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Contributions à la segmentation non supervisée d'images hyperspectrales : trois approches algébriques et géométriques / Saadallah El Asmar (2016)
Titre : Contributions à la segmentation non supervisée d'images hyperspectrales : trois approches algébriques et géométriques Type de document : Thèse/HDR Auteurs : Saadallah El Asmar, Auteur ; Michel Berthier, Directeur de thèse ; Carl Frélicot, Directeur de thèse Editeur : La Rochelle : Université de La Rochelle Année de publication : 2016 Importance : 96 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse pour l'obtention du grade de docteur de l'Université de La Rochelle, Mathématiques et ApplicationsLangues : Français (fre) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] analyse de groupement
[Termes IGN] analyse spectrale
[Termes IGN] appariement
[Termes IGN] classification non dirigée
[Termes IGN] classification par nuées dynamiques
[Termes IGN] classification pixellaire
[Termes IGN] codage
[Termes IGN] géométrie de Riemann
[Termes IGN] image hyperspectrale
[Termes IGN] segmentation d'image
[Termes IGN] similitude
[Termes IGN] télédétectionIndex. décimale : THESE Thèses et HDR Résumé : (auteur) Depuis environ une dizaine d’années, les images hyperspectrales produites par les systèmes de télédétection, “Remote Sensing”, ont permis d’obtenir des informations très fiables quant aux caractéristiques spectrales de matériaux présents dans une scène donnée. Nous nous intéressons dans ce travail au problème de la segmentation non supervisée d’images hyperspectrales suivant trois approches bien distinctes. La première, de type Graph Embedding, nécessite deux étapes : une première étape d’appariement des pixels de patchs de l’image initiale grâce à une mesure de similarité spectrale entre pixels et une seconde étape d’appariement d’objets issus des segmentations locales grâce à une mesure de similarité entre objets. La deuxième, de type Spectral Hashing ou Semantic Hashing, repose sur un codage binaire des variations des profils spectraux. On procède à des segmentations par clustering à l’aide d’un algorithme de k-modes adapté au caractère binaire des données à traiter et à l’aide d’une version généralisée de la distance classique de Hamming. La troisième utilise les informations riemanniennes des variétés issues des différentes façons de représenter géométriquement une image hyperspectrale. Les segmentations se font une nouvelle fois par clustering à l’aide d’un algorithme de k-means. Nous exploitons pour cela les propriétés géométriques de l’espace des matrices symétriques définies positives, induites par la métrique de Fisher Rao. Note de contenu : 1- Introduction
2- Segmentation par similarité
3- Segmentation par codage binaire
4- Segmentation riemanienne
5- ConclusionNuméro de notice : 25821 Affiliation des auteurs : non IGN Thématique : IMAGERIE/MATHEMATIQUE Nature : Thèse française Note de thèse : Thèse de Doctorat : Mathématiques et Applications : Université de La Rochelle : 2016 Organisme de stage : Laboratoire Mathématiques, Image et Applications nature-HAL : Thèse DOI : sans En ligne : https://tel.archives-ouvertes.fr/tel-01661468/document Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95094 Photogrammetric computer vision / Wolfgang Förstner (2016)
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 3DIndex. 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 AlgebraNuméro de notice : 22610 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Manuel Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82915 Réservation
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Titre : Probabilistic multi-person localisation and tracking Type de document : Thèse/HDR Auteurs : Tobias Klinger, Auteur ; Ingo Neumann, Directeur de thèse Editeur : Munich : Bayerische Akademie der Wissenschaften Année de publication : 2016 Collection : DGK - C, ISSN 0065-5325 num. 787 Importance : 125 p. Format : 21 x 30 cm ISBN/ISSN/EAN : 978-3-7696-5199-7 Note générale : bibliographie
PhD DissertationLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse de sensibilité
[Termes IGN] analyse multicritère
[Termes IGN] classification
[Termes IGN] détection de piéton
[Termes IGN] géolocalisation
[Termes IGN] image isolée
[Termes IGN] modèle stochastique
[Termes IGN] objet mobile
[Termes IGN] piéton
[Termes IGN] poursuite de cible
[Termes IGN] programmation linéaire
[Termes IGN] séquence d'images
[Termes IGN] similitude
[Termes IGN] surveillanceRésumé : (auteur) This dissertation investigates the problem of localising multiple persons in image sequences, while, at the same time, establishing temporal correspondences between single-frame locations. The aim of this work is the improvement of the reliability and precision of the generated trajectories, which is addressed by the formulation and investigation of a joint probabilistic model for the recursive filtering of the estimated positions. The trajectories are estimated in a common 3D object coordinate system, which was previously almost exclusively done in 2D. Note de contenu : 1. Introduction
1.1. Motivation
1.2. Research objectives and contributions
1.3. Outline of the dissertation
2. Basics
2.1. Probabilistic modelling
2.2. Recursive Bayesian estimation
2.3. Gaussian Process Regression
3. Related work
3.1. Tracking approaches
3.2. Observations
3.3. Temporal modelling
3.4. Data association
3.5. Discussion
4. A new probabilistic approach for multi-person localisation and tracking
4.1. Problem statement via Dynamic Bayesian Network
4.2. Observations
4.3. Temporal model
4.4. data association
4.5. Recursive estimation
4.6. Discussion
5. Experiments
5.1. Datasets and evaluation criteria
5.2. Sensitivity study and training
5.3. Model validation by ablation of its components
5.4. Multi-person localisation and tracking evaluation
6. Discussion of the results
6.1. Method evaluation
6.2. Evaluation of the trajectories
7. Conclusions and future workNuméro de notice : 19793 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Thèse étrangère Note de thèse : PhD Dissertation : : Stuttgart : 2016 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85037 Documents numériques
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Probabilistic multi-person localisation and trackingAdobe Acrobat PDF Extendable linearised adjustment model for deformation analysis / Hiddo Velsink in Survey review, vol 47 n° 345 (November 2015)
[article]
Titre : Extendable linearised adjustment model for deformation analysis Type de document : Article/Communication Auteurs : Hiddo Velsink, Auteur Année de publication : 2015 Article en page(s) : pp 397 - 410 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Topographie moderne
[Termes IGN] ajustement de paramètres
[Termes IGN] analyse diachronique
[Termes IGN] congruence
[Termes IGN] matrice de covariance
[Termes IGN] surveillance d'ouvrage
[Termes IGN] tachéomètre électronique
[Termes IGN] transformation affineRésumé : (auteur) This paper gives a linearised adjustment model for the affine, similarity and congruence transformations in 3D that is easily extendable with other parameters to describe deformations. The model considers all coordinates stochastic. Full positive semi-definite covariance matrices and correlation between epochs can be handled. The determination of transformation parameters between two or more coordinate sets, determined by geodetic monitoring measurements, can be handled as a least squares adjustment problem. It can be solved without linearisation of the functional model, if it concerns an affine, similarity or congruence transformation in one-, two- or three-dimensional space. If the functional model describes more than such a transformation, it is hardly ever possible to find a direct solution for the transformation parameters. Linearisation of the functional model and applying least squares formulas is then an appropriate mode of working. The adjustment model is given as a model of observation equations with constraints on the parameters. The starting point is the affine transformation, whose parameters are constrained to get the parameters of the similarity or congruence transformation. In this way the use of Euler angles is avoided. Because the model is linearised, iteration is necessary to get the final solution. In each iteration step approximate coordinates are necessary that fulfil the constraints. For the affine transformation it is easy to get approximate coordinates. For the similarity and congruence transformation the approximate coordinates have to comply to constraints. To achieve this, use is made of the singular value decomposition of the rotation matrix. To show the effectiveness of the proposed adjustment model total station measurements in two epochs of monitored buildings are analysed. Coordinate sets with full, rank deficient covariance matrices are determined from the measurements and adjusted with the proposed model. Testing the adjustment for deformations results in detection of the simulated deformations. Numéro de notice : A2015-946 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1179/1752270614Y.0000000140 En ligne : https://doi.org/10.1179/1752270614Y.0000000140 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=79650
in Survey review > vol 47 n° 345 (November 2015) . - pp 397 - 410[article]Polygonal clustering analysis using multilevel graph-partition / Wanyi Wang in Transactions in GIS, vol 19 n° 5 (October 2015)
[article]
Titre : Polygonal clustering analysis using multilevel graph-partition Type de document : Article/Communication Auteurs : Wanyi Wang, Auteur ; Shihong Du, Auteur ; Zhou Guo, Auteur ; Liqun Luo, Auteur Année de publication : 2015 Article en page(s) : pp 716 – 736 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse comparative
[Termes IGN] analyse de groupement
[Termes IGN] connexité (graphes)
[Termes IGN] distance
[Termes IGN] données spatiotemporelles
[Termes IGN] figure géométrique
[Termes IGN] groupe
[Termes IGN] partition des données
[Termes IGN] polygone
[Termes IGN] similitudeRésumé : (auteur) Existing methods of spatial data clustering have focused on point data, whose similarity can be easily defined. Due to the complex shapes and alignments of polygons, the similarity between non-overlapping polygons is important to cluster polygons. This study attempts to present an efficient method to discover clustering patterns of polygons by incorporating spatial cognition principles and multilevel graph partition. Based on spatial cognition on spatial similarity of polygons, four new similarity criteria (i.e. the distance, connectivity, size and shape) are developed to measure the similarity between polygons, and used to visually distinguish those polygons belonging to the same clusters from those to different clusters. The clustering method with multilevel graph-partition first coarsens the graph of polygons at multiple levels, using the four defined similarities to find clusters with maximum similarity among polygons in the same clusters, then refines the obtained clusters by keeping minimum similarity between different clusters. The presented method is a general algorithm for discovering clustering patterns of polygons and can satisfy various demands by changing the weights of distance, connectivity, size and shape in spatial similarity. The presented method is tested by clustering residential areas and buildings, and the results demonstrate its usefulness and universality. Numéro de notice : A2015-684 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12124 En ligne : http://dx.doi.org/10.1111/tgis.12124 Format de la ressource électronique : Url artticle Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78325
in Transactions in GIS > vol 19 n° 5 (October 2015) . - pp 716 – 736[article]An alternative method to constructing time cartograms for the visual representation of scheduled movement data / Rehmat Ullah in Journal of maps, vol 11 n° 4 ([01/08/2015])PermalinkA local approach to optimize the scale parameter in multiresolution segmentation for multispectral imagery / F. Cánovas-García in Geocarto international, vol 30 n° 7 - 8 (August - September 2015)PermalinkIntegrating user needs on misclassification error sensitivity into image segmentation quality assessment / Hugo Costa in Photogrammetric Engineering & Remote Sensing, PERS, vol 81 n° 6 (June 2015)PermalinkUK open source crime data: accuracy and possibilities for research / Lisa Tompson in Cartography and Geographic Information Science, Vol 42 n° 2 (April 2015)PermalinkScalable multi-platform distribution of spatial 3D contents / Jan Klimke in International journal of 3-D information modeling, vol 3 n° 3 (July- September 2014)PermalinkAutomatic registration of optical imagery with 3D LiDAR data using statistical similarity / Ebadat Ghanbari Parmehr in ISPRS Journal of photogrammetry and remote sensing, vol 88 (February 2014)PermalinkImproving representation of land-use maps derived from object-oriented image classification / Wenxiu Gao in Transactions in GIS, vol 17 n° 3 (June 2013)PermalinkFast construction of global pyramids for very large satellite images / Longgang Xiang in Transactions in GIS, vol 17 n° 2 (April 2013)PermalinkLow altitude aerial photography applications for digital surface models creation in archaeology / José-Angel Martinez-Del-Pozo in Transactions in GIS, vol 17 n° 2 (April 2013)PermalinkTrajectories of moving objects on a network: detection of similarities, visualization of relations, and classification of trajectories / Yukio Sadahiro in Transactions in GIS, vol 17 n° 1 (February 2013)Permalink