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A data model for moving regions of fixed shape in databases / Florian Heinz in International journal of geographical information science IJGIS, vol 32 n° 9-10 (September - October 2018)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 32 n° 9-10 (September - October 2018) . - pp 1737 - 1769 Titre : A data model for moving regions of fixed shape in databases Type de document : Article/Communication Auteurs : Florian Heinz, Auteur ; Ralf Hartmut Güting, Auteur Année de publication : 2018 Article en page(s) : pp 1737 - 1769 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes IGN] iceberg [Termes IGN] modèle conceptuel de données localisées [Termes IGN] modélisation spatio-temporelle [Termes IGN] objet mobile [Termes IGN] rotation d'objet [Termes IGN] système de gestion de base de données Résumé : (Auteur) Moving object databases are designed to store and process spatial and temporal object data. An especially useful moving object type is a moving region, which consists of one or more moving polygons suitable for modeling the spread of forest fires, the movement of clouds, spread of diseases and many other real-world phenomena. Previous implementations usually allow a changing shape of the region during the movement; however, the necessary restrictions on this model result in an inaccurate interpolation of rotating objects. In this paper, we present an alternative approach for moving and rotating regions of fixed shape, called Fixed Moving Regions, which provide a significantly better model for a wide range of applications like modeling the movement of oil tankers, icebergs and other rigid structures. Furthermore, we describe and implement several useful operations on this new object type to enable a database system to solve many real-world problems, as for example collision tests, projections and intersections, much more accurate than with other models. Based on this research, we also implemented a library for easy integration into moving objects database systems, as for example the DBMS Secondo (1) (2) developed at the FernUniversität in Hagen. Numéro de notice : A2018-303 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2018.1458103 Date de publication en ligne : 04/05/2018 En ligne : https://doi.org/10.1080/13658816.2018.1458103 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90446 [article]

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079-2018051	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Tree species classification using within crown localization of waveform LiDAR attributes / Rosmarie Blomley in ISPRS Journal of photogrammetry and remote sensing, vol 133 (November 2017)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 133 (November 2017) . - pp 142 - 156 Titre : Tree species classification using within crown localization of waveform LiDAR attributes Type de document : Article/Communication Auteurs : Rosmarie Blomley, Auteur ; Aarne Hovi, Auteur ; Martin Weinmann, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 142 - 156 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] analyse multiéchelle [Termes IGN] Betula pendula [Termes IGN] betula pubescens [Termes IGN] croissance des arbres [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] espèce végétale [Termes IGN] extraction de traits caractéristiques [Termes IGN] forêt boréale [Termes IGN] Norvège [Termes IGN] Picea abies [Termes IGN] Pinus sylvestris [Termes IGN] rotation d'objet Résumé : (Auteur) Since forest planning is increasingly taking an ecological, diversity-oriented perspective into account, remote sensing technologies are becoming ever more important in assessing existing resources with reduced manual effort. While the light detection and ranging (LiDAR) technology provides a good basis for predictions of tree height and biomass, tree species identification based on this type of data is particularly challenging in structurally heterogeneous forests. In this paper, we analyse existing approaches with respect to the geometrical scale of feature extraction (whole tree, within crown partitions or within laser footprint) and conclude that currently features are always extracted separately from the different scales. Since multi-scale approaches however have proven successful in other applications, we aim to utilize the within-tree-crown distribution of within-footprint signal characteristics as additional features. To do so, a spin image algorithm, originally devised for the extraction of 3D surface features in object recognition, is adapted. This algorithm relies on spinning an image plane around a defined axis, e.g. the tree stem, collecting the number of LiDAR returns or mean values of returns attributes per pixel as respective values. Based on this representation, spin image features are extracted that comprise only those components of highest variability among a given set of library trees. The relative performance and the combined improvement of these spin image features with respect to non-spatial statistical metrics of the waveform (WF) attributes are evaluated for the tree species classification of Scots pine (Pinus sylvestris L.), Norway spruce (Picea abies (L.) Karst.) and Silver/Downy birch (Betula pendula Roth/Betula pubescens Ehrh.) in a boreal forest environment. This evaluation is performed for two WF LiDAR datasets that differ in footprint size, pulse density at ground, laser wavelength and pulse width. Furthermore, we evaluate the robustness of the proposed method with respect to internal parameters and tree size. The results reveal, that the consideration of the crown-internal distribution of within-footprint signal characteristics captured in spin image features improves the classification results in nearly all test cases Numéro de notice : A2017-724 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.08.013 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.08.013 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88409 [article]

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081-2017113	DEP-EXM	Revue	Saint-Mandé	Dépôt en unité	Exclu du prêt

Implementation of a real-time stacking algorithm in a photogrammetric digital camera for UAVs / Ahmad Audi (2017)  

Public Titre : Implementation of a real-time stacking algorithm in a photogrammetric digital camera for UAVs Type de document : Article/Communication Auteurs : Ahmad Audi , Auteur ; Marc Pierrot-Deseilligny , Auteur ; Christophe Meynard , Auteur ; Christian Thom , Auteur Editeur : International Society for Photogrammetry and Remote Sensing ISPRS Année de publication : 2017 Collection : International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, ISSN 1682-1750 num. 42-2/W6 Conférence : ISPRS 2017, International Conference on Unmanned Aerial Vehicles in Geomatics 04/09/2017 07/09/2017 Bonn Allemagne ISPRS OA Archives Importance : pp 13 - 19 Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Acquisition d'image(s) et de donnée(s) [Termes IGN] chambre métrique [Termes IGN] drone [Termes IGN] flou [Termes IGN] GPS-INS [Termes IGN] implémentation (informatique) [Termes IGN] points homologues [Termes IGN] rééchantillonnage [Termes IGN] rotation d'objet Résumé : (auteur) In the recent years, unmanned aerial vehicles (UAVs) have become an interesting tool in aerial photography and photogrammetry activities. In this context, some applications (like cloudy sky surveys, narrow-spectral imagery and night-vision imagery) need a longexposure time where one of the main problems is the motion blur caused by the erratic camera movements during image acquisition. This paper describes an automatic real-time stacking algorithm which produces a high photogrammetric quality final composite image with an equivalent long-exposure time using several images acquired with short-exposure times. Our method is inspired by feature-based image registration technique. The algorithm is implemented on the light-weight IGN camera, which has an IMU sensor and a SoC/FPGA. To obtain the correct parameters for the resampling of images, the presented method accurately estimates the geometrical relation between the first and the Nth image, taking into account the internal parameters and the distortion of the camera. Features are detected in the first image by the FAST detector, than homologous points on other images are obtained by template matching aided by the IMU sensors. The SoC/FPGA in the camera is used to speed up time-consuming parts of the algorithm such as features detection and images resampling in order to achieve a real-time performance as we want to write only the resulting final image to save bandwidth on the storage device. The paper includes a detailed description of the implemented algorithm, resource usage summary, resulting processing time, resulting images, as well as block diagrams of the described architecture. The resulting stacked image obtained on real surveys doesn’t seem visually impaired. Timing results demonstrate that our algorithm can be used in real-time since its processing time is less than the writing time of an image in the storage device. An interesting by-product of this algorithm is the 3D rotation estimated by a photogrammetric method between poses, which can be used to recalibrate in real-time the gyrometers of the IMU. Numéro de notice : C2017-037 Affiliation des auteurs : LASTIG LOEMI (2012-2019) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/isprs-archives-XLII-2-W6-13-2017 Date de publication en ligne : 23/08/2017 En ligne : https://doi.org/10.5194/isprs-archives-XLII-2-W6-13-2017 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91886

Photogrammetric computer vision / Wolfgang Förstner (2016)

Public Titre : Photogrammetric computer vision : statistics, geometry, orientation and reconstruction Type de document : Guide/Manuel Auteurs : Wolfgang Förstner, Auteur ; Bernhard P. Wrobel, Auteur Editeur : Springer Nature Année de publication : 2016 Collection : Geometry and computing, ISSN 1866-6795 num. 11 Importance : 816 p. Format : 21 x 28 cm ISBN/ISSN/EAN : 978-3-319-11549-8 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique [Termes IGN] aérotriangulation numérique [Termes IGN] compensation par faisceaux [Termes IGN] couple stéréoscopique [Termes IGN] données maillées [Termes IGN] données vectorielles [Termes IGN] estimation statistique [Termes IGN] géométrie [Termes IGN] géométrie projective [Termes IGN] image 2D [Termes IGN] image 3D [Termes IGN] incertitude géométrique [Termes IGN] ligne (géométrie) [Termes IGN] modèle de Gauss-Markov [Termes IGN] modèle géométrique de prise de vue [Termes IGN] plan (géométrie) [Termes IGN] point [Termes IGN] reconstruction 3D [Termes IGN] reconstruction d'objet [Termes IGN] rotation d'objet [Termes IGN] semis de points [Termes IGN] transformation géométrique [Termes IGN] variable aléatoire [Termes IGN] vision par ordinateur [Termes IGN] visualisation 3D Index. décimale : 33.30 Photogrammétrie numérique Résumé : (Editeur) This textbook offers a statistical view on the geometry of multiple view analysis, required for camera calibration and orientation and for geometric scene reconstruction based on geometric image features. The authors have backgrounds in geodesy and also long experience with development and research in computer vision, and this is the first book to present a joint approach from the converging fields of photogrammetry and computer vision. Part I of the book provides an introduction to estimation theory, covering aspects such as Bayesian estimation, variance components, and sequential estimation, with a focus on the statistically sound diagnostics of estimation results essential in vision metrology. Part II provides tools for 2D and 3D geometric reasoning using projective geometry. This includes oriented projective geometry and tools for statistically optimal estimation and test of geometric entities and transformations and their rela­tions, tools that are useful also in the context of uncertain reasoning in point clouds. Part III is de­voted to modelling the geometry of single and multiple cameras, addressing calibration and orienta­tion, including statistical evaluation and reconstruction of corresponding scene features and surfaces based on geometric image features. The authors provide algorithms for various geometric computa­tion problems in vision metrology, together with mathematical justifications and statistical analysis, thus enabling thorough evaluations. The chapters are self-contained with numerous figures and exer­cises, and they are supported by an appendix that explains the basic mathematical notation and a de­tailed index. The book can serve as the basis for undergraduate and graduate courses in photogrammetry, com­puter vision, and computer graphics. It is also appropriate for researchers, engineers, and software developers in the photogrammetry and GIS industries, particularly those engaged with statistically based geometric computer vision methods. Note de contenu : 1. Introduction 1.1. Tasks for Photogrammetric Computer Vision 1.2. Modelling in Photogrammetric Computer Vision 1.3. The Book 1.4. On Notation Part One - Statistics and Estimation 2. Probability Theory and Random Variables 2.1. Notions of Probability 2.2. Axiomatic Definition of Probability 2.3. Random Variables 2.4. Distributions 2.5. Moments 2.6. Quantiles of a Distribution 2.7. Functions of Random Variables 2.8. Stochastic Processes 2.9. Generating Random Numbers 2.10. Exercises 3. Testing 3.1. Principles of Hypothesis Testing 3.2. Testability of an Alternative Hypothesis 3.3. Common Tests 3.4. Exercises 4. Estimation 4.1. Estimation Theory 4.2. The Linear Gauss–Markov Model 4.3. Gauss–Markov Model with Constraints 4.4. The Nonlinear Gauss–Markov Model 4.5. Datum or Gauge Definitions and Transformations 4.6. Evaluation 4.7. Robust Estimation and Outlier Detection 4.8. Estimation with Implicit Functional Models 4.9. Methods for Closed Form Estimations 4.10. Estimation in Autoregressive Models 4.11. Exercises Part two - Geometry 5. Homogeneous Representations of Points, Lines and Planes 5.1. Homogeneous Vectors and Matrices 5.2. Homogeneous Representations of Points and Lines in 2D 5.3. Homogeneous Representations in IPn 5.4. Homogeneous Representations of 3D Lines 5.5. On Plücker Coordinates for Points, Lines and Planes 5.6. The Principle of Duality 5.7. Conics and Quadrics 5.8. Normalizations of Homogeneous Vectors 5.9. Canonical Elements of Coordinate Systems 5.10. Exercises 6. Transformations 6.1. Structure of Projective Collineations 6.2. Basic Transformations 6.3. Concatenation and Inversion of Transformations 6.4. Invariants of Projective Mappings 6.5. Perspective Collineations 6.6. Projective Correlations 6.7. Hierarchy of Projective Transformations and Their Characteristics 6.8. Normalizations of Transformations 6.9. Conditioning 6.10. Exercises 7. Geometric Operations 7.1. Geometric Operations in 2D Space 7.2. Geometric Operations in 3D Space 7.3. Vector and Matrix Representations for Geometric Entities 7.4. Minimal Solutions for Conics and Transformations 7.5. Exercises 8. Rotations 8.1. Rotations in 3D 8.2. Concatenation of Rotations 8.3. Relations Between the Representations for Rotations 8.4. Rotations from Corresponding Vector Pairs 8.5. Exercises 9. Oriented Projective Geometry 9.1. Oriented Entities and Constructions 9.2. Transformation of Oriented Entities 9.3. Exercises 10. Reasoning with Uncertain Geometric Entities 10.1. Motivation 10.2. Representing Uncertain Geometric Elements 10.3. Propagation of the Uncertainty of Homogeneous Entities 10.4. Evaluating Statistically Uncertain Relations 10.5. Closed Form Solutions for Estimating Geometric Entities 10.6. Iterative Solutions for Maximum Likelihood Estimation 10.7. Exercises Part Three - Orientation and Reconstruction 11. Overview 11.1. Scene, Camera, and Image Models 11.2. The Setup of Orientation, Calibration, and Reconstruction 11.3. Exercises 12. Geometry and Orientation of the Single Image 12.1. Geometry of the Single Image 12.2. Orientation of the Single Image 12.3. Inverse Perspective and 3D Information from a Single Image 12.4. Exercises 13. Geometry and Orientation of the Image Pair 13.1. Motivation 13.2 The Geometry of the Image Pair 13.3 Relative Orientation of the Image Pair 13.4. Triangulation 13.5. Absolute Orientation and Spatial Similarity Transformation 13.6. Orientation of the Image Pair and Its Quality 13.7. Exercises 14. Geometry and Orientation of the Image Triplet 14.1. Geometry of the Image Triplet 14.2. Relative Orientation of the Image Triplet 14.3. Exercises 15. Bundle Adjustment 15.1. Motivation for Bundle Adjustment and Its Tasks 15.2. Block Adjustment 15.3. Sparsity of Matrices, Free Adjustment and Theoretical Precision 15.4. Self-calibrating Bundle Adjustment 15.5. Camera Calibration 15.6. Outlier Detection and Approximate Values 15.7. View Planning 15.8. Exercises 16. Surface Reconstruction 16.1. Introduction 16.2. Parametric 21/2D Surfaces 16.3. Models for Reconstructing One-Dimensional Surface Profiles 16.4. Reconstruction of 21/2D Surfaces from 3D Point Clouds 16.5. Examples for Surface Reconstruction 16.6. Exercises Appendix: Basics and Useful Relations from Linear Algebra Numéro de notice : 22610 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Manuel Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82915

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22610-03	DEP-ELZ	Livre	Marne-la-Vallée	Dépôt en unité	Exclu du prêt

Introduction du concept de patrons géométriques et application aux bâtiments afin de faciliter leur généralisation cartographique à la volée / M.N. Sabo in Geomatica, vol 59 n° 3 (September 2005)  

Public [article]  inGeomatica > vol 59 n° 3 (September 2005) . - pp 295 - 311 Titre : Introduction du concept de patrons géométriques et application aux bâtiments afin de faciliter leur généralisation cartographique à la volée Type de document : Article/Communication Auteurs : M.N. Sabo, Auteur ; A. Cardenas, Auteur ; et al., Auteur Année de publication : 2005 Article en page(s) : pp 295 - 311 Note générale : Bibliographie Langues : Français (fre) Descripteur : [Termes IGN] bati [Termes IGN] cartographie par internet [Termes IGN] déplacement d'objet géographique [Termes IGN] figure géométrique [Termes IGN] généralisation à la volée [Termes IGN] généralisation cartographique automatisée [Termes IGN] modèle géométrique du bâti [Termes IGN] objet cartographique [Termes IGN] objet géographique zonal [Termes IGN] rotation d'objet [Vedettes matières IGN] Généralisation Résumé : (Auteur) Malgré d'importants progrès réalisés ces dernières années, la généralisation cartographique automatique reste encore une tâche difficile qui demande le plus souvent une intervention humaine. Pour cette raison, les solutions actuelles basées sur l'exploitation d'algorithmes ne peuvent pas à elles seules répondre aux exigences des nouvelles applications comme la cartographie sur le Web qui nécessitent une généralisation à la volée. Cet article présente le concept de patrons géométriques, qui sont des formes communes à plusieurs objets cartographiques, et propose leur utilisation au sein d'un processus de généralisation cartographique. Ainsi, au lieu de généraliser un objet cartographique (ex. : un bâtiment) par le biais d'algorithmes de généralisation afin d'assurer une bonne lisibilité de la carte, un patron géométrique est utilisé pour remplacer l'objet. Pour cela, le patron géométrique est ajusté à la géométrie de l'objet par l'intermédiaire d'opérations très simples comme la rotation et le déplacement. Un même patron peut servir à plusieurs centaines d'occurrences d'objets et seul un nombre minimum de paramètres distinguant chaque relation patron-objet est nécessaire. Les premiers tests démontrent que les patrons géométriques présentent une performance de cinq à vingt-sept fois supérieure à celle des approches de généralisation utilisant des algorithmes. Numéro de notice : A2005-456 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.5623/geomat-2005-0038 En ligne : https://doi.org/10.5623/geomat-2005-0038 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27592 [article]

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035-05031	RAB	Revue	Centre de documentation	En réserve L003	Disponible