Publications of 2010

Result of the query in the list of publications :

• 11 Articles
• 1 PhD Thesis and Habilitation
• 17 Conference articles
• 2 Technical and Research Reports
• 2 Collection articles or Books chapters

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11 Articles

1 - A Marked Point Process for Modeling Lidar Waveforms. C. Mallet and F. Lafarge and M. Roux and U. Soergel and F. Bretar and C. Heipke. IEEE Trans. Image Processing, 19(12): pages 3204-3221, December 2010. Keywords : Clustering algorithms, Image color analysis, Image edge detection, Image segmentation, Monte Carlo Sampling, Object-based stochastic model. @ARTICLE{mallet_tip2010, author = {Mallet, C. and Lafarge, F. and Roux, M. and Soergel, U. and Bretar, F. and Heipke, C.}, title = {A Marked Point Process for Modeling Lidar Waveforms}, year = {2010}, month = {December}, journal = {IEEE Trans. Image Processing}, volume = {19}, number = {12}, pages = {3204-3221}, url = {http://dx.doi.org/10.1109/TIP.2010.2052825}, keyword = {Clustering algorithms, Image color analysis, Image edge detection, Image segmentation, Monte Carlo Sampling, Object-based stochastic model} } Abstract : Lidar waveforms are 1D signals representing a train of echoes caused by reflections at different targets. Modeling these echoes with the appropriate parametric function is useful to retrieve information about the physical characteristics of the targets. This paper presents a new probabilistic model based on a marked point process which reconstructs the echoes from recorded discrete waveforms as a sequence of parametric curves. Such an approach allows to fit each mode of a waveform with the most suitable function and to deal with both, symmetric and asymmetric, echoes. The model takes into account a data term, which measures the coherence between the models and the waveforms, and a regularization term, which introduces prior knowledge on the reconstructed signal. The exploration of the associated configuration space is performed by a Reversible Jump Markov Chain Monte Carlo sampler coupled with simulated annealing. Experiments with different kinds of lidar signals, especially from urban scenes, show the high potential of the proposed approach. To further demonstrate the advantages of the suggested method, actual laser scans are classified and the results are reported.

2 - Geometric Feature Extraction by a Multi-Marked Point Process . F. Lafarge and G. Gimel'farb and X. Descombes. IEEE Trans. Pattern Analysis and Machine Intelligence, 32(9): pages 1597-1609, September 2010. Keywords : Shape extraction, Spatial point process, Stochastic geometry, fast optimization, Texture, remote sensing. @ARTICLE{pami09b_lafarge, author = {Lafarge, F. and Gimel'farb, G. and Descombes, X.}, title = {Geometric Feature Extraction by a Multi-Marked Point Process }, year = {2010}, month = {September}, journal = {IEEE Trans. Pattern Analysis and Machine Intelligence}, volume = {32}, number = {9}, pages = {1597-1609}, url = {http://dx.doi.org/10.1109/TPAMI.2009.152}, keyword = {Shape extraction, Spatial point process, Stochastic geometry, fast optimization, Texture, remote sensing} } Abstract : This paper presents a new stochastic marked point process for describing images in terms of a finite library of geometric objects. Image analysis based on conventional marked point processes has already produced convincing results but at the expense of parameter tuning, computing time, and model specificity. Our more general multimarked point process has simpler parametric setting, yields notably shorter computing times, and can be applied to a variety of applications. Both linear and areal primitives extracted from a library of geometric objects are matched to a given image using a probabilistic Gibbs model, and a Jump-Diffusion process is performed to search for the optimal object configuration. Experiments with remotely sensed images and natural textures show that the proposed approach has good potential. We conclude with a discussion about the insertion of more complex object interactions in the model by studying the compromise between model complexity and efficiency.

3 - A formal Gamma-convergence approach for the detection of points in 2-D biological images. D. Graziani and G. Aubert and L. Blanc-Féraud. SIAM Journal on Imaging Sciences, 3(3): pages 578-594, September 2010. Keywords : points detection, curvature-depending functionals, divergence-measure fields. @ARTICLE{2, author = {Graziani, D. and Aubert, G. and Blanc-Féraud, L.}, title = {A formal Gamma-convergence approach for the detection of points in 2-D biological images}, year = {2010}, month = {September}, journal = {SIAM Journal on Imaging Sciences}, volume = {3}, number = {3}, pages = {578-594}, url = {http://hal.inria.fr/inria-00503152/}, keyword = {points detection, curvature-depending functionals, divergence-measure fields} } Abstract : We propose a new variational model to locate points in 2-dimensional biological images. To this purpose we introduce a suitable functional whose minimizers are given by the points we want to detect. In order to provide numerical experiments we replace this energy with a sequence of a more treatable functionals by means of the notion of Gamma-convergence.

4 - Régularité et parcimonie pour les problèmes inverses en imagerie : algorithmes et comparaisons. M. Carlavan and P. Weiss and L. Blanc-Féraud. Traitement du Signal, 27(2): pages 189-219, September 2010. Keywords : Inverse Problems, Regularization, Total variation, Wavelets. @ARTICLE{TSCarlavan2010, author = {Carlavan, M. and Weiss, P. and Blanc-Féraud, L.}, title = {Régularité et parcimonie pour les problèmes inverses en imagerie : algorithmes et comparaisons}, year = {2010}, month = {September}, journal = {Traitement du Signal}, volume = {27}, number = {2}, pages = {189-219}, url = {http://hal.inria.fr/inria-00503050/fr/}, pdf = {http://www.math.univ-toulouse.fr/~weiss/Publis/TS_Carlavan_Weiss_BlancFeraud_2010.pdf}, keyword = {Inverse Problems, Regularization, Total variation, Wavelets} } Résumé : Dans cet article, nous nous intéressons à la régularisation de problèmes inverses reposant sur des critères l1 . Nous séparons ces critères en deux catégories : ceux qui favorisent la régularisation des signaux (à variation totale bornée par exemple) et ceux qui expriment le fait qu'un signal admet une représentation parcimonieuse dans un dictionnaire. Dans une première partie, nous donnons quelques éléments de comparaisons théoriques et pratiques sur les deux a priori, pour aider le lecteur à choisir l'un ou l'autre en fonction de son problème. Pour cette étude, nous utilisons les transformées communément utilisées telles que la variation totale, les ondelettes redondantes ou les curvelets. Dans une deuxième partie, nous proposons un état des lieux des algorithmes de premier ordre adaptés à la minimisation de ces critères.

5 - Variational approximation for detecting point-like target problems. D. Graziani and G. Aubert. COCV: Esaim Control Optimization and Calculus of Variations DOI: 10.1051/cocv/2010029, August 2010. Keywords : points detection, Biological images, divergence-measure fields. @ARTICLE{COCV2010, author = {Graziani, D. and Aubert, G.}, title = {Variational approximation for detecting point-like target problems}, year = {2010}, month = {August}, journal = {COCV: Esaim Control Optimization and Calculus of Variations DOI: 10.1051/cocv/2010029}, url = {http://dx.doi.org/10.1051/cocv/2010029}, keyword = {points detection, Biological images, divergence-measure fields} } Abstract : The aim of this paper is to provide a rigorous variational formulation for the detection of points in 2-d biological images. To this purpose we introduce a new functional whose minimizers give the points we want to detect. Then we define an approximating sequence of functionals for which we prove the Γ-convergence to the initial one.

6 - Insertion of 3D-primitives in mesh-based representations: Towards compact models preserving the details. F. Lafarge and R. Keriven and M. Brédif. IEEE Trans. Image Processing, 19(7): pages 1683-1694, July 2010. Keywords : 3-D reconstruction, Graph-cut , Shape extraction, urban scenes. @ARTICLE{lafarge_tip2010, author = {Lafarge, F. and Keriven, R. and Brédif, M.}, title = {Insertion of 3D-primitives in mesh-based representations: Towards compact models preserving the details}, year = {2010}, month = {July}, journal = {IEEE Trans. Image Processing}, volume = {19}, number = {7}, pages = {1683-1694}, url = {http://dx.doi.org/10.1109/TIP.2010.2045695}, keyword = {3-D reconstruction, Graph-cut , Shape extraction, urban scenes} } Abstract : We propose an original hybrid modeling process of urban scenes that represents 3-D models as a combination of mesh-based surfaces and geometric 3-D-primitives. Meshes describe details such as ornaments and statues, whereas 3-D-primitives code for regular shapes such as walls and columns. Starting from an 3-D-surface obtained by multiview stereo techniques, these primitives are inserted into the surface after being detected. This strategy allows the introduction of semantic knowledge, the simplification of the modeling, and even correction of errors generated by the acquisition process. We design a hierarchical approach exploring different scales of an observed scene. Each level consists first in segmenting the surface using a multilabel energy model optimized by -expansion and then in fitting 3-D-primitives such as planes, cylinders or tori on the obtained partition where relevant. Experiments on real meshes, depth maps and synthetic surfaces show good potential for the proposed approach.

7 - Extended Phase Field Higher-Order Active Contour Models for Networks. T. Peng and I. H. Jermyn and V. Prinet and J. Zerubia. International Journal of Computer Vision, 88(1): pages 111-128, May 2010. Keywords : Active contour, Phase Field, Shape prior, Parameter analysis, remote sensing, Road network extraction. @ARTICLE{Peng09, author = {Peng, T. and Jermyn, I. H. and Prinet, V. and Zerubia, J.}, title = { Extended Phase Field Higher-Order Active Contour Models for Networks}, year = {2010}, month = {May}, journal = {International Journal of Computer Vision}, volume = {88}, number = {1}, pages = { 111-128}, url = {http://www.springerlink.com/content/d3641g2227316w58/}, keyword = {Active contour, Phase Field, Shape prior, Parameter analysis, remote sensing, Road network extraction} } 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 new phase field higher-order active contour (HOAC) prior models for network regions, and apply them 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 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. We solve this problem by introducing first an additional nonlinear nonlocal HOAC term, and then an additional linear nonlocal HOAC term to improve the computational speed. Both terms allow separate control of branch width and branch curvature, and furnish better prolongation for the same width, but the linear term has several advantages: it is more efficient, and it is able to model multiple widths simultaneously. To cope with the difficulty of parameter selection for these models, we perform a stability analysis of a long bar with a given width, and hence show how to choose the parameters of the energy functions. After adding a likelihood energy, we use both models to extract the road network quasi-automatically from pieces of a QuickBird image, and compare the results to other models in the literature. The state-of-the-art results obtained demonstrate the superiority of our new models, the importance of strong prior knowledge in general, and of the new terms in particular.

8 - Unsupervised line network extraction in remote sensing using a polyline process. C. Lacoste and X. Descombes and J. Zerubia. Pattern Recognition, 43(4): pages 1631-1641, April 2010. Keywords : Marked point process, Line networks, Road network extraction. @ARTICLE{lacoste10, author = {Lacoste, C. and Descombes, X. and Zerubia, J.}, title = {Unsupervised line network extraction in remote sensing using a polyline process}, year = {2010}, month = {April}, journal = {Pattern Recognition}, volume = {43}, number = {4}, pages = {1631-1641}, url = {http://dx.doi.org/10.1016/j.patcog.2009.11.003}, keyword = {Marked point process, Line networks, Road network extraction} } Abstract : Marked point processes provide a rigorous framework to describe a scene by an unordered set of objects. The efficiency of this modeling has been shown on line network extraction with models manipulating interacting segments. In this paper, we extend this previous modeling to polylines composed of an unknown number of segments. Optimization is done via simulated annealing using a Reversible Jump Markov Chain Monte Carlo (RJMCMC) algorithm. We accelerate the convergence of the algorithm by using appropriate proposal kernels. Results on aerial and satellite images show that this new model outperforms the previous one.

9 - Structural approach for building reconstruction from a single DSM. F. Lafarge and X. Descombes and J. Zerubia and M. Pierrot-Deseilligny. IEEE Trans. Pattern Analysis and Machine Intelligence, 32(1): pages 135-147, January 2010. @ARTICLE{lafarge_pami09, author = {Lafarge, F. and Descombes, X. and Zerubia, J. and Pierrot-Deseilligny, M.}, title = {Structural approach for building reconstruction from a single DSM}, year = {2010}, month = {January}, journal = {IEEE Trans. Pattern Analysis and Machine Intelligence}, volume = {32}, number = {1}, pages = {135-147}, url = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2008.281}, keyword = {} } Abstract : We present a new approach for building reconstruction from a single Digital Surface Model (DSM). It treats buildings as an assemblage of simple urban structures extracted from a library of 3D parametric blocks (like a LEGO set). First, the 2D-supports of the urban structures are extracted either interactively or automatically. Then, 3D-blocks are placed on the 2D-supports using a Gibbs model which controls both the block assemblage and the fitting to data. A Bayesian decision finds the optimal configuration of 3D--blocks using a Markov Chain Monte Carlo sampler associated with original proposition kernels. This method has been validated on multiple data set in a wide-resolution interval such as 0.7 m satellite and 0.1 m aerial DSMs, and provides 3D representations on complex buildings and dense urban areas with various levels of detail.

10 - Shape Analysis of Elastic Curves in Euclidean Spaces. S. Joshi and E. Klassen and W. Liu and I. H. Jermyn and A. Srivastava. IEEE Trans. Pattern Analysis and Machine Intelligence, 33(7): pages 1415-1428, 2010. Note : to appear Keywords : shape analysis, elastic deformations, Riemannian elastic metric. @ARTICLE{Joshi2010, author = {Joshi, S. and Klassen, E. and Liu, W. and Jermyn, I. H. and Srivastava, A.}, title = {Shape Analysis of Elastic Curves in Euclidean Spaces}, year = {2010}, journal = {IEEE Trans. Pattern Analysis and Machine Intelligence}, volume = {33}, number = {7}, pages = {1415-1428}, note = {to appear}, pdf = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5601739}, keyword = {shape analysis, elastic deformations, Riemannian elastic metric} }

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