } |
Abstract :
We address the problem of updating road maps in dense urban areas by extracting the main road network from a very high resolution (VHR) satellite image. Our model of the region occupied by the road network in the image is innovative. It incorporates three different types of prior geometric knowledge: generic boundary smoothness constraints, equivalent to a standard active contour prior; knowledge of the geometric properties of road networks (i.e. that they occupy regions composed of long, low-curvature segments joined at junctions), equivalent to a higher-order active contour prior; and knowledge of the road network at an earlier date derived from GIS data, similar to other ‘shape priors’ in the literature. In addition, we represent the road network region as a ‘phase field’, which offers a number of important advantages over other region modelling frameworks. All three types of prior knowledge prove important for overcoming the complexity of geometric ‘noise’ in VHR images. Promising results and a comparison with several other techniques demonstrate the effectiveness of our approach. |
|
5 - Urban road extraction from VHR images using a multiscale image model and a phase field model of network geometry.
T. Peng and I. H. Jermyn and V. Prinet and J. Zerubia. In Proc. Urban, Paris, France, April 2007.
Keywords : Road network, Very high resolution, Multiscale, Higher-order, Active contour, Shape.
@INPROCEEDINGS{Peng07_urban,
|
| author |
= |
{Peng, T. and Jermyn, I. H. and Prinet, V. and Zerubia, J.}, |
| title |
= |
{Urban road extraction from VHR images using a multiscale image model and a phase field model of network geometry}, |
| year |
= |
{2007}, |
| month |
= |
{April}, |
| booktitle |
= |
{Proc. Urban}, |
| address |
= |
{Paris, France}, |
| pdf |
= |
{http://www-sop.inria.fr/members/Ian.Jermyn/publications/Peng07urban.pdf}, |
| keyword |
= |
{Road network, Very high resolution, Multiscale, Higher-order, Active contour, Shape} |
| } |
Abstract :
This paper addresses the problem of automatically
extracting the main road network in a dense urban area from
a very high resolution optical satellite image using a variational
approach. The model energy has two parts: a phase field higherorder
active contour energy that describes our prior knowledge
of road network geometry, i.e. that it is composed of elongated
structures with roughly parallel borders that meet at junctions;
and a multi-scale statistical image model describing the image
we expect to see given a road network. By minimizing the model
energy, an estimate of the road network is obtained. Promising
results on 0.6m QuickBird Panchromatic images are presented,
and future improvements to the models are outlined. |
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