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Ian Jermyn
Researcher, INRIA
 Keywords : Variational methods, High Order Active Contours, Active contours, Markov Random Fields, Multiscale Modeling, Wavelets, Parameter Estimation, Texture, Segmentation, Classification, Object extraction, Line networks, Roads, Trees / Forest, Rivers, Phase fields
 Project : Shapes
 Demos : see this author's demos
 Contact :
| Mail : | | IandotJermynatinriadotfr | | Phone : | | (33)4-92-38-76-83 | | Fax : | | (33)4-92-38-76-43 | | Postal adress : | | INRIA Sophia Antipolis
2004, route des Lucioles
06902 Sophia Antipolis Cedex
France | | Webpage : | | visit ! |
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| Abstract :
| My research interests centre around the modelling of regions (incorporating prior geometric information into active contour models using the newly developed 'higher-order active contours'); and the modelling of images (adaptive wavelet packet models that capture the complex dependencies characterizing texture). These models are applied to remote sensing images for image segmentation; extraction of line networks and other entities for which shape information is important; and retrieval from remote sensing image databases. |
Last publications in Ariana Research Group :
A Marked Point Process Model Including Strong Prior Shape Information Applied to Multiple Object Extraction From Images.
M. S. Kulikova and I. H. Jermyn and X. Descombes and E. Zhizhina and J. Zerubia. International Journal of Computer Vision and Image Processing, 1(2): pages 1-12, 2011.
Keywords : Active contour, Marked point process, multiple birth-and-death dynamics, multiple object extraction, Shape prior.
@ARTICLE{kulikova_ijcvip2010,
|
| author |
= |
{Kulikova, M. S. and Jermyn, I. H. and Descombes, X. and Zhizhina, E. and Zerubia, J.}, |
| title |
= |
{A Marked Point Process Model Including Strong Prior Shape Information Applied to Multiple Object Extraction From Images}, |
| year |
= |
{2011}, |
| journal |
= |
{International Journal of Computer Vision and Image Processing}, |
| volume |
= |
{1}, |
| number |
= |
{2}, |
| pages |
= |
{1-12}, |
| url |
= |
{http://hal.archives-ouvertes.fr/hal-00804118}, |
| keyword |
= |
{Active contour, Marked point process, multiple birth-and-death dynamics, multiple object extraction, Shape prior} |
| } |
Abstract :
Object extraction from images is one of the most important tasks in remote sensing image analysis. For accurate extraction from very high resolution (VHR) images, object geometry needs to be taken into account. A method for incorporating strong yet flexible prior shape information into a marked point process model for the extraction of multiple objects of complex shape is presented. To control the computational complexity, the objects considered are defined using the image data and the prior shape information. To estimate the optimal configuration of objects, the process is sampled using a Markov chain based on a stochastic birth-and-death process on the space of multiple objects. The authors present several experimental results on the extraction of tree crowns from VHR aerial images. |
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. |
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. |
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All publications in Ariana Research Group
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2011 Activity report
