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  • Books  (6)
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  • Hannover : Fachrichtung Geodäsie und Geoinformatik, Univ. Hannover  (6)
  • 2020-2024  (6)
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  • 1
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    Deep learning for feature based image matching (2021)
    Hannover : Fachrichtung Geodäsie und Geoinformatik, Univ. Hannover
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    Call number: S 99.0139(369)
    In: Wissenschaftliche Arbeiten der Fachrichtung Geodäsie und Geoinformatik der Leibniz Universität Hannover
    Type of Medium: Series available for loan
    Pages: 155 Seiten , Illustrationen, Diagramme
    ISBN: 978-3-7696-5279-6 , 9783769652796
    ISSN: 0174-1454
    Series Statement: Wissenschaftliche Arbeiten der Fachrichtung Geodäsie und Geoinformatik der Leibniz Universität Hannover Nr. 369
    Language: English
    Note: Contents 1. Introduction 1.1. Motivation 1.2. Main Contributions 1.3. Thesis Outline 2. Basics 2.1. Feature based Image Matching 2.1.1. Overview: What is Feature based Image Matching? 2.1.2. Desired Properties for Detected Features and Descriptors 2.1.3. Scale-Invariant Feature Detection 2.1.4. Feature Affine Shape Estimation 2.1.5. Feature Orientation Assignment 2.1.6. Feature Description 2.1.7. Descriptor Matching 2.2. Convolutional Neural Network (CNN) 2.2.1. Architecture of CNN 2.2.2. Training of CNN 2.3. Siamese Convolutional Neural Network 3. Related Work 3.1. Local Feature Detection 3.1.1. Translation and Rotation Invariant Features 3.1.2. Scale Invariant Features 3.1.3. Detectors based on a Comparison of Grey Values or Saliency 3.1.4. Detectors based on Machine Learning 3.2. Feature Orientation and Affine Shape Estimation 3.2.1. Orientation Assignment 3.2.2. Affine Shape Estimation 3.3. Local Feature Description 3.3.1. Hand Crafted Descriptors 3.3.2. Machine Learning based Descriptors 3.4. An Application: Orientation of Oblique Aerial Images 3.5. Discussion 3.5.1. Orientation Assignment and Affine Shape Estimation 3.5.2. Descriptor Learning 3.5.3. An Aerial Photogrammetric Benchmark 3.5.4. Ability to Transfer Learned Modules 4. Deep Learning Feature Representation 4.1. Overview of the Methodology 4.2. Descriptor Learning using Active Weak Match Finder - WeMNet 4.2.1. Descriptor Learning Architecture 4.2.2. Generation of Training Pairs 4.2.3. Loss Function 4.2.4. Weak Match Branch 4.3. Self Supervised Feature Affine Shape Learning - MoNet 4.3.1. Affine Transformation Decomposition 4.3.2. Self Supervised Affine Shape Estimation Module 4.4. Self Supervised Orientation Assignment Module - MGNet 4.5. Full Affine Estimation Network - Full-AfFNet 4.5.1. Full Affine Network 4.5.2. Training Loss 4.5.3. Data Augmentation 4.6. Inference based on the Trained Networks 4.7. Discussion 4.7.1. Descriptor Learning 4.7.2. Affine Shape Estimation 4.7.3. Orientation Assignment Learning 4.7.4. The Inference Pipeline 5. Experiments and Results 5.1. Datasets 5.1.1. Datasets for Training 5.1.2. Datasets for Testing 5.2. Evaluation and Analysis Criteria 5.2.1. Task A: Patch based Image Matching 5.2.2. Task B: Descriptor Distance Analysis 5.2.3. Task C: Feature based Image Matching 5.2.4. Task D: Image Orientation 5.2.5. Summary of Tasks and Involved Datasets 5.3. Descriptor Learning and Patch Based Image Matching 5.3.1. Parameter Study for WeMNet 5.3.2. Comparison to Related Work 5.4. Descriptor Distance Analysis 5.4.1. Translation 5.4.2. Rotation 5.4.3. Affine Shape Transformation 5.5. Image Matching Analysis 5.5.1. Parameter Study for Affine Shape Learning 5.5.2. Image Matching for Rotation Dataset 5.5.3. Image Matching for Hpatches Affine Dataset 5.6. Image Orientation 5.6.1. Determination of Image Orientation 5.6.2. Experiment Setup Details 5.6.3. Orientation Result of Different Blocks 5.6.4. Matching Quality Analysis 6. Discussion 6.1. Descriptor Learning and Patch Based Image Matching 6.1.1. Parameter Study 6.1.2. Comparison to Related Works 6.2. Descriptor Distance Analysis 6.2.1. Translation 6.2.2. Rotation 6.2.3. Affine Shape Transformation 6.3. Feature based Image Matching 6.3.1. Parameter Study 6.3.2. Rotation Set 6.3.3. Affine Set 6.4. Image Orientation 7. Conclusion and Outlook Bibliography A. Affine Shape Adaptation Theory A.l. transformation of affine Gaussian scale-space A.2. Local affine distortion measurement A.3. More affine transformation
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  • 2
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    Robust deformation monitoring of bridge structures using MEMS accelerometers and image-assisted total stations (2020)
    Hannover : Fachrichtung Geodäsie und Geoinformatik, Univ. Hannover
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    Call number: S 99.0139(366)
    In: Wissenschaftliche Arbeiten der Fachrichtung Geodäsie und Geoinformatik der Leibniz Universität Hannover
    Type of Medium: Series available for loan
    Pages: ix, 241 Seiten , Illustrationen, Diagramme
    ISBN: 978-3-7696-5271-0 , 9783769652710
    ISSN: 0174-1454
    Series Statement: Wissenschaftliche Arbeiten der Fachrichtung Geodäsie und Geoinformatik der Leibniz Universität Hannover Nr. 366
    Language: English
    Note: Contents Abstract Zusammenfassung Abbreviation List of Figures List of Tables 1 Introduction 1.1 Structural Health Monitoring of Bridge Structures 1.2 Relevant Parameters for a Deformation Process 1.2.1 Static deformation parameters 1.2.2 Kinematic deformation parameters 1.3 Modal Identification Techniques 1.4 Related Works 1.5 Problem Statements 1.6 Research Topics 1.7 Outline of the Dissertation 2 Suitability Analysis of MEMS Accelerometers for Deformation Monitoring of Bridges 2.1 Introduction 2.2 Measurement Set-up and Data Acquisition 2.3 Suitability Analysis in Selecting Optimal MEMS Accelerometers 2.3.1 Controlled excitation experiment 2.3.2 Static test experiment 2.4 Calibration of MEMS Accelerometers 2.4.1 Development of test scenarios for MEMS accelerometers calibration 2.4.2 Calibration model of triaxial accelerometers 2.4.3 Adjustment of the calibration model with standard Gauss-Helmert model and variance component estimation 2.4.4 Adjustment of the calibration model with applied adaptive robust estimation 2.4.5 Experimental results of the MEMS accelerometer calibration 2.5 Sensor Specifications 2.6 Summary 3 Potential of Image-Assisted Total Stations for Bridge Monitoring 3.1 Introduction 3.2 Data Acquisition and Preprocessing 3.3 Automatic and Accurate Passive Target Centroid Detection 3.4 Conversion of Displacement Time Series from Pixel to Metric Units 3.5 External Calibration of Image-Assisted Total Stations and Digital Camera 3.6 Sensor Specifications and Measurement Systems 3.7 Summary 4 Robust Kinematic Deformation Monitoring of Bridge Structures 4.1 Introduction 4.2 Ambient Window Selection 4.3 Identification of Initial Modal Frequencies 4.4 Robust Modal Parameter Estimation based on Time Series Analysis 4.4.1 Estimation model 4.4.2 Robust adjustment procedure 4.5 Displacement Analysis based on Double Integration 4.6 Characterisation of Eigenforms 4.7 Summary 5 MEMS-based Bridge Monitoring Supported by Image-Assisted Total Stations 5.1 Introduction 5.2 Basics of Kalman Filtering for Displacement Analysis 5.3 Heterogeneous Data Fusion of Displacement and Acceleration Measurements 5.4 Summary 6 Experiments and Results 6.1 Example based on Simulated Acceleration Data 6.2 Example based on Shaker Vibration Calibrator 6.3 Example based on a Footbridge 6.4 Example based on a Synthetic Bridge 6.5 Summary 7 Summary, Conclusion and Outlook 7.1 Summary and Conclusion 7.2 Outlook A Appendix List of Publications Contributions of Authors Paper 1 Paper 2 Paper 3 Paper 4 Paper 5 Paper 6 Bibliography Curriculum Vitae Acknowledgement
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  • 3
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    Alternative integrity measures based on interval analysis and set theory (2021)
    Hannover : Fachrichtung Geodäsie und Geoinformatik, Univ. Hannover
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    Call number: S 99.0139(370)
    In: Wissenschaftliche Arbeiten der Fachrichtung Geodäsie und Geoinformatik der Leibniz Universität Hannover
    Type of Medium: Series available for loan
    Pages: x, 126 Seiten , Illustrationen, Diagramme
    ISBN: 978-3-7696-5286-4 , 9783769652864
    ISSN: 0174-1454
    Series Statement: Wissenschaftliche Arbeiten der Fachrichtung Geodäsie und Geoinformatik der Leibniz Universität Hannover Nr. 370
    Language: English
    Note: Contents 1 Introduction 1.1 Motivation 1.2 Objective & Research Questions 1.3 Outline of the Thesis 2 Related Work and Theory 2.1 GNSS Positioning 2.1.1 GNSS Constellations 2.1.2 Position Estimation 2.1.3 Measurement Errors in GPS Measurements 2.2 Reliability 2.3 Integrity 2.3.1 Conventional RAIM 2.3.2 Advanced RAIM 2.3.3 Augmentation Systems 2.3.4 Derivation of Conventional RAIM 2.3.5 Protection Level 2.4 Interval Mathematics 2.4.1 Basic Interval Mathematical Operations 2.4.2 Interval Vectors and Matrices 2.4.3 Interval Functions 2.4.4 Set Inversion via Interval Analysis 2.4.5 Contractors 2.4.6 Application of Interval Analysis to Navigation 2.5 Determination of Observation Interval Bounds 2.5.1 Probabilistic Approaches with a Priori Integrity Risk 2.5.2 Sensitivity Analysis of the Measurement Correction 2.5.2.1 Concept 2.5.2.2 Klobuchar Ionospheric Model 2.5.2.3 Saastamoinen Tropospheric Model 2.5.3 Expert Knowledge and Desired Size of the Bounding Zone 3 Integrity Approaches Based on Interval Mathematics and Set Theory 3.1 Interval Extension of Least-Squares Adjustment 3.2 Set Inversion Via Interval Analysis 3.3 Linear Programming Bounding Method 3.4 Developed Method Based on Primal-Dual Poly tope and Intervals 3.4.1 Polytopes and Zonotopes 3.4.2 Formulation and Methodology 3.4.3 Interpretation of Bounding Zones and Related Consistency Measures 3.4.4 Minimum Detectable Bias Definition 3.4.5 Integrity Measures Via Zonotopes and Polytopes 4 Performance Analysis of the Developed Methods by Monte Carlo Simulations 4.1 Introduction 4.2 Characterization of a Nominal Behavior 4.3 Proposal to Select the Critical Value of the Polytope Tests 4.4 Impact of Biases 4.4.1 Introductory Example 4.4.2 Impact of Different Biased Satellites and Observation Interval Bounds 4.4.3 Impact of Different Biased Satellites and Satellite Geometry 4.5 Analysis of the Polytope Global and Local Tests 4.5.1 General Proceeding 4.5.2 Critical Detection Scenarios - Correlated Satellites 4.5.3 Critical Detection Scenarios - Bad Geometry 4.6 Probabilistic Test Statistic Results 4.6.1 Results of Probabilistic Test Statistics 4.7 Comparison Between Probabilistic Tests and Polytopal Test 4.7.1 Good Satellite Geometry 4.7.2 Bad Satellite Geometry 4.8 Analysis of Protection Levels 4.8.1 Zonotopal Horizontal and Vertical Protection Levels 4.8.2 Statistical Based Horizontal and Vertical Protection Level 5 Real Data Analysis 5.1 Introduction 5.2 Positioning Analysis 5.2.1 Results from Scenario 1 - Urban Area 5.2.2 Results from Scenario 2 - Semi-Urban Area 5.3 Fault Detection and Exclusion 5.4 Minimum Detectable Bias 5.5 Protection Level 6 Conclusions and Outlook Bibliography Acknowledgments Curriculum Vitae
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  • 4
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    Fortgeschrittene Methoden und Algorithmen für die computergestützte geodätische Datenanalyse (2020)
    Hannover : Fachrichtung Geodäsie und Geoinformatik, Univ. Hannover
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    Call number: S 99.0139(367)
    In: Wissenschaftliche Arbeiten der Fachrichtung Geodäsie und Geoinformatik der Leibniz Universität Hannover
    Type of Medium: Series available for loan
    Pages: 348 Seiten , Illustrationen, Diagramme
    ISBN: 978-3-7696-5273-4 , 9783769652734
    ISSN: 0174-1454
    Series Statement: Wissenschaftliche Arbeiten der Fachrichtung Geodäsie und Geoinformatik der Leibniz Universität Hannover Nr. 367
    Language: German , English
    Note: Inhaltsverzeichnis Kurzfassung Abstract Abbildungsverzeichnis Liste der Abkürzungen 1 Einleitung 1.1 Motivation 1.1.1 Merkmale von geodätischen Daten 1.1.2 Statistische Modelle 1.1.3 Computergestützte und simulationsbasierte geodätische Datenanalyse 1.2 Ziele der Arbeit 1.3 Übersicht der originalen Publikationen zur kumulativen Habilitation 1 TEIL 1: Synopse der Habilitation 2 Optimierungsverfahren und Parameterschätzung in linearen und nicht-linearen Modellen 2.1 Einführung in die Parameterschätzung in linearen Regressionsmodellen 2.2 Vorstellung der ausgewählten nicht-linearen Regressionsmodelle 2.3 MODELL I: Robuste B-Spline Regressionsmodelle 2.3.1 Mathematisches Modell und Algorithmen zur Optimierung von Knotenvektoren 2.3.2 Anwendungsfälle zum MODELL I 2.4 MODELL II: Regressionsmodelle mit autoregressiven Prozessen und t-verteiltem Messrauschen 2.4.1 Mathematisches Modell und GEM-Algorithmen zur Parameterschätzung 2.4.2 Anwendungsfälle zum MODELL II 2.5 MODELL III: Bayessche robuste Regressionsmodelle mit t-verteiltem Messrauschen 2.5.1 Mathematisches Modell 2.5.2 Markov-Chain-Monte-Carlo (MCMC) Algorithmen zur Parameterschätzung 2.5.3 Anwendungsfälle zum MODELL III 2.6 Zusammenfassung des Kapitels 3 Filterungs- und Optimierungsverfahren für die Zustandsschätzung im nicht-linearen Zustandsraum 3.1 Einführung zur verschiedenen Filterungstechniken und zur sequentiellen Bayesschen Zustandsschätzung 3.2 Vorstellung nicht-linearer Filterungstechniken 3.3 FILTERUNGSANSÄTZE I: Stochastisch-analytische Ansätze 3.4 FILTERUNGSANSÄTZE II: Monte-Carlo-Simulationsbasierte Ansätze 3.5 FILTERUNGS ANSÄTZE III: Kombinierte stochastisch-deterministische Ansätze 3.6 Zusammenfassung des Kapitels 4 Simulationsbasierte Methoden für die Inferenzstatistik 4.1 Einführung in Monte-Carlo- und Bootstrapping-Methoden 4.2 Vorstellung der simulationsbasierten Methoden für die Inferenzstatistik 4.3 Simulationsbasierte-Techniken zur Bestimmung von statistischen Momenten und Konfidenzintervallen 4.3.1 BEREICHSSCHÄTZUNG I: Monte Carlo-Methoden zur GUM Unsicherheitsmodellierung 4.3.2 BEREICHSSCHÄTZUNG II: Bootstrapping zur Schätzung des Konfidenzbereichs für den EM-Algorithmus 4.4 Statistische Beurteilung von Schätzergebnissen mit simulationsbasierten Hypothesentests 4.4.1 TESTPROBLEM I: Simulationsbasierter Ansatz für die Modellwahl zur Oberflächenmodellierung von 3D-Punktwolken 4.4.2 TESTPROBLEM II: Bootstrap-Ansatz für das Testen auf Zeitvariabilität eines AR-Prozesses 4.5 Zusammenfassung des Kapitels 5 Zusammenfassung und Ausblick 5.1 Zusammenfassung 5.2 Ausblick II TEIL 2: Eigene Veröffentlichungen 6 Liste der eigenen Publikation 6.1 Veröffentlichungen zur Kapitel 2 6.2 Veröffentlichungen zur Kapitel 3 6.3 Veröffentlichungen zur Kapitel 4 6.4 Beiträge der Autoren 7 Originalpublikationen 7.1 Originalpublikationen zur Kapitel 2 7.2 Originalpublikationen zur Kapitel 3 7.3 Originalpublikationen zur Kapitel 4 Literaturverzeichnis Danksagung Lebenslauf
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  • 5
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    Automatic calibration of finite element analysis based on geometric boundary models from terrestrial laser scanning (2021)
    Hannover : Fachrichtung Geodäsie und Geoinformatik, Univ. Hannover
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    Call number: S 99.0139(375)
    In: Wissenschaftliche Arbeiten der Fachrichtung Geodäsie und Geoinformatik der Leibniz Universität Hannover
    Type of Medium: Series available for loan
    Pages: xii, 141 Seiten , Illustrationen, Diagramme
    ISSN: 0174-1454
    Series Statement: Wissenschaftliche Arbeiten der Fachrichtung Geodäsie und Geoinformatik der Universität Hannover Nr. 375
    Language: English
    Note: Contents Abstract Zusammenfassung List of Figures List of Tables Abbreviation Contents 1 Introduction 1.1 Motivation 1.2 Proposal and content 2 State of art in terrestrial laser scanning and finite element analysis 2.1 General surface measurements and TLS 2.1.1 Surface measurement 2.1.2 Geometric measurements with TLS and its technical fundamentals 2.2 FEA computations 2.2.1 FEA 2.2.2 FEA computation and the description of the boundary domain 2.3 TLS application in FEA 2.3.1 FEA parameter calibration with TLS 2.3.2 FEA geometric boundary modeling with TLS 2.3.3 Benefits of combination between TLS and FEA 3 Finite element analysis parametric geometric modeling and calibration based on terrestrial laser scanning 3.1 Fundamentals of polynomial and B-spline fitting 3.1.1 Polynomial fitting 3.1.2 B-spline fitting 3.2 Other parametric methods in fitting point clouds 3.3 Analysis and comparison between polynomial and B-spline approximations 3.4 Implementation of the calibration cases 3.5 Validation based on deformation analysis 3.5.1 General methods in deformation computation and analysis for TLS 3.5.2 Case implementation 4 Sequential calibration of finite element analysis results with terrestrial laser scanning reference based on deep learning 4.1 Employment of DL in FEA 4.1.1 Direct outputs corresponding to inputs by training neural networks 4.1.2 Material model 4.1.3 Other problems 4.1.4 Summary and analysis 4.2 DL sequential prediction and its potential in FEA 4.3 LSTM methods based on sequential prediction 4.3.1 Challenges and advanced variant models of LSTM 4.3.2 Effects of activation functions in convolutional LSTM 4.4 Sequential prediction and calibration of FEA results with a TLS reference based on convolutional LSTM 5 Conclusions and outlook Contributions of authors Paper 1 Paper 2 Paper 3 Paper 4 Bibliography Curriculum Vitae Acknowledgment
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  • 6
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    Extraction of flood and precipitation observations from opportunistic volunteered geographic information (2021)
    Hannover : Fachrichtung Geodäsie und Geoinformatik, Univ. Hannover
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    Call number: S 99.0139(376)
    In: Wissenschaftliche Arbeiten der Fachrichtung Geodäsie und Geoinformatik der Leibniz Universität Hannover
    Type of Medium: Series available for loan
    Pages: 133 Seiten
    ISSN: 0174-1454
    Series Statement: Wissenschaftliche Arbeiten der Fachrichtung Geodäsie und Geoinformatik der Leibniz Universität Hannover 376
    URL: https://dgk.badw.de/fileadmin/user_upload/Files/DGK/docs/c-882.pdf
    Classification:
    Photogrammetry, Remote Sensing
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