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Bundle adjustment of spherical images acquired with a portable panoramic image mapping system (PPIMS) / Yi-Hsing Tseng in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 12 (December 2016)
[article]
Titre : Bundle adjustment of spherical images acquired with a portable panoramic image mapping system (PPIMS) Type de document : Article/Communication Auteurs : Yi-Hsing Tseng, Auteur ; Yung-Chuan Chen, Auteur ; Kuan-Ying Lin, Auteur Année de publication : 2016 Article en page(s) : pp 935 - 943 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] antenne GPS
[Termes IGN] compensation par faisceaux
[Termes IGN] image panoramique
[Termes IGN] prise de vue terrestre
[Termes IGN] spatiotriangulation
[Termes IGN] système de numérisation mobileRésumé : (auteur) Thanks to the development of mobile mapping technologies, close-range photogrammetry (CRP) has advanced to be an efficient mapping method for a variety of applications. A compact CRP system equipped with multiple cameras and a GPS receiver is one of those advanced portable mapping systems. A portable panoramic image mapping system (PPIMS) was specially designed to capture panoramic images with eight cameras and to obtain the position of image station with a GPS receiver. A PPIMS can be considered as a panoramic CRP system. The coordinates of an object point can be determined by the intersection of panoramic image points. For the implementation, we propose a new concept of photogrammetry by using panoramic images. Eight images captured by PPIMS forms a spherical panorama image (SPI). Instead of using the original images, PPIMS SPIs are then used for photogrammetric triangulation and mapping. Under this circumstance, one SPI is formed for each station, and it is associated with only one set of exterior orientation (EO) parameters. Traditional collinearity equations are not applicable to SPI triangulation and mapping. Therefore, a novel bundle adjustment algorithm is proposed to solve EO of multi-station SPIs. Because PPIMS SPIs are not ideal SPIs, a correction scheme was also developed to correct the imperfect geometry of PPIMS SPI. Two test studies were performed for the data collected at a campus test field of National Cheng Kung University (NCKU) and at a historical site of Tainan. Both cases demonstrate the feasibility of SPI bundle adjustment and applying corrections for PPIMS SPIs necessary for effective for bundle adjustment. Furthermore, the experiment's results also confirm that SPIs can replace original images for PPIMS triangulation. Numéro de notice : A2016-982 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.82.12.935 En ligne : https://doi.org/10.14358/PERS.82.12.935 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83698
in Photogrammetric Engineering & Remote Sensing, PERS > vol 82 n° 12 (December 2016) . - pp 935 - 943[article]Refined satellite image orientation in the free open-source photogrammetric tools Apero/MicMac / Ewelina Rupnik in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol III-1 (July 2016)
[article]
Titre : Refined satellite image orientation in the free open-source photogrammetric tools Apero/MicMac Type de document : Article/Communication Auteurs : Ewelina Rupnik , Auteur ; Marc Pierrot-Deseilligny , Auteur ; Arthur Delorme, Auteur ; Yann Klinger, Auteur Année de publication : 2016 Projets : TOSCA / Conférence : ISPRS 2016, Commission 3, 23th international congress 12/07/2016 19/07/2016 Prague République tchèque ISPRS OA Annals Commission 3 Article en page(s) : 8 p. Note générale : bibliographie
The authors would like to thank CNES for funding this project through the TOSCA program.Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique
[Termes IGN] APERO
[Termes IGN] compensation par faisceaux
[Termes IGN] géoréférencement
[Termes IGN] image Pléiades-HR
[Termes IGN] MicMac
[Termes IGN] modèle par fonctions rationnelles
[Termes IGN] orientation absolue
[Termes IGN] point d'appui
[Termes IGN] rotationRésumé : (auteur) This publication presents the RPC-based bundle adjustment implemented in the freeware open-source photogrammetric tool Apero/MicMac. The bundle adjustment model is based on some polynomial correction functions, enriched with a physical constraint that introduces the notion of a global sensor rotation into the model. The devised algorithms are evaluated against two datasets consisting of two stereo and a triplet pair of the Pleiades images. Two sets of correction functions and a number of GCPs configurations are examined. The obtained geo-referencing accuracy falls below the size of 1GSD. Numéro de notice : A2016--189 Affiliation des auteurs : ENSG+Ext (2012-2019) Autre URL associée : vers HAL Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprs-annals-III-1-83-2016 Date de publication en ligne : 01/06/2016 En ligne : http://dx.doi.org/10.5194/isprs-annals-III-1-83-2016 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91831
in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol III-1 (July 2016) . - 8 p.[article]Documents numériques
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Refined satellite image orientation ... - pdf éditeurAdobe Acrobat PDF
Titre : Evaluation of SIFT and SURF for vision based localization Type de document : Article/Communication Auteurs : Xiaozhi Qu , Auteur ; Bahman Soheilian , Auteur ; Emmanuel Habets , Auteur ; Nicolas Paparoditis , Auteur Editeur : International Society for Photogrammetry and Remote Sensing ISPRS Année de publication : 2016 Collection : International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, ISSN 1682-1750 num. 41-B3 Conférence : ISPRS 2016, Commission 3, 23th international congress 12/07/2016 19/07/2016 Prague République tchèque ISPRS OA Archives Commission 3 Importance : pp 685 - 692 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] compensation locale par faisceaux
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] localisation basée vision
[Termes IGN] point d'intérêt
[Termes IGN] SIFT (algorithme)
[Termes IGN] SURF (algorithme)Résumé : (auteur) Vision based localization is widely investigated for the autonomous navigation and robotics. One of the basic steps of vision based localization is the extraction of interest points in images that are captured by the embedded camera. In this paper, SIFT and SURF extractors were chosen to evaluate their performance in localization. Four street view image sequences captured by a mobile mapping system, were used for the evaluation and both SIFT and SURF were tested on different image scales. Besides, the impact of the interest point distribution was also studied. We evaluated the performances from for aspects: repeatability, precision, accuracy and runtime. The local bundle adjustment method was applied to refine the pose parameters and the 3D coordinates of tie points. According to the results of our experiments, SIFT was more reliable than SURF. Apart from this, both the accuracy and the efficiency of localization can be improved if the distribution of feature points are well constrained for SIFT. Numéro de notice : C2016-039 Affiliation des auteurs : LASTIG MATIS (2012-2019) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/isprs-archives-XLI-B3-685-2016 Date de publication en ligne : 10/06/2016 En ligne : https://doi.org/10.5194/isprs-archives-XLI-B3-685-2016 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91851 Documents numériques
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Evaluation of SIFT and SURF ... - pdf éditeurAdobe Acrobat PDF Localisation à base d’amers visuels : Cartographie et mise en correspondance de marquages au sol et intégration dans LBA / Bahman Soheilian (2016)
Titre : Localisation à base d’amers visuels : Cartographie et mise en correspondance de marquages au sol et intégration dans LBA Type de document : Article/Communication Auteurs : Bahman Soheilian , Auteur ; Xiaozhi Qu , Auteur ; Mathieu Brédif , Auteur Editeur : Association française pour la reconnaissance et l'interprétation des formes AFRIF Année de publication : 2016 Projets : TrafiPollu / Conférence : RFIA 2016, 20e conférence Reconnaissance des Formes et Intelligence Artificielle 27/06/2016 01/07/2016 Clermond-Ferrand France OA Proceedings Importance : 7 p. Note générale : bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique
[Termes IGN] algorithme RJMCMC
[Termes IGN] amer
[Termes IGN] appariement d'images
[Termes IGN] carte en 3D
[Termes IGN] compensation locale par faisceaux
[Termes IGN] détection d'objet
[Termes IGN] géopositionnement
[Termes IGN] méthode de Monte-Carlo par chaînes de Markov
[Termes IGN] point d'appui
[Termes IGN] reconstruction 3D
[Termes IGN] reconstruction d'objet
[Termes IGN] signalisation routière
[Termes IGN] zone urbaine denseRésumé : (auteur) La localisation précise en milieux urbains denses est un verrou important pour les systèmes de transport intelligent et cartographie mobile. Nous proposons une stratégie pour utiliser des marquages au sol comme des amers visuels pour la localisation basée vision. La première étape consiste à reconstruire une carte de marquages au sol en 3D. Un système de cartographie mobile est utilisé pour numériser l’environnement en 3D et générer une orthophoto de route. L’algorithme RJ-MCMC est utilisé pour détecter un ensemble extensible de modèles de marquage. Les objets détectés sont reconstruits en 3D en utilisant les informations de hauteur des points 3D. Une caméra optique calibrée et une antenne GPS de bas coût sont embarquées sur un véhicule pour la localisation. L’ajustement de faisceaux local (LBA) est appliqué pour estimer la trajectoire du véhicule. Pour réduire la dérive de localisation, les images acquises par la caméra sont fréquemment appariées sur les marquages au sol géoréférencés. Cet appariement est initialisé en utilisant la pose estimée (LBA) et optimisée par un algorithme MCMC. Cela produit des points d’appui qui seront intégrés dans le LBA. La méthode est évaluée sur des données réelles acquises en milieux urbains denses. Numéro de notice : C2016-019 Affiliation des auteurs : LASTIG MATIS (2012-2019) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésNat DOI : sans En ligne : http://rfia2016.iut-auvergne.com/media/articles/JVI12.pdf Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84610 Documents numériques
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Localisation à base d’amers visuelsAdobe Acrobat PDF 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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