Publications of Josiane Zerubia

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• 59 Articles
• 173 Conference articles
• 64 Technical and Research Reports
• 7 Collection articles or Books chapters
• 3 Books

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173 Conference articles

42 - Phase diagram of a long bar under a higher-order active contour energy: application to hydrographic network extraction from VHR satellite images. A. El Ghoul and I. H. Jermyn and J. Zerubia. In International Conference on Pattern Recognition (ICPR), Tampa, Florida, December 2008. Keywords : Phase diagram, Higher-order actif contours, Shape, river extraction. @INPROCEEDINGS{ElGhoul08b, author = {El Ghoul, A. and Jermyn, I. H. and Zerubia, J.}, title = {Phase diagram of a long bar under a higher-order active contour energy: application to hydrographic network extraction from VHR satellite images}, year = {2008}, month = {December}, booktitle = {International Conference on Pattern Recognition (ICPR)}, address = {Tampa, Florida}, url = {https://hal.inria.fr/inria-00316619}, pdf = {http://hal.inria.fr/docs/00/31/66/19/PDF/icpr08aymenelghoul.pdf}, keyword = {Phase diagram, Higher-order actif contours, Shape, river extraction} } Abstract : The segmentation of networks is important in several imaging domains, and models incorporating prior shape knowledge are often essential for the automatic performance of this task. Higher-order active contours provide a way to include such knowledge, but their behaviour can vary significantly with parameter values: e.g. the same energy can model networks or a ‘gas of circles’. In this paper, we present a stability analysis of a HOAC energy leading to the phase diagram of a long bar. The results, which are confirmed by numerical experiments, enable the selection of parameter values for the modelling of network shapes using the energy. We apply the resulting model to the problem of hydrographic network extraction from VHR satellite images.

43 - An extended phase field higher-order active contour model for networks and its application to road network extraction from VHR satellite images. T. Peng and I. H. Jermyn and V. Prinet and J. Zerubia. In Proc. European Conference on Computer Vision (ECCV), Marseille, France, October 2008. Keywords : Dense urban area, Phase Field, Road network, Variational methods, 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 = {October}, 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, Phase Field, Road network, Variational methods, 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.

44 - Unsupervised One-Class SVM Using a Watershed Algorithm and Hysteresis Thresholding to Detect Burnt Areas. O. Zammit and X. Descombes and J. Zerubia. In Proc. International Conference on Pattern Recognition and Image Analysis (PRIA), Nizhny Novgorod, Russia, September 2008. Keywords : Classification, Segmentation, Support Vector Machines, Burnt areas, Forest fires, Satellite images. 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 = {September}, 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, Burnt areas, Forest fires, Satellite images} }

45 - Combining One-Class Support Vector Machines and hysteresis thresholding: application to burnt area mapping. O. Zammit and X. Descombes and J. Zerubia. In Proc. European Signal Processing Conference (EUSIPCO), Lausanne, Switzerland, August 2008. Note : to appear. Keywords : Classification, Satellite images, Support Vector Machines, Burnt areas, Forest fires, 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 = {August}, booktitle = {Proc. European Signal Processing Conference (EUSIPCO)}, address = {Lausanne, Switzerland}, url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7080254}, keyword = {Classification, Satellite images, Support Vector Machines, Burnt areas, Forest fires, Clustering} }

46 - Unsupervised Hierarchical Image Segmentation based on the TS-MRF model and Fast Mean-Shift Clustering. R. Gaetano and G. Scarpa and G. Poggi and J. Zerubia. In Proc. European Signal Processing Conference (EUSIPCO), Lausanne, Switzerland, August 2008. Keywords : 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 = {August}, 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.

47 - A new computationally efficient stochastic approach for building reconstruction from satellite data. F. Lafarge and M. Durupt and X. Descombes and J. Zerubia and M. Pierrot-Deseilligny. In XXI ISPRS Congress, Part A, Beijing, China, July 2008. Note : Copyright ISPRS Keywords : 3D reconstruction, 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 = {July}, 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 = {3D reconstruction, Building, satellite data, stochastic approach, jump process} }

48 - Indexing of mid-resolution satellite images with structural attributes. A. Bhattacharya and M. Roux and H. Maitre and I. H. Jermyn and X. Descombes and J. Zerubia. In The International Society for Photogrammetry and Remote Sensing, Beijing, China, July 2008. Keywords : 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 = {July}, 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.

49 - Extraction of main and secondary roads in VHR images using a higher-order phase field model. T. Peng and I. H. Jermyn and V. Prinet and J. Zerubia. In Proc. XXI ISPRS Congress, Part A, pages 215-22, Beijing, China, July 2008. Keywords : Road network, Urban areas, Satellite images, Segmentation, Modelling, Variational methods. 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 = {July}, 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 = {Road network, Urban areas, Satellite images, Segmentation, Modelling, Variational methods} } 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.

50 - Building reconstruction from a single DEM. F. Lafarge and X. Descombes and J. Zerubia and M. Pierrot-Deseilligny. In Proc. IEEE Computer Vision and Pattern Recognition (CVPR), Anchorage, Alaska, U.S., June 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 = {June}, 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 = {} }

51 - Blind deconvolution for diffraction-limited fluorescence microscopy. P. Pankajakshan and B. Zhang and L. Blanc-Féraud and Z. Kam and J.C. Olivo-Marin and J. Zerubia. In Proc. IEEE International Symposium on Biomedical Imaging (ISBI), pages 740-743, Paris, France, May 2008. Keywords : Confocal microscopy, 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 = {May}, 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 = {Confocal microscopy, 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.

52 - Automatic 3D modeling of urban scenes from satellite images. F. Lafarge and X. Descombes and J. Zerubia and M. Pierrot-Deseilligny. In Proc. SPACEAPPLI, Toulouse, France, April 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 = {April}, booktitle = {Proc. SPACEAPPLI}, address = {Toulouse, France}, url = {http://www.toulousespaceshow.eu/tss08/spaceappli08/index.htm}, keyword = {} }

53 - AUTOMATIC FLAMINGO DETECTION USING A MULTIPLE BIRTH AND DEATH PROCESS. S. Descamps and X. Descombes and A. Béchet and J. Zerubia. In Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Las Vegas, USA, March 2008. Copyright : copyright IEEE 2008 @INPROCEEDINGS{descamps08, author = {Descamps, S. and Descombes, X. and Béchet, A. and Zerubia, J.}, title = {AUTOMATIC FLAMINGO DETECTION USING A MULTIPLE BIRTH AND DEATH PROCESS}, year = {2008}, month = {March}, booktitle = {Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, address = {Las Vegas, USA}, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/2008_descamps08.pdf}, keyword = {} }

54 - SATELLITE IMAGE RECONSTRUCTION FROM AN IRREGULAR SAMPLING. E. Bughin and L. Blanc-Féraud and J. Zerubia. In Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Las Vegas, USA, March 2008. Keywords : Irregular sampling, Variational methods, Fourier analysis, Satellite imaging. Copyright : @INPROCEEDINGS{Bughin08, author = {Bughin, E. and Blanc-Féraud, L. and Zerubia, J.}, title = {SATELLITE IMAGE RECONSTRUCTION FROM AN IRREGULAR SAMPLING}, year = {2008}, month = {March}, booktitle = {Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, address = {Las Vegas, USA}, url = {http://hal.inria.fr/docs/00/27/89/19/PDF/bughinICASSP08.pdf}, keyword = {Irregular sampling, Variational methods, Fourier analysis, Satellite imaging} }

55 - Mixing Geometric and Radiometric Features for Change Classification. A. Fournier and X. Descombes and J. Zerubia. In Proc. SPIE Symposium on Electronic Imaging, San Jose, USA, January 2008. Keywords : Change detection, directional Statistics, polygonal approximation, Classification. Copyright : Copyright 2008 SPIE and IS&T. This paper was published in the proceedings of IS&T/SPIE 20th Annual Symposium on Electronic Imaging and is made available as an electronic reprint (preprint) 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{fournier_spie08, author = {Fournier, A. and Descombes, X. and Zerubia, J.}, title = {Mixing Geometric and Radiometric Features for Change Classification}, year = {2008}, month = {January}, booktitle = {Proc. SPIE Symposium on Electronic Imaging}, address = {San Jose, USA}, url = {http://hal.inria.fr/inria-00269853/fr/}, keyword = {Change detection, directional Statistics, polygonal approximation, Classification} } Abstract : Most basic change detection algorithms use a pixel-based approach. Whereas such approach is quite well defined for monitoring important area changes (such as urban growth monitoring) in low resolution images, an object based approach seems more relevant when the change detection is specifically aimed toward targets (such as small buildings and vehicles). In this paper, we present an approach that mixes radiometric and geometric features to qualify the changed zones. The goal is to establish bounds (appearance, disappearance, substitution ...) between the detected changes and the underlying objects. We proceed by first clustering the change map (containing each pixel bitemporal radiosity) in different classes using the entropy-kmeans algorithm. Assuming that most man-made objects have a polygonal shape, a polygonal approximation algorithm is then used in order to characterize the resulting zone shapes. Hence allowing us to refine the primary rough classification, by integrating the polygon orientations in the state space. Tests are currently conducted on Quickbird data.

56 - Diagramme de phase d'une énergie de type contours actifs d'ordre supérieur : le cas d'une barre longue. A. El Ghoul and I. H. Jermyn and J. Zerubia. In 16ème congrès francophone AFRIF-AFIA Reconnaissance des Formes et Intelligence Artificielle (RFIA), Amiens, France, January 2008. Keywords : Diagramme de phase, Contours actifs d'ordre supérieur, Shape, geometric prior, Télédétection. @INPROCEEDINGS{ElGhoul08, author = {El Ghoul, A. and Jermyn, I. H. and Zerubia, J.}, title = {Diagramme de phase d'une énergie de type contours actifs d'ordre supérieur : le cas d'une barre longue}, year = {2008}, month = {January}, booktitle = {16ème congrès francophone AFRIF-AFIA Reconnaissance des Formes et Intelligence Artificielle (RFIA)}, address = {Amiens, France}, url = {https://hal.inria.fr/inria-00319575}, pdf = {http://hal.inria.fr/docs/00/31/95/75/PDF/rfia08aymenelghoul.pdf}, keyword = {Diagramme de phase, Contours actifs d'ordre supérieur, Shape, geometric prior, Télédétection} } Résumé : Dans cet article, nous présentons l’analyse de stabilité du modèle des “contours actifs d’ordre supérieur” (CAOS), pour l’extraction des réseaux routiers présents dans des images de télédétection. Le modèle énergétique des CAOS à minimiser présente des comportements différents en fonction des valeurs des paramètres du modèle. Il s’est avéré que deux structures géométriques sont favorisées par ce modèle : des structures linéiques et circulaires. Nous nous intéressons ici à la détermination du diagramme de phase, qui définit les gammes des valeurs des paramètres du modèle des CAOS, permettant d’obtenir des structures linéiques. Abstract : In this paper, we present a stability analysis of a “higher-order active contour” (HOAC) model for road network extraction from remotely sensed images. The HOAC energy presents several different behaviours depending on the model parameter values. Two types of geometric structure are favoured, namely line networks and circles. In this work, we derive the phase diagram giving the parameter ranges of the HOAC model that allow stable linear structures.

57 - Forest Fire Detection based on Gaussian field analysis. F. Lafarge and X. Descombes and J. Zerubia. In Proc. European Signal Processing Conference (EUSIPCO), Poznan, Poland, September 2007. Note : Copyright EURASIP Keywords : Gaussian Field, DT-caracteristic, Forest fires. @INPROCEEDINGS{lafarge_eusipco07, author = {Lafarge, F. and Descombes, X. and Zerubia, J.}, title = {Forest Fire Detection based on Gaussian field analysis}, year = {2007}, month = {September}, booktitle = {Proc. European Signal Processing Conference (EUSIPCO)}, address = {Poznan, Poland}, note = {Copyright EURASIP}, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/2007_lafarge_eusipco07.pdf}, keyword = {Gaussian Field, DT-caracteristic, Forest fires} }

58 - 3D city modeling based on Hidden Markov Model. F. Lafarge and X. Descombes and J. Zerubia and M. Pierrot-Deseilligny. In Proc. IEEE International Conference on Image Processing (ICIP), San Antonio, U.S., September 2007. Note : Copyright IEEE Keywords : 3D reconstruction, Building, Hidden Markov Model. @INPROCEEDINGS{lafarge_icip07, author = {Lafarge, F. and Descombes, X. and Zerubia, J. and Pierrot-Deseilligny, M.}, title = {3D city modeling based on Hidden Markov Model}, year = {2007}, month = {September}, booktitle = {Proc. IEEE International Conference on Image Processing (ICIP)}, address = {San Antonio, U.S.}, note = {Copyright IEEE}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4379207}, keyword = {3D reconstruction, Building, Hidden Markov Model} }

59 - A Phase Field Model Incorporating Generic and Specific Prior Knowledge Applied to Road Network Extraction from VHR Satellite Images. T. Peng and I. H. Jermyn and V. Prinet and J. Zerubia and B. Hu. In Proc. British Machine Vision Conference (BMVC), Warwick, UK, September 2007. Keywords : Road network, Very high resolution, Higher-order, Active contour, Shape, Prior. @INPROCEEDINGS{Peng07a, author = {Peng, T. and Jermyn, I. H. and Prinet, V. and Zerubia, J. and Hu, B.}, title = {A Phase Field Model Incorporating Generic and Specific Prior Knowledge Applied to Road Network Extraction from VHR Satellite Images}, year = {2007}, month = {September}, booktitle = {Proc. British Machine Vision Conference (BMVC)}, address = {Warwick, UK}, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/2007_Peng07a.pdf}, keyword = {Road network, Very high resolution, Higher-order, Active contour, Shape, Prior} } Abstract : We address the problem of updating road maps in dense urban areas by extracting the main road network from a very high resolution (VHR) satellite image. Our model of the region occupied by the road network in the image is innovative. It incorporates three different types of prior geometric knowledge: generic boundary smoothness constraints, equivalent to a standard active contour prior; knowledge of the geometric properties of road networks (i.e. that they occupy regions composed of long, low-curvature segments joined at junctions), equivalent to a higher-order active contour prior; and knowledge of the road network at an earlier date derived from GIS data, similar to other ‘shape priors’ in the literature. In addition, we represent the road network region as a ‘phase field’, which offers a number of important advantages over other region modelling frameworks. All three types of prior knowledge prove important for overcoming the complexity of geometric ‘noise’ in VHR images. Promising results and a comparison with several other techniques demonstrate the effectiveness of our approach.

60 - Apprentissage non supervisé des SVM par un algorithme des K-moyennes entropique pour la détection de zones brûlées. O. Zammit and X. Descombes and J. Zerubia. In Proc. GRETSI Symposium on Signal and Image Processing, Troyes, France, September 2007. Keywords : Satellite images, Forest fires, Burnt areas, Classification, Support Vector Machines, Learning base. @INPROCEEDINGS{zammit_gretsi_07, author = {Zammit, O. and Descombes, X. and Zerubia, J.}, title = {Apprentissage non supervisé des SVM par un algorithme des K-moyennes entropique pour la détection de zones brûlées}, year = {2007}, month = {September}, booktitle = {Proc. GRETSI Symposium on Signal and Image Processing}, address = {Troyes, France}, pdf = {ftp://ftp-sop.inria.fr/ariana/Articles/2007_zammit_gretsi_07.pdf}, keyword = {Satellite images, Forest fires, Burnt areas, Classification, Support Vector Machines, Learning base} }

61 - A Multi-Layer MRF Model for Object-Motion Detection in Unregistered Airborne Image-Pairs. C. Benedek and T. Szirányi and Z. Kato and J. Zerubia. In Proc. IEEE International Conference on Image Processing (ICIP), Vol. 6, pages 141--144, San Antonio, Texas, USA, September 2007. Keywords : Change detection, Aerial images, Camera motion, MRF. Copyright : Copyright IEEE @INPROCEEDINGS{benedek_ICIP07, author = {Benedek, C. and Szirányi, T. and Kato, Z. and Zerubia, J.}, title = {A Multi-Layer MRF Model for Object-Motion Detection in Unregistered Airborne Image-Pairs}, year = {2007}, month = {September}, booktitle = {Proc. IEEE International Conference on Image Processing (ICIP)}, volume = {6}, pages = {141--144}, address = {San Antonio, Texas, USA}, url = {http://ieeexplore.ieee.org/search/srchabstract.jsp?arnumber=4379541&isnumber=4379494&punumber=4378863&k2dockey=4379541@ieeecnfs&query=%28benedek+%3Cin%3E+metadata%29+%3Cand%3E+%284379494+%3Cin%3E+isnumber%29&pos=0}, pdf = {http://web.eee.sztaki.hu/~bcsaba/Publications/Pdf/benedek_icip2007.pdf}, keyword = {Change detection, Aerial images, Camera motion, MRF} } Abstract : In this paper, we give a probabilistic model for automatic change detection on airborne images taken with moving cameras. To ensure robustness, we adopt an unsupervised coarse matching instead of a precise image registration. The challenge of the proposed model is to eliminate the registration errors, noise and the parallax artifacts caused by the static objects having considerable height (buildings, trees, walls etc.) from the difference image. We describe the background membership of a given image point through two different features, and introduce a novel three-layerMarkov Random Field (MRF) model to ensure connected homogenous regions in the segmented image.

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