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Les Publications
Résultat de la recherche dans la liste des publications :
245 Articles de conférence |
69 - A Geometric Primitive Extraction Process for Remote Sensing Problems..
F. Lafarge et G. Gimel'farb et X. Descombes. Dans Proc. Advanced Concepts for Intelligent Vision Systems, pages 518-529, Juan-les-Pins, France, octobre 2008.
Copyright :
@INPROCEEDINGS{LGF2008,
|
| author |
= |
{Lafarge, F. and Gimel'farb, G. and Descombes, X.}, |
| title |
= |
{A Geometric Primitive Extraction Process for Remote Sensing Problems.}, |
| year |
= |
{2008}, |
| month |
= |
{octobre}, |
| booktitle |
= |
{ACIVS}, |
| pages |
= |
{518-529}, |
| address |
= |
{Juan-les-Pins, France}, |
| pdf |
= |
{http://www.springerlink.com/content/b228321527177226/}, |
| keyword |
= |
{} |
| } |
|
70 - Unsupervised One-Class SVM Using a Watershed Algorithm and Hysteresis Thresholding to Detect Burnt Areas.
O. Zammit et X. Descombes et J. Zerubia. Dans Proc. International Conference on Pattern Recognition and Image Analysis (PRIA), Nizhny Novgorod, Russia, septembre 2008.
Mots-clés : Classification, Segmentation, Support Vector Machines, Zones brûlées, Feux de foret, Imagerie satellitaire.
Copyright :
@INPROCEEDINGS{zammit_pria_08,
|
| author |
= |
{Zammit, O. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Unsupervised One-Class SVM Using a Watershed Algorithm and Hysteresis Thresholding to Detect Burnt Areas}, |
| year |
= |
{2008}, |
| month |
= |
{septembre}, |
| booktitle |
= |
{Proc. International Conference on Pattern Recognition and Image Analysis (PRIA)}, |
| address |
= |
{Nizhny Novgorod, Russia}, |
| pdf |
= |
{http://hal.inria.fr/inria-00316297/fr/}, |
| keyword |
= |
{Classification, Segmentation, Support Vector Machines, Zones brûlées, Feux de foret, Imagerie satellitaire} |
| } |
|
71 - Combining One-Class Support Vector Machines and hysteresis thresholding: application to burnt area mapping.
O. Zammit et X. Descombes et J. Zerubia. Dans Proc. European Signal Processing Conference (EUSIPCO), Lausanne, Switzerland, août 2008.
Note : à paraître.
Mots-clés : Classification, Imagerie satellitaire, Support Vector Machines, Zones brûlées, Feux de foret, Clustering.
Copyright :
@INPROCEEDINGS{zammit_eusipco_08,
|
| author |
= |
{Zammit, O. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Combining One-Class Support Vector Machines and hysteresis thresholding: application to burnt area mapping}, |
| year |
= |
{2008}, |
| month |
= |
{août}, |
| booktitle |
= |
{Proc. European Signal Processing Conference (EUSIPCO)}, |
| address |
= |
{Lausanne, Switzerland}, |
| url |
= |
{http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7080254}, |
| keyword |
= |
{Classification, Imagerie satellitaire, Support Vector Machines, Zones brûlées, Feux de foret, Clustering} |
| } |
|
72 - Unsupervised Hierarchical Image Segmentation based on the TS-MRF model and Fast Mean-Shift Clustering.
R. Gaetano et G. Scarpa et G. Poggi et J. Zerubia. Dans Proc. European Signal Processing Conference (EUSIPCO), Lausanne, Switzerland, août 2008.
Mots-clés : Segmentation, Markov Random Fields, Mean Shift, Land Classification.
@INPROCEEDINGS{Gaetano2008,
|
| author |
= |
{Gaetano, R. and Scarpa, G. and Poggi, G. and Zerubia, J.}, |
| title |
= |
{Unsupervised Hierarchical Image Segmentation based on the TS-MRF model and Fast Mean-Shift Clustering}, |
| year |
= |
{2008}, |
| month |
= |
{août}, |
| booktitle |
= |
{Proc. European Signal Processing Conference (EUSIPCO)}, |
| address |
= |
{Lausanne, Switzerland}, |
| pdf |
= |
{http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7080521}, |
| keyword |
= |
{Segmentation, Markov Random Fields, Mean Shift, Land Classification} |
| } |
Abstract :
Tree-Structured Markov Random Field (TS-MRF) models have been recently proposed to provide a hierarchical multiscale description of images. Based on such a model, the unsupervised image segmentation is carried out by means of a sequence of nested class splits, where each class is modeled as a local binary MRF.
We propose here a new TS-MRF unsupervised segmentation technique which improves upon the original algorithm by selecting a better tree structure and eliminating spurious classes. Such results are obtained by using the Mean-Shift procedure to estimate the number of pdf modes at each node (thus allowing for a non-binary tree), and to obtain a more reliable initial clustering for subsequent MRF optimization. To this end, we devise a new reliable and fast clustering algorithm based on the Mean-Shift technique. Experimental results prove the potential of the proposed method. |
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73 - A new computationally efficient stochastic approach for building reconstruction from satellite data.
F. Lafarge et M. Durupt et X. Descombes et J. Zerubia et M. Pierrot-Deseilligny. Dans XXI ISPRS Congress, Part A, Beijing, China, juillet 2008.
Note : Copyright ISPRS
Mots-clés : Reconstruction en 3D, Building, satellite data, stochastic approach, jump process.
@INPROCEEDINGS{lafarge_isprs08,
|
| author |
= |
{Lafarge, F. and Durupt, M. and Descombes, X. and Zerubia, J. and Pierrot-Deseilligny, M.}, |
| title |
= |
{A new computationally efficient stochastic approach for building reconstruction from satellite data}, |
| year |
= |
{2008}, |
| month |
= |
{juillet}, |
| booktitle |
= |
{XXI ISPRS Congress, Part A}, |
| address |
= |
{Beijing, China}, |
| note |
= |
{Copyright ISPRS}, |
| url |
= |
{http://www.isprs.org/proceedings/XXXVII/congress/3_pdf/40.pdf}, |
| keyword |
= |
{Reconstruction en 3D, Building, satellite data, stochastic approach, jump process} |
| } |
|
74 - Indexing of mid-resolution satellite images with structural attributes.
A. Bhattacharya et M. Roux et H. Maitre et I. H. Jermyn et X. Descombes et J. Zerubia. Dans The International Society for Photogrammetry and Remote Sensing, Beijing, China, juillet 2008.
Mots-clés : Landscape, Segmentation, Features, Extraction, Classification, Modelling.
@INPROCEEDINGS{Bhattacharya08,
|
| author |
= |
{Bhattacharya, A. and Roux, M. and Maitre, H. and Jermyn, I. H. and Descombes, X. and Zerubia, J.}, |
| title |
= |
{Indexing of mid-resolution satellite images with structural attributes}, |
| year |
= |
{2008}, |
| month |
= |
{juillet}, |
| booktitle |
= |
{The International Society for Photogrammetry and Remote Sensing}, |
| address |
= |
{Beijing, China}, |
| pdf |
= |
{http://www-sop.inria.fr/members/Ian.Jermyn/publications/Bhattacharya08isprs.pdf}, |
| keyword |
= |
{Landscape, Segmentation, Features, Extraction, Classification, Modelling} |
| } |
Abstract :
Indexing and retrieval of satellite images relies on the extraction of appropriate information from the data about the entity of interest
(e.g. land cover type) and on the robustness of this extraction to nuisance variables. Entities in an image may be strongly correlated
with each other and can therefore be used to characterize geographical environments on the Earth’s surface.
The properties of road networks vary considerably from one geographical environment to another. The networks pertaining in a
satellite image can therefore be used to classify and retrieve such environments. In the work presented in this paper we have defined
7 such classes. These classes can be categorized as follows: 2 urban classes consisting of “Urban USA” and “Urban Europe”; 3
rural classes consisting of “Villages”, “Mountains” and “Fields”; an “Airports” class and a “Common” class (this can be considered
as a rejection class). These classes were then classified with the aid of geometrical and topological features computed from the road
networks occurring in them. In our work we have used two extraction methods simultaneously on an image to extract the road networks
pertaining in it. A set of 16 network features were computed from one extraction method and were categorized into 6 groups as follows:
6 measures of ‘density’, 4 measures of ‘curviness’, 2 measures of ‘homogeneity’, 1 measure of ‘length’, 2 measures of ‘distribution’
and 1 measure of ‘entropy’.
Due to certain limitations of these extraction methods there was a relative failure of network extraction in certain urban regions con-
taining narrow and dense road structures. This loss of information was circumvented by segmenting the urban regions and computing
a second set of geometrical and topological features from them. A set of 4 urban region features were computed and were categorized
into 3 groups as follows: 2 measures of ‘density’, 1 measure of ‘labels’ and 1 measure of ‘compactness’.
The 500 images (each of size 512x512 pixels) forming our database were selected from SPOT5 scenes with 5m resolution. From each
image a set of geometrical and topological features were computed from the road networks and urban regions. These features were
then used to classify the pre-defined geographical classes. Feature selection was done to avoid the burden of feature dimensionality
and increase the classification performance. A set of 20 features was selected from 36 features by Fisher Linear Discriminant (FLD)
analysis which gave the least classification error with an one-vs-rest linear Support Vector Machine (SVM).
The impact of spatial resolution and size of images on the feature set have been explored in this work. We took a closer look at the effect
of spatial resolution and size of images on the discriminative power of the feature set to classify the images belonging to the pre-defined
geographical classes. Tests were performed with feature selection by FLD and one-vs-rest linear SVM classification on a database with
images of 10m resolution. Another test was performed with feature selection by FLD and one-vs-rest linear SVM classification on a
database with 5m resolution images (each of size 256x256 pixels).
With the above mentioned approaches, we developed a novel method to classify large satellite images acquired by SPOT5 satellite (5m
resolution) with patches of images each of size 512x512 pixels extracted from them. There has been a large amount of work dedicated
to the classification of large satellite images at pixel level rather than considering image patches of different sizes. Classification of
image patches of different sizes from a large satellite image is a novel idea in the sense that the patches considered contain significant
coverage of a particular type of geographical environment.
Road networks and urban region features were computed from these image patches extracted from the large image. A one-vs-rest
Gaussian kernel SVM classification method was used to classify this large image. The classification results show that the image
patches were labeled with the class having the maximum geographical coverage of the area associated in the large image. The large
image was mapped into a “region matrix”, where each element of the matrix corresponds to a geographical class. This is a ‘hard’
classification and no inference can be drawn about the classification confidence.
In certain cases, this produces some anomalies, as a single patch may contain two or more different geographical coverages. In order
to have an estimate of these partial coverages, the output of the SVM was mapped into probabilities. These probability measures were
then studied to have a closer look at the classification accuracies. The results confirm that our method is able to classify a large image
into various geographical classes with a mean error of less than 10%.
Future studies can use operators to detect not only man-made structures like roads and urban areas, but also natural entities like rivers,
forests, etc. In this work we have restricted ourselves to a single resolution, but our methodology can be adapted to consider images
of higher resolutions from QuickBird and the future Pleiade satellite. At a better resolution it may be possible to extract different
structures like buildings, gardens, cross-roads, etc. This in turn will allow us to incorporate more classes to appropriately classify any
geographical environment. At an image resolution of 1m, we may imagine to have sub-classes of an existing class, e.g., classes like
urban Europe and urban USA can de divided into downtown, residential and industrial classes. |
|
75 - Extraction of main and secondary roads in VHR images using a higher-order phase field model.
T. Peng et I. H. Jermyn et V. Prinet et J. Zerubia. Dans Proc. XXI ISPRS Congress, Part A, pages 215-22, Beijing, China, juillet 2008.
Mots-clés : Reseaux routiers, Zones urbaines, Imagerie satellitaire, Segmentation, Modelling, Methodes variationnelles.
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 |
= |
{juillet}, |
| 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 |
= |
{Reseaux routiers, Zones urbaines, Imagerie satellitaire, Segmentation, Modelling, Methodes variationnelles} |
| } |
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. |
|
76 - Building reconstruction from a single DEM.
F. Lafarge et X. Descombes et J. Zerubia et M. Pierrot-Deseilligny. Dans Proc. IEEE Computer Vision and Pattern Recognition (CVPR), Anchorage, Alaska, U.S., juin 2008.
@INPROCEEDINGS{lafarge_cvpr08,
|
| author |
= |
{Lafarge, F. and Descombes, X. and Zerubia, J. and Pierrot-Deseilligny, M.}, |
| title |
= |
{Building reconstruction from a single DEM}, |
| year |
= |
{2008}, |
| month |
= |
{juin}, |
| booktitle |
= |
{Proc. IEEE Computer Vision and Pattern Recognition (CVPR)}, |
| address |
= |
{Anchorage, Alaska, U.S.}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2008_lafarge_cvpr08.pdf}, |
| keyword |
= |
{} |
| } |
|
77 - Blind deconvolution for diffraction-limited fluorescence microscopy.
P. Pankajakshan et B. Zhang et L. Blanc-Féraud et Z. Kam et J.C. Olivo-Marin et J. Zerubia. Dans Proc. IEEE International Symposium on Biomedical Imaging (ISBI), pages 740-743, Paris, France, mai 2008.
Mots-clés : Microscopie confocale, Blind Deconvolution, point spread function, Richardson-Lucy algorithm, total variation regularization.
Copyright : This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.
@INPROCEEDINGS{ppankajakshan08a,
|
| author |
= |
{Pankajakshan, P. and Zhang, B. and Blanc-Féraud, L. and Kam, Z. and Olivo-Marin, J.C. and Zerubia, J.}, |
| title |
= |
{Blind deconvolution for diffraction-limited fluorescence microscopy}, |
| year |
= |
{2008}, |
| month |
= |
{mai}, |
| booktitle |
= |
{Proc. IEEE International Symposium on Biomedical Imaging (ISBI)}, |
| pages |
= |
{740-743}, |
| address |
= |
{Paris, France}, |
| pdf |
= |
{ftp://ftp-sop.inria.fr/ariana/Articles/2008_ppankajakshan08a.pdf}, |
| keyword |
= |
{Microscopie confocale, Blind Deconvolution, point spread function, Richardson-Lucy algorithm, total variation regularization} |
| } |
Abstract :
Optical Sections of biological samples obtained from a fluorescence Confocal Laser Scanning Microscopes (CLSM) are often degraded by out-of-focus blur and photon counting noise. Such physical constraints on the observation are a result of the diffraction-limited nature of the optical system, and the reduced amount of light detected by the photomultiplier respectively. Hence, the image stacks can benefit from postprocessing restoration methods based on deconvolution. The parameters of the acquisition system’s Point Spread Function (PSF) may vary during the course of experimentation, and so they have to be estimated directly from the observation data. We describe here an alternate minimization algorithm for the simultaneous blind estimation of the specimen 3D distribution of fluorescent sources and the PSF. Experimental results on real data show that the algorithm provides very good deconvolution results in comparison to theoretical microscope PSF models. |
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78 - Automatic 3D modeling of urban scenes from satellite images.
F. Lafarge et X. Descombes et J. Zerubia et M. Pierrot-Deseilligny. Dans Proc. SPACEAPPLI, Toulouse, France, avril 2008.
@INPROCEEDINGS{lafarge_spaceappli08,
|
| author |
= |
{Lafarge, F. and Descombes, X. and Zerubia, J. and Pierrot-Deseilligny, M.}, |
| title |
= |
{Automatic 3D modeling of urban scenes from satellite images}, |
| year |
= |
{2008}, |
| month |
= |
{avril}, |
| booktitle |
= |
{Proc. SPACEAPPLI}, |
| address |
= |
{Toulouse, France}, |
| url |
= |
{http://www.toulousespaceshow.eu/tss08/spaceappli08/index.htm}, |
| keyword |
= |
{} |
| } |
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Rapport d'activité 2011
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