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The Publications
Result of the query in the list of publications :
245 Conference articles |
29 - Tree crown detection in high resolution optical and LiDAR images of tropical forest.
J. Zhou and C. Proisy and X. Descombes and I. Hedhli and N. Barbier and J. Zerubia and J.-P. Gastellu-Etchegorry and P. Couteron. In Proc. SPIE Symposium on Remote Sensing, Toulouse, France, September 2010.
Keywords : Tropical forest, tree detection, Marked point process.
@INPROCEEDINGS{Zhou10,
|
| author |
= |
{Zhou, J. and Proisy, C. and Descombes, X. and Hedhli, I. and Barbier, N. and Zerubia, J. and Gastellu-Etchegorry, J.-P. and Couteron, P.}, |
| title |
= |
{Tree crown detection in high resolution optical and LiDAR images of tropical forest}, |
| year |
= |
{2010}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. SPIE Symposium on Remote Sensing}, |
| address |
= |
{Toulouse, France}, |
| url |
= |
{http://dx.doi.org/10.1117/12.865068}, |
| keyword |
= |
{Tropical forest, tree detection, Marked point process} |
| } |
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30 - Multi-spectral Image Analysis for Skin Pigmentation Classification.
S. Prigent and X. Descombes and D. Zugaj and P. Martel and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Hong-Kong, China, September 2010.
Keywords : skin hyper-pigmentation, Multi-spectral images, Support Vector Machines, Independant Component Analysis, Data reduction.
@INPROCEEDINGS{sp02,
|
| author |
= |
{Prigent, S. and Descombes, X. and Zugaj, D. and Martel, P. and Zerubia, J.}, |
| title |
= |
{Multi-spectral Image Analysis for Skin Pigmentation Classification}, |
| year |
= |
{2010}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. IEEE International Conference on Image Processing (ICIP)}, |
| address |
= |
{Hong-Kong, China}, |
| pdf |
= |
{http://hal.inria.fr/docs/00/49/94/92/PDF/Article_ICIP.pdf}, |
| keyword |
= |
{skin hyper-pigmentation, Multi-spectral images, Support Vector Machines, Independant Component Analysis, Data reduction} |
| } |
Abstract :
In this paper, we compare two different approaches for semi-automatic detection of skin hyper-pigmentation on multi-spectral images. These two methods are support vector machine (SVM) and blind source separation. To apply SVM, a dimension reduction method adapted to data classification is proposed. It allows to improve the quality of SVM classification as well as to have reasonable computation time. For the blind source separation approach we show that, using independent component analysis, it is possible to extract a relevant cartography of skin pigmentation.
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31 - 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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32 - Classification of very high resolution SAR images of urban areas by dictionary-based mixture models, copulas and Markov random fields using textural features.
A. Voisin and G. Moser and V. Krylov and S.B. Serpico and J. Zerubia. In Proc. of SPIE (SPIE Symposium on Remote Sensing 2010), Vol. 7830, Toulouse, France, September 2010.
Keywords : SAR Images, Supervised classification, Urban areas, Textural features, Copulas, Markov Random Fields.
Copyright : SPIE
@INPROCEEDINGS{7830-23,
|
| author |
= |
{Voisin, A. and Moser, G. and Krylov, V. and Serpico, S.B. and Zerubia, J.}, |
| title |
= |
{Classification of very high resolution SAR images of urban areas by dictionary-based mixture models, copulas and Markov random fields using textural features}, |
| year |
= |
{2010}, |
| month |
= |
{September}, |
| booktitle |
= |
{Proc. of SPIE (SPIE Symposium on Remote Sensing 2010)}, |
| volume |
= |
{7830}, |
| address |
= |
{Toulouse, France}, |
| url |
= |
{http://hal.archives-ouvertes.fr/inria-00516333/en}, |
| pdf |
= |
{http://hal.archives-ouvertes.fr/docs/00/51/63/33/PDF/Classification_of_VHR_SAR_SPIE_sept2010_Toulouse_Voisin.pdf}, |
| keyword |
= |
{SAR Images, Supervised classification, Urban areas, Textural features, Copulas, Markov Random Fields} |
| } |
Abstract :
This paper addresses the problem of the classification of very high resolution SAR amplitude images of urban areas. The proposed supervised method combines a finite mixture technique to estimate class-conditional probability density functions, Bayesian classification, and Markov random fields (MRFs). Textural features, such as those extracted by the grey-level co-occurrency method, are also integrated in the technique, as they allow improving the discrimination of urban areas. Copula theory is applied to estimate bivariate joint class-conditional statistics, merging the marginal distributions of both textural and SAR amplitude features. The resulting joint distribution estimates are plugged into a hidden MRF model, endowed with a modified Metropolis dynamics scheme for energy minimization. Experimental results with COSMO-SkyMed images point out the accuracy of the proposed method, also as compared with previous contextual classifiers. |
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33 - Building Detection in a Single Remotely Sensed Image with a Point Process of Rectangles.
C. Benedek and X. Descombes and J. Zerubia. In Proc. International Conference on Pattern Recognition (ICPR), Istanbul, Turkey, August 2010.
Keywords : Marked point process, multiple birth-and-death dynamics, Building extraction.
@INPROCEEDINGS{benedekICPR10,
|
| author |
= |
{Benedek, C. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Building Detection in a Single Remotely Sensed Image with a Point Process of Rectangles}, |
| year |
= |
{2010}, |
| month |
= |
{August}, |
| booktitle |
= |
{Proc. International Conference on Pattern Recognition (ICPR)}, |
| address |
= |
{Istanbul, Turkey}, |
| pdf |
= |
{http://hal.archives-ouvertes.fr/inria-00481019/en/}, |
| keyword |
= |
{Marked point process, multiple birth-and-death dynamics, Building extraction} |
| } |
Abstract :
In this paper we introduce a probabilistic approach of building extraction in remotely sensed images. To cope with data heterogeneity we construct a flexible hierarchical framework which can create various building appearance models from different elementary feature based modules. A global optimization process attempts to find the optimal configuration of buildings, considering simultaneously the observed data, prior knowledge, and interactions between the neighboring building parts. The proposed method is evaluated on various aerial image sets containing more than 500 buildings, and the results are matched against two state-of-the-art techniques. |
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34 - Graph-based Analysis of Textured Images for Hierarchical Segmentation.
R. Gaetano and G. Scarpa and T. Sziranyi. In Proc. British Machine Vision Conference (BMVC), Aberystwyth, UK, August 2010.
@INPROCEEDINGS{Gaetano2010,
|
| author |
= |
{Gaetano, R. and Scarpa, G. and Sziranyi, T.}, |
| title |
= |
{Graph-based Analysis of Textured Images for Hierarchical Segmentation}, |
| year |
= |
{2010}, |
| month |
= |
{August}, |
| booktitle |
= |
{Proc. British Machine Vision Conference (BMVC)}, |
| address |
= |
{Aberystwyth, UK}, |
| url |
= |
{http://hal.archives-ouvertes.fr/inria-00506596}, |
| keyword |
= |
{} |
| } |
Abstract :
The Texture Fragmentation and Reconstruction (TFR) algorithm has beenrecently introduced to address the problem of image segmentationby textural properties, based on a suitable image description toolknown as the Hierarchical Multiple Markov Chain (H-MMC) model. TFRprovides a hierarchical set of nested segmentation maps by firstidentifying the elementary image patterns, and then merging themsequentially to identify complete textures at different scales ofobservation.In this work, we propose a major modification to the TFR by resortingto a graph based description of the image content and a graph clusteringtechnique for the enhancement and extraction of image patterns. Aprocedure based on mathematical morphology will be introduced thatallows for the construction of a color-wise image representationby means of multiple graph structures, along with a simple clusteringtechnique aimed at cutting the graphs and correspondingly segmentgroups of connected components with a similar spatial context.The performance assessment, realized both on synthetic compositionsof real-world textures and images from the remote sensing domain,confirm the effectiveness and potential of the proposed method. |
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35 - Multichannel SAR Image Classification by Finite Mixtures, Copula Theory and Markov Random Fields.
V. Krylov and G. Moser and S.B. Serpico and J. Zerubia. In Proc. of Bayesian Inference and Maximum Entropy Methods in Science and Engineering (MaxEnt 2010), Vol. 1305, pages 319-326, Chamonix, France, July 2010.
Keywords : multichannel SAR, Classification, probability density function estimation, Markov random field, copula.
Copyright : AIP
@INPROCEEDINGS{krylovMaxEnt10,
|
| author |
= |
{Krylov, V. and Moser, G. and Serpico, S.B. and Zerubia, J.}, |
| title |
= |
{Multichannel SAR Image Classification by Finite Mixtures, Copula Theory and Markov Random Fields}, |
| year |
= |
{2010}, |
| month |
= |
{July}, |
| booktitle |
= |
{Proc. of Bayesian Inference and Maximum Entropy Methods in Science and Engineering (MaxEnt 2010)}, |
| volume |
= |
{1305}, |
| pages |
= |
{319-326}, |
| address |
= |
{Chamonix, France}, |
| url |
= |
{http://hal.archives-ouvertes.fr/inria-00495557/en/}, |
| pdf |
= |
{http://hal.archives-ouvertes.fr/docs/00/49/55/57/PDF/krylov_MaxEnt2010.pdf}, |
| keyword |
= |
{multichannel SAR, Classification, probability density function estimation, Markov random field, copula} |
| } |
Abstract :
The last decades have witnessed an intensive development and a significant increase of interest to remote sensing, and, in particular, to synthetic aperture radar (SAR) imagery. In this paper we develop a supervised classification approach for medium and high resolution multichannel SAR amplitude images. The proposed technique combines finite mixture modeling for probability density function estimation, copulas for multivariate distribution modeling and the Markov random field approach to Bayesian image classification. The finite mixture modeling is done via a recently proposed SAR-specific dictionary-based stochastic expectation maximization approach to class-conditional amplitude probability density function estimation, which is applied separately to all the SAR channels. For modeling the class-conditional joint distributions of multichannel data the statistical concept of copulas is employed, and a dictionary-based copula selection method is proposed. Finally, the Markov random field approach enables to take into account the contextual information and to gain robustness against the inherent noise-like phenomenon of SAR known as speckle. The designed method is an extension and a generalization to multichannel SAR of a recently developed single-channel and Dual-pol SAR image classification technique. The accuracy of the developed multichannel SAR classification approach is validated on several multichannel Quad-pol RADARSAT-2 images and compared to benchmark classification techniques. |
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36 - Hybrid Multi-view Reconstruction by Jump-Diffusion.
F. Lafarge and R. Keriven and M. Brédif and H. Vu. In Proc. IEEE Computer Vision and Pattern Recognition (CVPR), San Franscico, U.S., June 2010.
@INPROCEEDINGS{lafarge_cvpr10,
|
| author |
= |
{Lafarge, F. and Keriven, R. and Brédif, M. and Vu, H.}, |
| title |
= |
{Hybrid Multi-view Reconstruction by Jump-Diffusion}, |
| year |
= |
{2010}, |
| month |
= |
{June}, |
| booktitle |
= |
{Proc. IEEE Computer Vision and Pattern Recognition (CVPR)}, |
| address |
= |
{San Franscico, U.S.}, |
| pdf |
= |
{http://certis.enpc.fr/publications/papers/CVPR10a.pdf}, |
| keyword |
= |
{} |
| } |
|
37 - Spectral Analysis and Unsupervised SVM Classification for Skin Hyper-pigmentation Classification.
S. Prigent and X. Descombes and D. Zugaj and J. Zerubia. In Proc. IEEE Workshop on Hyperspectral Image and Signal Processing : Evolution in Remote Sensing (WHISPERS), Reykjavik, Iceland, June 2010.
Keywords : Sectral analysis, Data reduction, Projection pursuit, Support Vector Machines, skin hyper-pigmentation.
@INPROCEEDINGS{sp01,
|
| author |
= |
{Prigent, S. and Descombes, X. and Zugaj, D. and Zerubia, J.}, |
| title |
= |
{Spectral Analysis and Unsupervised SVM Classification for Skin Hyper-pigmentation Classification}, |
| year |
= |
{2010}, |
| month |
= |
{June}, |
| booktitle |
= |
{Proc. IEEE Workshop on Hyperspectral Image and Signal Processing : Evolution in Remote Sensing (WHISPERS)}, |
| address |
= |
{Reykjavik, Iceland}, |
| pdf |
= |
{http://hal.inria.fr/docs/00/49/55/60/PDF/whispers2010_submission_124.pdf}, |
| keyword |
= |
{Sectral analysis, Data reduction, Projection pursuit, Support Vector Machines, skin hyper-pigmentation} |
| } |
Abstract :
Data reduction procedures and classification via support vector machines (SVMs) are often associated with multi or hyperspectral image analysis. In this paper, we propose an automatic method with these two schemes in order to perform a classification of skin hyper-pigmentation on multi-spectral images. We propose a spectral analysis method to partition the spectrum as a tool for data reduction, implemented by projection pursuit. Once the data is reduced, an SVM is used to differentiate the pathological from the healthy areas. As SVM is a supervised classification method, we propose a spatial criterion for spectral analysis in order to perform automatic learning. |
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38 - Hidden fuzzy Markov chain model with K discrete classes.
A. Gamal Eldin and Fabien Salzenstein and Christophe Collet. In Information Sciences Signal Processing and their Applications (ISSPA), May 2010.
Keywords : hidden fuzzy Markov chain, multispectral image segmentation, parameterized joint density.
@INPROCEEDINGS{fuzzy_segmentation10,
|
| author |
= |
{Gamal Eldin, A. and Salzenstein, Fabien and Collet, Christophe}, |
| title |
= |
{Hidden fuzzy Markov chain model with K discrete classes}, |
| year |
= |
{2010}, |
| month |
= |
{May}, |
| booktitle |
= |
{Information Sciences Signal Processing and their Applications (ISSPA)}, |
| url |
= |
{http://hal.inria.fr/hal-00616372}, |
| keyword |
= |
{hidden fuzzy Markov chain, multispectral image segmentation, parameterized joint density} |
| } |
Abstract :
This paper deals with a new unsupervised fuzzy Bayesian segmentation method based on the hidden Markov chain model, in order to separate continuous from discrete components in the hidden data. We present a new F-HMC (fuzzy hidden Markov chain) related to three hard classes, based on a general extension of the previously algorithms proposed. For a given observation, the hidden variable owns a density according to a measure containing Dirac and Lebesgue components. We have performed our approach in the multispectral context. The hyper-parameters are estimated using a Stochastic Expectation Maximization (SEM) algorithm. We present synthetic simulations and also segmentation results related to real multi-band data. |
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