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Publications of Maria Kulikova
Result of the query in the list of publications :
Article |
1 - 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. |
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PhD Thesis and Habilitation |
1 - Shape recognition for image scene analysis.
M. S. Kulikova. PhD Thesis, Universite de Nice - Sophia-Antipolis, December 2009.
Keywords : tree crown , Classification, Shape, multiple object extraction, Marked point process, Shape prior.
@PHDTHESIS{mkulikova_phd09,
|
| author |
= |
{Kulikova, M. S.}, |
| title |
= |
{Shape recognition for image scene analysis}, |
| year |
= |
{2009}, |
| month |
= |
{December}, |
| school |
= |
{Universite de Nice - Sophia-Antipolis}, |
| url |
= |
{http://tel.archives-ouvertes.fr/docs/00/48/20/19/PDF/phd_mkulikova_2009.pdf}, |
| keyword |
= |
{tree crown , Classification, Shape, multiple object extraction, Marked point process, Shape prior} |
| } |
Résumé :
Cette thèse est composée de deux parties principales. La première partie est dédiée au problème de la classification d’espèces d’arbres en utilisant des descripteurs de forme, en combainison ou non, avec ceux de radiométrie ou de texture. Nous montrons notamment que l’information sur la forme améliore la performance d’un classifieur. Pour ce faire, dans un premier temps, une étude des formes de couronnes d’arbres extraites à partir d’images aériennes, en infrarouge couleur, est e ectuée en utilisant une méthodologie d’analyse de
formes des courbes continues fermées dans un espace de formes, en utilisant la notion de chemin géodésique sous deux métriques dans des espaces appropriés : une métrique non-élastique en utilisant la reprèsentation par la fonction d’angle de la courbe, ainsi qu’une métrique élastique induite par une représentation par la racinecarée appelée q-fonction. Une étape préliminaire nécessaire à la classification est l’extraction des couronnes d’arbre. Dans une seconde partie, nous abordons donc le problème de l’extraction d’objets de forme complexe
arbitraire, à partir d’images de télédétection à très haute résolution. Nous construisons un modèle fondé sur les processus ponctuels marqués. Son originalité tient dans sa prise en compte d’objets de forme arbitraire par rapport aux objets de forme paramétrique, e.g. ellipses ou rectangles. Les formes sélectionnées sont obtenues par la minimisation locale d’une énergie de type contours actifs avec di érents a priori sur la forme incorporé. Les objets de la configuration finale (optimale) sont ensuite sélectionnés parmi les candidats par une dynamique
de naissances et morts multiples, couplée à un schéma de recuit simulé. L’approche est validée sur des images de zones forestières à très haute résolution fournies par l’Université d’Agriculture de Suède. |
Abstract :
This thesis includes two main parts. In the first part we address the problem of tree crown classification into species using shape features, without, or in combination with, those of radiometry and texture, to demonstrate that shape information improves classification performance. For this purpose, we first study the shapes of tree crowns extracted from very high resolution colour aerial infra-red images. For our study, we choose a methodology based on the shape analysis of closed continuous curves on shape spaces using geodesic paths under the bending metric with the angle-function curve representation, and the elastic metric with the square root
q-function representation. A necessary preliminary step to classification is extraction of the tree crowns. In the second part, we address thus the problem of extraction of multiple objects with complex, arbitrary shape from remote sensing images of very high resolution. We develop a model based on marked point processes. Its originality lies in its use of arbitrarily-shaped objects as opposed to parametric shape objects, e.g. ellipses or rectangles. The shapes considered are obtained by local minimisation of an active contour energy with weak and then strong shape prior knowledge included. The objects in the final (optimal) configuration are then selected from amongst these candidates by a multiple birth-and-death dynamics embedded in an annealing scheme. The approach is validated on very high resolution images of forest provided by the Swedish University of Agriculture. |
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3 Conference articles |
1 - Extraction of arbitrarily shaped objects using stochastic multiple birth-and-death dynamics and active contours.
M. S. Kulikova and I. H. Jermyn and X. Descombes and E. Zhizhina and J. Zerubia. In Proc. IS&T/SPIE Electronic Imaging, San Jose, USA, January 2010.
Keywords : Object extraction, Marked point process, Shape prior, Active contour, birth-and-death dynamics.
Copyright : Copyright 2010 by SPIE and IS&T. This paper was published in the proceedings of IS&T/SPIE Electronic Imaging 2010 Conference in San Jose, USA, and is made available as an electronic reprint with permission of SPIE and IS&T. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.
@INPROCEEDINGS{Kulikova10a,
|
| author |
= |
{Kulikova, M. S. and Jermyn, I. H. and Descombes, X. and Zhizhina, E. and Zerubia, J.}, |
| title |
= |
{Extraction of arbitrarily shaped objects using stochastic multiple birth-and-death dynamics and active contours}, |
| year |
= |
{2010}, |
| month |
= |
{January}, |
| booktitle |
= |
{Proc. IS&T/SPIE Electronic Imaging}, |
| address |
= |
{San Jose, USA}, |
| pdf |
= |
{http://hal.archives-ouvertes.fr/docs/00/46/54/72/PDF/Kulikova_SPIE2010.pdf}, |
| keyword |
= |
{Object extraction, Marked point process, Shape prior, Active contour, birth-and-death dynamics} |
| } |
Abstract :
We extend the marked point process models that have been used for object extraction from images to arbitrarily shaped objects, without greatly increasing the computational complexity of sampling and estimation. From an alternative point of view, the approach can be viewed as an extension of the active contour methodology to an a priori unknown number of
objects. Sampling and estimation are based on a stochastic birth-and-death process defined on the configuration space of an arbitrary number of objects, where the objects are defined by the image data and prior information. The performance of the approach is demonstrated via experimental results on synthetic and real data. |
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2 - A marked point process model with strong prior shape information for extraction of multiple, arbitrarily-shaped objects.
M. S. Kulikova and I. H. Jermyn and X. Descombes and E. Zhizhina and J. Zerubia. In Proc. IEEE SITIS, Publ. IEEE Computer Society, Marrakech, Maroc, December 2009.
Keywords : Object extraction, Marked point process, Shape prior, Active contour, multiple birth-and-death dynamics.
@INPROCEEDINGS{Kulikova09a,
|
| author |
= |
{Kulikova, M. S. and Jermyn, I. H. and Descombes, X. and Zhizhina, E. and Zerubia, J.}, |
| title |
= |
{A marked point process model with strong prior shape information for extraction of multiple, arbitrarily-shaped objects}, |
| year |
= |
{2009}, |
| month |
= |
{December}, |
| booktitle |
= |
{Proc. IEEE SITIS}, |
| publisher |
= |
{IEEE Computer Society}, |
| address |
= |
{Marrakech, Maroc}, |
| pdf |
= |
{http://hal.inria.fr/docs/00/43/63/20/PDF/PID1054029.pdf}, |
| keyword |
= |
{Object extraction, Marked point process, Shape prior, Active contour, multiple birth-and-death dynamics} |
| } |
Abstract :
We define a method for incorporating strong prior shape information into a recently extended Markov point process model for the extraction of arbitrarily-shaped objects from images. To estimate the optimal configuration of objects, the process is sampled using a Markov chain based on a stochastic birth-and-death process defined in a space of multiple
objects. The single objects considered are defined by both the image data
and the prior information in a way that controls the computational
complexity of the estimation problem. The method is tested via experiments
on a very high resolution aerial image of a scene composed of tree crowns. |
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3 - Tree Species Classification Using Radiometry, Texture and Shape Based Features.
M. S. Kulikova and M. Mani and A. Srivastava and X. Descombes and J. Zerubia. In Proc. European Signal Processing Conference (EUSIPCO), 2007.
Keywords : shape based features, SVM, tree classification.
@INPROCEEDINGS{Kulikova07,
|
| author |
= |
{Kulikova, M. S. and Mani, M. and Srivastava, A. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Tree Species Classification Using Radiometry, Texture and Shape Based Features}, |
| year |
= |
{2007}, |
| booktitle |
= |
{Proc. European Signal Processing Conference (EUSIPCO)}, |
| pdf |
= |
{http://hal.archives-ouvertes.fr/docs/00/46/55/05/PDF/Kulikova_EUSIPCO2007.pdf}, |
| keyword |
= |
{shape based features, SVM, tree classification} |
| } |
Abstract :
We consider the problem of tree species classification from high resolution aerial images based on radiometry, texture and a shape modeling. We use the notion of shape space proposed by Klassen et al., which provides a shape description invariant to translation, rotation and scaling. The shape features are extracted within a geodesic distance in the shape space. We then perform a classification using a SVM approach. We are able to show that the shape descriptors improve the classification performance relative to a classifier based on radiometric and textural descriptors alone. We obtain these results using high resolution Colour InfraRed (CIR) aerial images provided by the Swedish University of Agricultural Sciences. The image viewpoint is close to the nadir, i.e. the tree crowns are seen from above. |
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