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Reconstructing spatiotemporal trajectories from sparse data / P. Partsinevelos in ISPRS Journal of photogrammetry and remote sensing, vol 60 n° 1 (December 2005 - March 2006)
[article]
Titre : Reconstructing spatiotemporal trajectories from sparse data Type de document : Article/Communication Auteurs : P. Partsinevelos, Auteur ; Peggy Agouris, Auteur ; A. Stefanidis, Auteur Année de publication : 2005 Article en page(s) : pp 3 - 16 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] analyse de groupement
[Termes IGN] carte de Kohonen
[Termes IGN] classification dirigée
[Termes IGN] classification par réseau neuronal
[Termes IGN] données spatiotemporelles
[Termes IGN] modélisation spatio-temporelle
[Termes IGN] objet mobile
[Termes IGN] reconstruction d'itinéraire ou de trajectoire
[Termes IGN] segmentation
[Termes IGN] seuillageRésumé : (Auteur) In motion imagery-based tracking applications, it is common to extract locations of moving objects without any knowledge about the identity of the objects they correspond to. The identification of individual spatiotemporal trajectories from such data sets is far from trivial when these trajectories intersect in space, time, or attributes. In this paper, we present a novel approach for the reconstruction of entangled spatiotemporal trajectories of moving objects captured in motion imagery data sets. We have developed AGENT (Attribute-aided Classification of Entangled Trajectories), a novel framework that comprises classification, clustering, and neural net processes to progressively reconstruct elongated trajectories using as input spatiotemporal coordinates of image patches and corresponding attribute values. AGENT proceeds by first forming brief fragments and then linking them and adding points to them. An initial classification allows us to form brief segments corresponding to distinct objects. These segments are then linked together through clustering to form longer trajectories. Back-propagation neural network classification and geometric/self-organizing map (SOM) analysis refine these trajectories by removing misclassified and redistributing unassigned points. Thus, AGENT integrates some established classification and clustering tools to devise a novel approach that can address the tracking challenges of busy environments. Furthermore, AGENT allows us use spatiotemporal (ST) thresholds to cluster trajectories according to their spatial and temporal extent. In the paper, we present in detail our framework and experimental results that support the application potential of our approach. Numéro de notice : A2006-218 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2005.10.004 En ligne : https://doi.org/10.1016/j.isprsjprs.2005.10.004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27945
in ISPRS Journal of photogrammetry and remote sensing > vol 60 n° 1 (December 2005 - March 2006) . - pp 3 - 16[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 081-06011 SL Revue Centre de documentation Revues en salle Disponible Amalgamation in cartographic generalization using Kohonen's feature nets / M.K. Allouche in International journal of geographical information science IJGIS, vol 19 n° 8 - 9 (september 2005)
[article]
Titre : Amalgamation in cartographic generalization using Kohonen's feature nets Type de document : Article/Communication Auteurs : M.K. Allouche, Auteur ; Bruno Moulin, Auteur Année de publication : 2005 Article en page(s) : pp 899 - 914 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] carte de Kohonen
[Termes IGN] déformation géométrique
[Termes IGN] densité des points
[Termes IGN] données multiéchelles
[Termes IGN] généralisation cartographique automatisée
[Termes IGN] reconnaissance de formes
[Termes IGN] représentation multiple
[Termes IGN] triangulation de Delaunay
[Vedettes matières IGN] GénéralisationRésumé : (Auteur) Empirical observations of the way cartographers deal with generalization problems lead to the hypothesis that they first detect patterns of anomalies in the cartographic data set and then eliminate anomalies by transforming the data. Automatically identifying patterns of anomalies on the map is a difficult task when using GIS functions or traditional algorithmic approaches. Techniques based on the use of neural networks have been widely used in artificial intelligence in order to solve pattern-recognition problems. In this paper, we explore how Kohonen-type neural networks can be used to deal with map generalization applications in which the main problem is to identify high-density regions that include cartographic elements of the same type. We also propose an algorithm to replace cartographic elements located in a region by its surrounding polygon. The use of this type of neural network permitted us to generate different levels of grouping according to the chosen zoom-scale on the map. These levels correspond to a multiple representation of the generalized cartographic elements. As an illustration, we apply our approach to the automatic replacement of a group of houses represented as a set of very close points in the original data set, by a polygon representing the corresponding urban area in the generalized map. Numéro de notice : A2005-406 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/13658810500161211 En ligne : https://doi.org/10.1080/13658810500161211 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27542
in International journal of geographical information science IJGIS > vol 19 n° 8 - 9 (september 2005) . - pp 899 - 914[article]Exemplaires(2)
Code-barres Cote Support Localisation Section Disponibilité 079-05081 RAB Revue Centre de documentation En réserve L003 Disponible 079-05082 RAB Revue Centre de documentation En réserve L003 Disponible Optimization approaches for generalization and data abstraction / Monika Sester in International journal of geographical information science IJGIS, vol 19 n° 8 - 9 (september 2005)
[article]
Titre : Optimization approaches for generalization and data abstraction Type de document : Article/Communication Auteurs : Monika Sester, Auteur Année de publication : 2005 Article en page(s) : pp 871 - 897 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] carte de Kohonen
[Termes IGN] déplacement d'objet géographique
[Termes IGN] données localisées numériques
[Termes IGN] extraction automatique
[Termes IGN] généralisation cartographique automatisée
[Termes IGN] jeu de données localisées
[Termes IGN] méthode des moindres carrés
[Termes IGN] optimisation (mathématiques)
[Termes IGN] représentation des détails topographiques
[Termes IGN] typification
[Vedettes matières IGN] GénéralisationRésumé : (Auteur) The availability of methods for abstracting and generalizing spatial data is vital for understanding and communicating spatial information. Spatial analysis using maps at different scales is a good example of this. Such methods are needed not only for analogue spatial data sets but even more so for digital data. In order to automate the process of generating different levels of detail of a spatial data set, generalization operations are used. The paper first gives an overview on current approaches for the automation of generalization and data abstraction, and then presents solutions for three generalization problems based on optimization techniques. Least-Squares Adjustment is used for displacement and shape simplification (here, building groundplans), and Self-Organizing Maps, a Neural Network technique, is applied for typification, i.e. a density preserving reduction of objects. The methods are validated with several examples and evaluated according to their advantages and disadvantages. Finally, a scenario describes how these methods can be combined to automatically yield a satisfying result for integrating two data sets of different scales. Numéro de notice : A2005-405 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/13658810500161179 En ligne : https://doi.org/10.1080/13658810500161179 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27541
in International journal of geographical information science IJGIS > vol 19 n° 8 - 9 (september 2005) . - pp 871 - 897[article]Exemplaires(2)
Code-barres Cote Support Localisation Section Disponibilité 079-05081 RAB Revue Centre de documentation En réserve L003 Disponible 079-05082 RAB Revue Centre de documentation En réserve L003 Disponible A statistical self-organizing learning system for remote sensing classification / H.M. Chi in IEEE Transactions on geoscience and remote sensing, vol 43 n° 8 (August 2005)
[article]
Titre : A statistical self-organizing learning system for remote sensing classification Type de document : Article/Communication Auteurs : H.M. Chi, Auteur ; O.K. Ersoy, Auteur Année de publication : 2005 Article en page(s) : pp 1890 - 1900 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage automatique
[Termes IGN] carte de Kohonen
[Termes IGN] classification par réseau neuronal
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] image hyperspectrale
[Termes IGN] méthode des moindres carrés
[Termes IGN] noeud
[Termes IGN] système expert
[Termes IGN] transformation non linéaireRésumé : (Auteur) A new learning system called a statistical self-organizing learning system (SSOLS), combining functional-link neural networks, statistical hypothesis testing, and self-organization of a number of enhancement nodes, is introduced for remote sensing applications. Its structure consists of two stages, a mapping stage and a learning stage. The input training vectors are initially mapped to the enhancement vectors in the mapping stage by multiplying with a random matrix, followed by pointwise nonlinear transformations. Starting with only one enhancement node, the enhancement layer incrementally adds an extra node in each iteration. The optimum dimension of the enhancement layer is determined by using an efficient leave-one-out cross-validation method. In this way, the number of enhancement nodes is also learned automatically. A t-test algorithm can also be applied to the mapping stage to mitigate the effect of overfitting and to further reduce the number of enhancement nodes required, resulting in a more compact network. In the learning stage, both the input vectors and the enhancement vectors are fed into a least squares learning module to obtain the estimated output vectors. This is made possible by choosing the output layer linear. In addition, several SSOLSs can be trained independently in parallel to form a consensual SSOLS, whose final output is a linear combination of the outputs of each SSOLS module. The SSOLS is simple, fast to compute, and suitable for remote sensing applications, especially with hyperspectral image data of high dimensionality. Numéro de notice : A2005-393 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2005.851188 En ligne : https://doi.org/10.1109/TGRS.2005.851188 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27529
in IEEE Transactions on geoscience and remote sensing > vol 43 n° 8 (August 2005) . - pp 1890 - 1900[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 065-05081 RAB Revue Centre de documentation En réserve L003 Disponible Assessment of simulated cognitive maps: the influence of prior knowledge from cartographic maps / R.E. Lloyd in Cartography and Geographic Information Science, vol 32 n° 3 (July 2005)
[article]
Titre : Assessment of simulated cognitive maps: the influence of prior knowledge from cartographic maps Type de document : Article/Communication Auteurs : R.E. Lloyd, Auteur Année de publication : 2005 Article en page(s) : pp 161 - 179 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie numérique
[Termes IGN] apprentissage automatique
[Termes IGN] apprentissage dirigé
[Termes IGN] base de connaissances
[Termes IGN] carte cognitive
[Termes IGN] carte de Kohonen
[Termes IGN] congruence
[Termes IGN] représentation cartographique
[Termes IGN] représentation mentale spatiale
[Termes IGN] réseau neuronal artificiel
[Termes IGN] simulationRésumé : (Auteur) Real cognitive maps encoded by humans are difficult to study using experimental methods because they are a product of complex processes whose content and timing, cannot easily be known or controlled. This paper assesses the value of using neural network model simulations for investigating cognitive maps. The study simulated the learning of mapped city locations in South Carolina from reference sites in the three primary regions of the state using Kohonen selforganizing maps. The learning performances of models were considered based on available prior knowledge. Bi-dimensional regression analyses were used to assess the congruity of the simulated cognitive maps with a cartographic map and with sketch maps produced by human subjects. Error analyses indicated differences between central and peripheral reference sites. The cities known by subjects living at a central location were more evenly distributed in space and associated with significantly smaller errors. Models that learned combined state boundary and interstate highway information as prior knowledge or simultaneously with city locations consistently produced the best simulation results. The results indicated simulated cognitive maps could be used effectively to study the acquisition of spatial knowledge. Numéro de notice : A2005-417 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1559/1523040054738963 En ligne : https://doi.org/10.1559/1523040054738963 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27553
in Cartography and Geographic Information Science > vol 32 n° 3 (July 2005) . - pp 161 - 179[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 032-05031 RAB Revue Centre de documentation En réserve L003 Disponible Visualizing demographic trajectories with self-organizing maps / A. Skupin in Geoinformatica, vol 9 n° 2 (June - August 2005)PermalinkDesigning fuzzy rule based classifier using self-organizing feature map for analysis of multispectral satellite images / Nikhil R. Pal in International Journal of Remote Sensing IJRS, vol 26 n° 10 (May 2005)PermalinkMultivariate analysis and geovisualization with an integrated geographic knowledge discovery approach / D. Guo in Cartography and Geographic Information Science, vol 32 n° 2 (April 2005)PermalinkEstimation and monitoring of bare soil/vegetation ratio with SPOT vegetation and HRVIR / Grégoire Mercier in IEEE Transactions on geoscience and remote sensing, vol 43 n° 2 (February 2005)PermalinkUsing maximum likelihood (ML) and maximum a prior probability (MAP) in iterative self-organizing data (Isodata) / Hassan A. Karimi in Geocarto international, vol 19 n° 1 (March - May 2004)PermalinkAgile 2004, 7th Agile Conference on Geographic Information Science, Heraklion (Greece), 29 April - 1 May 2004 / Fred Toppen (2004)PermalinkMise en correspondance stéréo par fenêtres adaptatives en imagerie aérienne haute résolution / Jean-Luc Lotti (1996)Permalink