Publications de 2008

Résultat de la recherche dans la liste des publications :

• 5 Articles
• 4 Thèses de Doctorat et Habilitations
• 20 Articles de conférence
• 4 Rapports de recherche et Rapports techniques
• 2 Articles de collection ou Chapitres de livres

nouvelle recherche | bibtex

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20 Articles de conférence

2 - A Mixed Markov Model for Change Detection in Aerial Photos with Large Time Differences. C. Benedek et T. Szirányi. Dans Proc. International Conference on Pattern Recognition (ICPR), Tampa, USA, décembre 2008. Mots-clés : Aerial images, Change detection, mixed Markov models. @INPROCEEDINGS{benedekICPR08, author = {Benedek, C. and Szirányi, T.}, title = {A Mixed Markov Model for Change Detection in Aerial Photos with Large Time Differences}, year = {2008}, month = {décembre}, booktitle = {Proc. International Conference on Pattern Recognition (ICPR)}, address = {Tampa, USA}, pdf = {http://hal.inria.fr/docs/00/35/91/16/PDF/benedekICPR08.pdf}, keyword = {Aerial images, Change detection, mixed Markov models} } Abstract : In the paper we propose a novel multi-layer Mixed Markov model for detecting relevant changes in registered aerial images taken with significant time differences. The introduced approach combines global intensity statistics with local correlation and contrast features. A global energy optimization process simultaneously ensures optimal local feature selection and smooth, observation-consistent classification. Validation is given on real aerial photos.

3 - A contrast equalization procedure for change detection algorithms: applications to remotely sensed images of urban areas. A. Fournier et P. Weiss et L. Blanc-Féraud et G. Aubert. Dans International Conference on Pattern Recognition (ICPR), Tampa, USA, décembre 2008. Mots-clés : Change detection, Level Lines, remote sensing. 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. @INPROCEEDINGS{l_lines_icpr08, author = {Fournier, A. and Weiss, P. and Blanc-Féraud, L. and Aubert, G.}, title = {A contrast equalization procedure for change detection algorithms: applications to remotely sensed images of urban areas}, year = {2008}, month = {décembre}, booktitle = {International Conference on Pattern Recognition (ICPR)}, address = {Tampa, USA}, url = {http://www.math.univ-toulouse.fr/~weiss/Publis/Conferences/icpr2008.pdf}, pdf = {http://www.math.univ-toulouse.fr/~weiss/Publis/Conferences/icpr2008.pdf}, keyword = {Change detection, Level Lines, remote sensing} }

4 - Texture representation by geometric objects using a jump-diffusion process. F. Lafarge et G. Gimel'farb. Dans Proc. British Machine Vision Conference (BMVC), Leeds, U.K., novembre 2008. @INPROCEEDINGS{lafarge_bmvc08, author = {Lafarge, F. and Gimel'farb, G.}, title = {Texture representation by geometric objects using a jump-diffusion process}, year = {2008}, month = {novembre}, booktitle = {Proc. British Machine Vision Conference (BMVC)}, address = {Leeds, U.K.}, url = {http://www.comp.leeds.ac.uk/bmvc2008/proceedings/papers/86.pdf}, keyword = {} }

5 - An extended phase field higher-order active contour model for networks and its application to road network extraction from VHR satellite images. T. Peng et I. H. Jermyn et V. Prinet et J. Zerubia. Dans Proc. European Conference on Computer Vision (ECCV), Marseille, France, octobre 2008. Mots-clés : Dense urban area, Champ de Phase, Reseaux routiers, Methodes variationnelles, 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 = {octobre}, 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, Champ de Phase, Reseaux routiers, Methodes variationnelles, 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.

6 - 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 = {} }

7 - 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} }

8 - 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} }

9 - 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.

10 - 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} }

11 - 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.

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