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Road marking extraction using a model&data-driven RJ-MCMC / Alexandre Hervieu in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol II-3 W4 (March 2015)
[article]
Titre : Road marking extraction using a model&data-driven RJ-MCMC Type de document : Article/Communication Auteurs : Alexandre Hervieu , Auteur ; Bahman Soheilian , Auteur ; Mathieu Brédif , Auteur Année de publication : 2015 Conférence : ISPRS 2015, PIA 2015 - HRIGI 2015 Joint ISPRS conference 25/03/2015 27/03/2015 Munich Allemagne ISPRS OA Annals Article en page(s) : pp 47 - 54 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] espace image
[Termes IGN] méthode de Monte-Carlo par chaînes de Markov
[Termes IGN] orthoimage
[Termes IGN] projection orthogonale
[Termes IGN] signalisation routièreMots-clés libres : reversible-jump Markov chain Monte Carlo Résumé : (auteur) We propose an integrated bottom-up/top-down approach to road-marking extraction from image space. It is based on energy minimization using marked point processes. A generic road marking object model enable us to define universal energy functions that handle various types of road-marking objects (dashed-lines, arrows, characters, etc.). A RJ-MCMC sampler coupled with a simulated annealing is applied to find the configuration corresponding to the minimum of the proposed energy. We used input data measurements to guide the sampler process (data driven RJ-MCMC). The approach is enhanced with a model-driven kernel using preprocessed autocorrelation and inter-correlation of road-marking templates, in order to resolve type and transformation ambiguities. The method is generic and can be applied to detect road-markings in any orthogonal view produced from optical sensors or laser scanners from aerial or terrestrial platforms. We show the results an ortho-image computed from ground-based laser scanning. Numéro de notice : A2015-758 Affiliation des auteurs : LASTIG MATIS (2012-2019) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprsannals-II-3-W4-47-2015 Date de publication en ligne : 11/05/2015 En ligne : http://dx.doi.org/10.5194/isprsannals-II-3-W4-47-2015 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78754
in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol II-3 W4 (March 2015) . - pp 47 - 54[article]Documents numériques
en open access
Road marking extractionAdobe Acrobat PDF
Titre : Bayesian essentials with R Type de document : Monographie Auteurs : Jean-Michel Marin, Auteur ; Christian P. Robert, Auteur Editeur : Berlin, Heidelberg, Vienne, New York, ... : Springer Année de publication : 2014 Importance : 296 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-1-4614-8687-9 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques
[Termes IGN] algorithme de Métropolis-Hastings
[Termes IGN] classification bayesienne
[Termes IGN] échantillonnage de Gibbs
[Termes IGN] méthode de Monte-Carlo par chaînes de Markov
[Termes IGN] modèle linéaire
[Termes IGN] problème de Dirichlet
[Termes IGN] R (langage)
[Termes IGN] régression linéaire
[Termes IGN] série temporelle
[Termes IGN] théorème de BayesRésumé : (éditeur) This Bayesian modeling book provides a self-contained entry to computational Bayesian statistics. Focusing on the most standard statistical models and backed up by real datasets and an all-inclusive R (CRAN) package called bayess, the book provides an operational methodology for conducting Bayesian inference, rather than focusing on its theoretical and philosophical justifications. Readers are empowered to participate in the real-life data analysis situations depicted here from the beginning. The stakes are high and the reader determines the outcome. Special attention is paid to the derivation of prior distributions in each case and specific reference solutions are given for each of the models. Similarly, computational details are worked out to lead the reader towards an effective programming of the methods given in the book. In particular, all R codes are discussed with enough detail to make them readily understandable and expandable. This works in conjunction with the bayess package. Bayesian Essentials with R can be used as a textbook at both undergraduate and graduate levels, as exemplified by courses given at Université Paris Dauphine (France), University of Canterbury (New Zealand), and University of British Columbia (Canada). It is particularly useful with students in professional degree programs and scientists to analyze data the Bayesian way. The text will also enhance introductory courses on Bayesian statistics. Prerequisites for the book are an undergraduate background in probability and statistics, if not in Bayesian statistics. A strength of the text is the noteworthy emphasis on the role of models in statistical analysis. Note de contenu : 1- User’s Manual
2- Normal Models
3- Regression and Variable Selection
4- Generalized Linear Models
5- Capture–Recapture Experiments
6- Mixture Models
7- Time Series
8- Image AnalysisNuméro de notice : 25759 Affiliation des auteurs : non IGN Thématique : MATHEMATIQUE Nature : Monographie En ligne : https://link.springer.com/book/10.1007%2F978-1-4614-8687-9#toc Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94954 A stochastic method for the generation of optimized building-layouts respecting urban regulation / Shuang He (oct 2014)
Titre : A stochastic method for the generation of optimized building-layouts respecting urban regulation Type de document : Article/Communication Auteurs : Shuang He , Auteur ; Julien Perret , Auteur ; Mickaël Brasebin , Auteur ; Mathieu Brédif , Auteur Editeur : Berlin, Heidelberg, Vienne, New York, ... : Springer Année de publication : oct 2014 Collection : Advances in geographic information science, ISSN 1867-2434 Conférence : SDH 2014, 16th international IGU Spatial Data Handling symposium, Geospatial theory, processing and applications 06/10/2014 08/10/2014 Toronto Canada Proceedings Springer Importance : pp 265 - 288 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] algorithme du recuit simulé
[Termes IGN] bati
[Termes IGN] méthode de Monte-Carlo par chaînes de Markov
[Termes IGN] objet géographique 3D
[Termes IGN] programmation stochastique
[Termes IGN] urbanismeRésumé : (auteur) In most countries, a project for the development of an urban area has to obey zoning regulations. In France, such zoning regulations are specified in local urban planning schemes (LUPS or PLU in French) defining the right to build at the scale of a parcel. Such rules define, for example, the maximal building height. As the rules are stated in technical documents, they are not easy for non-professionals to comprehend. It is also hard for professionals to assess their impacts. Driven by such issues, we propose to generate 3D building layouts that comply with these rules while optimizing urban indicators (e.g. floor area ratio). A building layout can be seen as a realization of a marked point process (MPP), which is a stochastic model mapping from a probability space to configurations of geometric objects, namely horizontal 3D boxes. Then, the problem of finding an optimized building layout is converted into finding the optimal realization of a MPP of 3D boxes. We solve this optimization problem by trans-dimensional simulated annealing (TDSA), which allows to explore both parameter space and model space in order to find the combination optimizing a given criterion or energy function. A global energy function is defined as the sum of weighted energy terms. Each energy term is able to penalize the building layouts that violate a specific rule or favor the ones according to the optimization task. TDSA generates the optimal building layout by minimizing this global energy using the coupling of a simulated annealing scheme with a Reversible Jump Markov Chain Monte Carlo (RJMCMC) sampler. We studied several common types of the French PLU rules and modeled them into energy terms. A case study is conducted and the results show that our proposed approach is capable of such an optimization task within a short computation time. Numéro de notice : C2014-027 Affiliation des auteurs : LASTIG COGIT (2012-2019) Thématique : GEOMATIQUE/URBANISME Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1007/978-3-319-19950-4_16 Date de publication en ligne : 30/06/2015 En ligne : http:// dx.doi.org/10.1007/978-3-319-19950-4_16 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78466 Documents numériques
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A stochastic method for the generation of optimized building-layoutsAdobe Acrobat PDF Evaluation of bayesian despeckling and texture extraction methods based on Gauss–Markov and auto-binomial gibbs random fields: Application to TerraSAR-X data / D. Espinoza Molina in IEEE Transactions on geoscience and remote sensing, vol 50 n° 5 Tome 2 (May 2012)
[article]
Titre : Evaluation of bayesian despeckling and texture extraction methods based on Gauss–Markov and auto-binomial gibbs random fields: Application to TerraSAR-X data Type de document : Article/Communication Auteurs : D. Espinoza Molina, Auteur ; D. Gleich, Auteur ; M. Dactu, Auteur Année de publication : 2012 Article en page(s) : pp 2001 - 2025 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] analyse comparative
[Termes IGN] analyse texturale
[Termes IGN] champ aléatoire de Markov
[Termes IGN] échantillonnage de Gibbs
[Termes IGN] évaluation
[Termes IGN] filtre de déchatoiement
[Termes IGN] image radar moirée
[Termes IGN] image TerraSAR-X
[Termes IGN] inférence statistique
[Termes IGN] texture d'imageRésumé : (Auteur) Speckle hinders information in synthetic aperture radar (SAR) images and makes automatic information extraction very difficult. The Bayesian approach allows us to perform the despeckling of an image while preserving its texture and structures. This model-based approach relies on a prior model of the scene. This paper presents an evaluation of two despeckling and texture extraction model-based methods using the two levels of Bayesian inference. The first method uses a Gauss-Markov random field as prior, and the second is based on an auto-binomial model (ABM). Both methods calculate a maximum a posteriori and determine the best model using an evidence maximization algorithm. Our evaluation approach assesses the quality of the image by means of the despeckling and texture extraction qualities. The proposed objective measures are used to quantify the despeckling performances of these methods. The accuracy of modeling and characterization of texture were determined using both supervised and unsupervised classifications, and confusion matrices. Real and simulated SAR data were used during the validation procedure. The results show that both methods enhance the image during the despeckling process. The ABM is superior regarding texture extraction and despeckling for real SAR images. Numéro de notice : A2012-190 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2011.2169679 En ligne : https://doi.org/10.1109/TGRS.2011.2169679 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31637
in IEEE Transactions on geoscience and remote sensing > vol 50 n° 5 Tome 2 (May 2012) . - pp 2001 - 2025[article]Réservation
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contenu dans Proceedings, Commission 3, XXII ISPRS Congress, 25 August – 01 September 2012, Melbourne, Australia / M. Shortis (2012)
Titre : LIBRJMCMC: an open-source generic C++ library for stochastic optimization Type de document : Article/Communication Auteurs : Mathieu Brédif , Auteur ; Olivier Tournaire , Auteur Editeur : International Society for Photogrammetry and Remote Sensing ISPRS Année de publication : 2012 Collection : International Archives of Photogrammetry and Remote Sensing, ISSN 0252-8231 num. 39-B3 Conférence : ISPRS 2012, Commission 3, 22th international congress 25/08/2012 01/09/2012 Melbourne Australie OA ISPRS Archives Commission 3 Importance : pp 259 - 264 Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Produits informatiques
[Termes IGN] algorithme du recuit simulé
[Termes IGN] algorithme RJMCMC
[Termes IGN] bibliothèque logicielle
[Termes IGN] code source libre
[Termes IGN] processus ponctuel marqué
[Termes IGN] programmation stochastique
[Termes IGN] semis de pointsRésumé : (auteur) The LIBRJMCMC is an open source C++ library that solves optimization problems using a stochastic framework. The library is primarily intended for but not limited to research purposes in computer vision, photogrammetry and remote sensing, as it has initially been developed in the context of extracting building footprints from digital elevation models using a marked point process of rectangles. It has been designed to be both highly modular and extensible, and have computational times comparable to a code specifically designed for a particular application, thanks to the powerful paradigms of metaprogramming and generic programming. The proposed stochastic optimization is built on the coupling of a stochastic Reversible-Jump Markov Chain Monte Carlo (RJMCMC) sampler and a simulated annealing relaxation. This framework allows, with theoretical guarantees, the optimization of an unrestricted objective function without requiring any initial solution.
The modularity of our library allows the processing of any kind of input data, whether they are 1D signals (e.g. LiDAR or SAR waveforms), 2D images, 3D point clouds... The library user has just to define a few modules describing its domain specific context: the encoding of a configuration (e.g. its object type in a marked point process context), reversible jump kernels (e.g. birth, death, modifications...), the optimized energies (e.g. data and regularization terms) and the probabilized search space given by the reference process. Similar to this extensibility in the application domain, concepts are clearly and orthogonally separated such that it is straightforward to customize the convergence test, the temperature schedule, or to add visitors enabling visual feedback during the optimization. The library offers dedicated modules for marked point processes, allowing the user to optimize a Maximum A Posteriori (MAP) criterion with an image data term energy on a marked point process of rectangles.Numéro de notice : C2012-014 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE/INFORMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/isprsarchives-XXXIX-B3-23-2012 En ligne : http://dx.doi.org/10.5194/isprsarchives-XXXIX-B3-259-2012 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94277 A marked point process for modeling lidar waveforms / Clément Mallet in IEEE Transactions on image processing, vol 19 n° 12 (December 2010)PermalinkPermalinkPermalinkA parametric model for automatic 3D building reconstruction from high resolution satellite images / Florent Lafarge (2005)PermalinkA spectral mixture process conditioned by Gibbs-based partitioning / R.S. Rand in IEEE Transactions on geoscience and remote sensing, vol 39 n° 7 (July 2001)PermalinkA RJMCMC algorithm for object processes in image processing / Xavier Descombes in Monte Carlo Methods and Applications, vol 7 n° 1-2 (2001)PermalinkContribution au développement d'outils pour l'analyse automatique de documents cartographiques / Laurent Lefrère (1993)Permalink