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A smooth curve as a fractal under the third definition / Ding Ma in Cartographica, vol 53 n° 3 (Fall 2018)
[article]
Titre : A smooth curve as a fractal under the third definition Type de document : Article/Communication Auteurs : Ding Ma, Auteur ; Bin Jiang, Auteur Année de publication : 2018 Article en page(s) : pp 203 - 210 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie
[Termes IGN] approche hiérarchique
[Termes IGN] courbe
[Termes IGN] lissage de courbe
[Termes IGN] logarithme
[Termes IGN] objet fractalRésumé : (Auteur) It is commonly believed in the literature that smooth curves, such as circles, are not fractal, and only non-smooth curves, such as coastlines, are fractal. However, this article demonstrates that a smooth curve can be fractal, under a new, relaxed, third definition of fractal – a set or pattern is fractal if the scaling of far more small things than large ones recurs at least twice. The scaling can be rephrased as a hierarchy, consisting of numerous smallest, a very few largest, and some in between the smallest and the largest. The logarithmic spiral, as a smooth curve, is apparently fractal because it bears the self-similarity property, or the scaling of far more small squares than large ones recurs multiple times, or the scaling of far more small bends than large ones recurs multiple times. A half-circle or half-ellipse and the UK coastline (before or after smooth processing) are fractal if the scaling of far more small bends than large ones recurs at least twice. Numéro de notice : A2018-483 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3138/cart.53.3.2017-0032 Date de publication en ligne : 01/10/2018 En ligne : https://doi.org/10.3138/cart.53.3.2017-0032 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91208
in Cartographica > vol 53 n° 3 (Fall 2018) . - pp 203 - 210[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 031-2018031 SL Revue Centre de documentation Revues en salle Disponible Video event recognition and anomaly detection by combining gaussian process and hierarchical dirichlet process models / Michael Ying Yang in Photogrammetric Engineering & Remote Sensing, PERS, vol 84 n° 4 (April 2018)
[article]
Titre : Video event recognition and anomaly detection by combining gaussian process and hierarchical dirichlet process models Type de document : Article/Communication Auteurs : Michael Ying Yang, Auteur ; Wentong Liao, Auteur ; Yanpeng Cao, Auteur ; Bodo Rosenhahn, Auteur Année de publication : 2018 Article en page(s) : pp 203 - 214 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] agent (intelligence artificielle)
[Termes IGN] apprentissage non-dirigé
[Termes IGN] approche hiérarchique
[Termes IGN] image vidéo
[Termes IGN] modèle de Markov
[Termes IGN] modèle orienté agent
[Termes IGN] séquence d'imagesRésumé : (Auteur) In this paper, we present an unsupervised learning framework for analyzing activities and interactions in surveillance videos. In our framework, three levels of video events are connected by Hierarchical Dirichlet Process (HDP) model: low-level visual features, simple atomic activities, and multi-agent interactions. Atomic activities are represented as distribution of low-level features, while complicated interactions are represented as distribution of atomic activities. This learning process is unsupervised. Given a training video sequence, low-level visual features are extracted based on optic flow and then clustered into different atomic activities and video clips are clustered into different interactions. The HDP model automatically decides the number of clusters, i.e., the categories of atomic activities and interactions. Based on the learned atomic activities and interactions, a training dataset is generated to train the Gaussian Process (GP) classifier. Then, the trained GP models work in newly captured video to classify interactions and detect abnormal events in real time. Furthermore, the temporal dependencies between video events learned by HDP-Hidden Markov Models (HMM) are effectively integrated into GP classifier to enhance the accuracy of the classification in newly captured videos. Our framework couples the benefits of the generative model (HDP) with the discriminant model (GP). We provide detailed experiments showing that our framework enjoys favorable performance in video event classification in real-time in a crowded traffic scene. Numéro de notice : A2018-139 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.84.4.203 Date de publication en ligne : 01/04/2018 En ligne : https://doi.org/10.14358/PERS.84.4.203 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89689
in Photogrammetric Engineering & Remote Sensing, PERS > vol 84 n° 4 (April 2018) . - pp 203 - 214[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 105-2018041 RAB Revue Centre de documentation En réserve L003 Disponible Progressive amalgamation of building clusters for map generalization based on scaling subgroups / Xianjin He in ISPRS International journal of geo-information, vol 7 n° 3 (March 2018)
[article]
Titre : Progressive amalgamation of building clusters for map generalization based on scaling subgroups Type de document : Article/Communication Auteurs : Xianjin He, Auteur ; Xinchang Zhang, Auteur ; Jie Yang, Auteur Année de publication : 2018 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] approche hiérarchique
[Termes IGN] généralisation du bâti
[Termes IGN] regroupement de données
[Termes IGN] représentation multiple
[Vedettes matières IGN] GénéralisationRésumé : (Auteur) Map generalization utilizes transformation operations to derive smaller-scale maps from larger-scale maps, and is a key procedure for the modelling and understanding of geographic space. Studies to date have largely applied a fixed tolerance to aggregate clustered buildings into a single object, resulting in the loss of details that meet cartographic constraints and may be of importance for users. This study aims to develop a method that amalgamates clustered buildings gradually without significant modification of geometry, while preserving the map details as much as possible under cartographic constraints. The amalgamation process consists of three key steps. First, individual buildings are grouped into distinct clusters by using the graph-based spatial clustering application with random forest (GSCARF) method. Second, building clusters are decomposed into scaling subgroups according to homogeneity with regard to the mean distance of subgroups. Thus, hierarchies of building clusters can be derived based on scaling subgroups. Finally, an amalgamation operation is progressively performed from the bottom-level subgroups to the top-level subgroups using the maximum distance of each subgroup as the amalgamating tolerance instead of using a fixed tolerance. As a consequence of this step, generalized intermediate scaling results are available, which can form the multi-scale representation of buildings. The experimental results show that the proposed method can generate amalgams with correct details, statistical area balance and orthogonal shape while satisfying cartographic constraints (e.g., minimum distance and minimum area). Numéro de notice : A2018-102 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi7030116 En ligne : https://doi.org/10.3390/ijgi7030116 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89517
in ISPRS International journal of geo-information > vol 7 n° 3 (March 2018)[article]Multifractal analysis for multivariate data with application to remote sensing / Sébastien Combrexelle (2016)
Titre : Multifractal analysis for multivariate data with application to remote sensing Type de document : Thèse/HDR Auteurs : Sébastien Combrexelle, Auteur ; Jean-Yves Tourneret, Directeur de thèse ; Steve Mclaughlin, Directeur de thèse Editeur : Toulouse : Université de Toulouse Année de publication : 2016 Importance : 211 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse en vue de l'obtention du Doctorat de l'Université de Toulouse, Spécialité Signal, Image, Acoustique et OptimisationLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse fractale
[Termes IGN] analyse multivariée
[Termes IGN] approche hiérarchique
[Termes IGN] estimation bayesienne
[Termes IGN] image hyperspectrale
[Termes IGN] modèle statistique
[Termes IGN] télédétection
[Termes IGN] texture d'image
[Termes IGN] transformation en ondelettesRésumé : (auteur) Texture characterization is a central element in many image processing applications. Texture analysis
can be embedded in the mathematical framework of multifractal analysis, enabling the study of the fluctuations in regularity of image intensity and providing practical tools for their assessment, the wavelet coefficients or wavelet leaders. Although successfully applied in various contexts, multifractal analysis suffers at present from two major limitations. First, the accurate estimation of multifractal parameters for image texture remains a challenge, notably for small image sizes. Second, multifractal analysis has so far been limited to the analysis of a single image, while the data available in applications are increasingly multivariate. The main goal of this thesis is to develop practical contributions to overcome these limitations. The first limitation is tackled by introducing a generic statistical model for the logarithm of wavelet leaders, parametrized by multifractal parameters of interest. This statistical model enables us to counterbalance the variability induced by small sample sizes and to
embed the estimation in a Bayesian framework. This yields robust and accurate estimation procedures, effective both for small and large images. The multifractal analysis of multivariate images is then addressed by generalizing this Bayesian framework to hierarchical models able to account for the assumption that multifractal properties evolve smoothly in the dataset. This is achieved via the design of suitable priors relating the dynamical properties of the multifractal parameters of the different components composing the dataset. Different priors are investigated and compared in this thesis by means of numerical simulations conducted on synthetic multivariate multifractal images. This work is further completed by the investigation of the potential benefits of multifractal analysis and the proposed Bayesian methodology for remote sensing via the example of hyperspectral imaging.Note de contenu : Introduction
1- Multifractal analysis
2- Statistical model and univariate Bayesian estimation
3- Bayesian multifractal analysis of
multivariate imagesNuméro de notice : 25811 Affiliation des auteurs : non IGN Thématique : IMAGERIE/MATHEMATIQUE Nature : Thèse étrangère Note de thèse : Thèse de Doctorat : Spécialité : Signal, Image, Acoustique et Optimisation : Toulouse : 2016 Organisme de stage : Institut de Recherche en Informatique de Toulouse (I.R.I.T.) En ligne : http://www.theses.fr/2016INPT0078 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95074 Hierarchical unsupervised change detection in multitemporal hyperspectral images / S. Liu in IEEE Transactions on geoscience and remote sensing, vol 53 n° 1 (January 2015)
[article]
Titre : Hierarchical unsupervised change detection in multitemporal hyperspectral images Type de document : Article/Communication Auteurs : S. Liu, Auteur ; Lorenzo Bruzzone, Auteur ; Francesca Bovolo, Auteur ; Peijun Du, Auteur Année de publication : 2015 Article en page(s) : pp 244 - 260 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse diachronique
[Termes IGN] approche hiérarchique
[Termes IGN] détection de changement
[Termes IGN] image hyperspectrale
[Termes IGN] image multitemporelleRésumé : (Auteur) The new generation of satellite hyperspectral (HS) sensors can acquire very detailed spectral information directly related to land surface materials. Thus, when multitemporal images are considered, they allow us to detect many potential changes in land covers. This paper addresses the change-detection (CD) problem in multitemporal HS remote sensing images, analyzing the complexity of this task. A novel hierarchical CD approach is proposed, which is aimed at identifying all the possible change classes present between the considered images. In greater detail, in order to formalize the CD problem in HS images, an analysis of the concept of “change” is given from the perspective of pixel spectral behaviors. The proposed novel hierarchical scheme is developed by considering spectral change information to identify the change classes having discriminable spectral behaviors. Due to the fact that, in real applications, reference samples are often not available, the proposed approach is designed in an unsupervised way. Experimental results obtained on both simulated and real multitemporal HS images demonstrate the effectiveness of the proposed CD method. Numéro de notice : A2015-031 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2321277 En ligne : https://doi.org/10.1109/TGRS.2014.2321277 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75112
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 1 (January 2015) . - pp 244 - 260[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2015011 RAB Revue Centre de documentation En réserve L003 Disponible PermalinkA hierarchical approach to change detection in very high resolution SAR images for surveillance applications / Francesca Bovolo in IEEE Transactions on geoscience and remote sensing, vol 51 n° 4 Tome 1 (April 2013)PermalinkPermalinkComputational method for the point cluster analysis on networks / K. Sugihara in Geoinformatica, vol 15 n° 1 (January 2011)PermalinkOntologies of geographic information / Helen Couclelis in International journal of geographical information science IJGIS, vol 24 n°11-12 (december 2010)PermalinkVisualization of attributed hierarchical structures in a spatiotemporal context / S. Hadlak in International journal of geographical information science IJGIS, vol 24 n° 10 (october 2010)PermalinkInsertion of 3-D-primitives in mesh-based representations: Towards compact models preserving the details / Florent Lafarge in IEEE Transactions on image processing, vol 19 n° 7 (July 2010)PermalinkComprendre les dynamiques urbaines : l'utilisation de bases de données topographiques et la création de bases de données spatio-temporelles / Julien Perret in Le monde des cartes, n° 202 (décembre 2009)PermalinkStreet hierarchies: a minority of streets account for a majority of traffic flow / Bin Jiang in International journal of geographical information science IJGIS, vol 23 n° 7-8 (july 2009)PermalinkHierarchical lane-oriented 3D road-network model / Q. Zhu in International journal of geographical information science IJGIS, vol 22 n° 4-5 (april 2008)Permalink