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Weight-proportional space partitioning using adaptative Voronoi diagrams / R. Reitsma in Geoinformatica, vol 11 n° 3 (September - November 2007)
[article]
Titre : Weight-proportional space partitioning using adaptative Voronoi diagrams Type de document : Article/Communication Auteurs : R. Reitsma, Auteur ; S. Trubin, Auteur ; E. Mortensen, Auteur Année de publication : 2007 Article en page(s) : pp 383 - 405 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] arbre quadratique
[Termes IGN] décomposition d'image
[Termes IGN] diagramme de Voronoï
[Termes IGN] données maillées
[Termes IGN] itération
[Termes IGN] optimisation (mathématiques)
[Termes IGN] partitionnement
[Termes IGN] pondérationRésumé : (Auteur) Traditional application of Voronoi diagrams for space partitioning results in Voronoi regions, each with a specific area determined by the generators’ relative locations and weights. Particularly in the area of information space (re)construction, however, there is a need for inverse solutions; i.e., finding weights that result in regions with predefined area ratios. In this paper, we formulate an adaptive Voronoi solution and propose a raster-based optimization method for finding the associated weight set. The solution consists of a combination of simple, fixed-point iteration with an optional spatial resolution refinement along the regions’ boundaries using quadtree decomposition. We present the corresponding algorithm and its complexity analysis. The method is successfully tested on a series of ideal–typical cases and the interactions between the adaptive technique and boundary resolution refinement are explored and assessed. Numéro de notice : A2007-382 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s10707-006-0006-8 En ligne : https://doi.org/10.1007/s10707-006-0006-8 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28745
in Geoinformatica > vol 11 n° 3 (September - November 2007) . - pp 383 - 405[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 057-07031 RAB Revue Centre de documentation En réserve L003 Disponible Building facade interpretation from uncalibrated wide-baseline image sequences / Helmut Mayer in ISPRS Journal of photogrammetry and remote sensing, vol 61 n° 6 (February 2007)
[article]
Titre : Building facade interpretation from uncalibrated wide-baseline image sequences Type de document : Article/Communication Auteurs : Helmut Mayer, Auteur ; S. Reznik, Auteur Année de publication : 2007 Article en page(s) : pp 371 - 380 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] apprentissage dirigé
[Termes IGN] bâtiment
[Termes IGN] chaîne de Markov
[Termes IGN] façade
[Termes IGN] interprétation automatique
[Termes IGN] mesure géométrique
[Termes IGN] méthode de Monte-Carlo
[Termes IGN] méthode des moindres carrés
[Termes IGN] méthode robuste
[Termes IGN] morphologie mathématique
[Termes IGN] photogrammétrie terrestre
[Termes IGN] reconstruction 3D du bâtiRésumé : (Auteur) We propose an approach for building facade interpretation ranging from uncalibrated wide-baseline image sequences to the extraction of windows. The approach comprises several novel features, such as determination of the facade planes by robust least squares matching, learning of implicit shape models for objects, particularly windows, and the determination of the latter by means of Markov Chain Monte Carlo (MCMC) employing an abstraction hierarchy generated via mathematical morphology. Results for the fully automatic approach show its potential and shortcomings. Copyright ISPRS Numéro de notice : A2007-064 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2006.10.007 En ligne : https://doi.org/10.1016/j.isprsjprs.2006.10.007 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28429
in ISPRS Journal of photogrammetry and remote sensing > vol 61 n° 6 (February 2007) . - pp 371 - 380[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-07021 SL Revue Centre de documentation Revues en salle Disponible Modelling and detection of geospatial objects using texture motifs / S. Bhagavathy in IEEE Transactions on geoscience and remote sensing, vol 44 n° 12 (December 2006)
[article]
Titre : Modelling and detection of geospatial objects using texture motifs Type de document : Article/Communication Auteurs : S. Bhagavathy, Auteur Année de publication : 2006 Article en page(s) : pp 3706 - 3715 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] analyse texturale
[Termes IGN] apprentissage dirigé
[Termes IGN] détection d'objet
[Termes IGN] distribution spatiale
[Termes IGN] image aérienne
[Termes IGN] objet géographique
[Termes IGN] texture d'imageRésumé : (Auteur) We propose the use of texture motifs, or characteristic spatially recurrent patterns, for modeling and detecting geospatial objects. A method is proposed for learning a texture-motif model from object examples and detecting objects based on the learned model. The model is learned in a two-layered framework: the first learns the constituent "texture elements" of the motif and, the second, the spatial distribution of the elements. In the experimental session, we demonstrate the model training and selection methodology for objects given a set of training examples. The utility of such models for detecting the presence or absence of geospatial objects in large aerial image datasets comprising tens of thousands of image tiles is then emphasized. Copyright IEEE Numéro de notice : A2006-561 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2006.881741 En ligne : https://doi.org/10.1109/TGRS.2006.881741 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28284
in IEEE Transactions on geoscience and remote sensing > vol 44 n° 12 (December 2006) . - pp 3706 - 3715[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-06121 RAB Revue Centre de documentation En réserve L003 Disponible A novel transductive SVM for semisupervised classification of remote-sensing images / Lorenzo Bruzzone in IEEE Transactions on geoscience and remote sensing, vol 44 n° 11 Tome 2 (November 2006)
[article]
Titre : A novel transductive SVM for semisupervised classification of remote-sensing images Type de document : Article/Communication Auteurs : Lorenzo Bruzzone, Auteur ; M. Chi, Auteur ; Mattia Marconcini, Auteur Année de publication : 2006 Article en page(s) : pp 3363 - 3373 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] apprentissage dirigé
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] classification semi-dirigée
[Termes IGN] reconnaissance automatiqueRésumé : (Auteur) This paper introduces a semisupervised classification method that exploits both labeled and unlabeled samples for addressing ill-posed problems with support vector machines (SVMs). The method is based on recent developments in statistical learning theory concerning transductive inference and in particular transductive SVMs (TSVMs). TSVMs exploit specific iterative algorithms which gradually search a reliable separating hyperplane (in the kernel space) with a transductive process that incorporates both labeled and unlabeled samples in the training phase. Based on an analysis of the properties of the TSVMs presented in the literature, a novel modified TSVM classifier designed for addressing ill-posed remote-sensing problems is proposed. In particular, the proposed technique: 1) is based on a novel transductive procedure that exploits a weighting strategy for unlabeled patterns, based on a time-dependent criterion; 2) is able to mitigate the effects of suboptimal model selection (which is unavoidable in the presence of small-size training sets); and 3) can address multiclass cases. Experimental results confirm the effectiveness of the proposed method on a set of ill-posed remote-sensing classification problems representing different operative conditions. Copyright IEEE Numéro de notice : A2006-527 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2006.877950 En ligne : https://doi.org/10.1109/TGRS.2006.877950 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28250
in IEEE Transactions on geoscience and remote sensing > vol 44 n° 11 Tome 2 (November 2006) . - pp 3363 - 3373[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-06111B RAB Revue Centre de documentation En réserve L003 Disponible
contenu dans AutoCarto 2006, Vancouver, Washington, Volume 2. Papers / Cartography and geographic information society (2006)
Titre : Methods for improving and updating the knowledge of a generalization system Type de document : Article/Communication Auteurs : Anne Ruas , Auteur ; Aurélie Dyèvre, Auteur ; Cécile Duchêne , Auteur ; Patrick Taillandier , Auteur Editeur : Cartography and Geographic Information Society Année de publication : 2006 Conférence : Auto-Carto 2006, international symposium on cartography and computing 26/06/2006 28/06/2006 Vancouver Colombie britannique - Canada OA Proceedings Importance : 11 p. Format : 21 x 30 cm Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] agent (intelligence artificielle)
[Termes IGN] apprentissage dirigé
[Termes IGN] base de connaissances
[Termes IGN] base de règles
[Termes IGN] bati
[Termes IGN] généralisation cartographique automatisée
[Vedettes matières IGN] GénéralisationRésumé : (Auteur) In this paper we present a method to improve and to update the knowledge used for the automation of the generalization of buildings based on agent paradigm. We propose to store 1/ each building decision, 2/ the reason why the decision was taken (the conflicts) 3/ the result of each algorithm (an improvement or not) and 4/ the successful process chain within all trials. At the end, the processes of all buildings are compared in order to identify the weakness (for example the case where a specific algorithm is often used but never succeeds). When a deficiency is identified we introduce new rules and we study the effect of this change on the efficiency of the process. It can be used either to improve existing knowledge or to introduce new rules associate to the use of a new measure or a new algorithm. The first study has been made on building independent generalization to set the learning methodology. We wish now to apply it on more complex cases such as contextual generalization which still needs knowledge improvement. Numéro de notice : 14347 Affiliation des auteurs : COGIT (1988-2011) Thématique : GEOMATIQUE/INFORMATIQUE Nature : Communication DOI : sans En ligne : https://cartogis.org/docs/proceedings/2006/ruas_dyevre_duchene_taillandier.pdf Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=64342 Documents numériques
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