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  • Wissenschaftspark Albert Einstein  (1)
  • SB Bad Wilsnack  (1)
  • Stadtmuseum Berlin
  • 2020-2024  (2)
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  • 1
    UID:
    b3kat_BV047616439
    Format: 52 ungezählte Seiten , 27.6 cm x 22.6 cm
    Edition: Originalausgabe, 1. Aufl.
    ISBN: 9783551521286 , 355152128X
    Note: Enthält 1 Leporello, 1 Karte, 1 Spielvorlage (mit Anleitung)
    Additional Edition: Erscheint auch als Online-Ausgabe, EPUB ISBN 978-3-646-93418-2
    Language: German
    Subjects: Comparative Studies. Non-European Languages/Literatures , Education , German Studies
    RVK:
    RVK:
    RVK:
    Keywords: Prinzessin ; Einhorn ; Schlangen ; Langeweile ; Bilderbuch ; Kinderbuch
    Author information: Henn, Astrid
    Author information: Kling, Marc-Uwe 1982-
    Library Location Call Number Volume/Issue/Year Availability
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  • 2
    Book
    Book
    Hannover : Fachrichtung Geodäsie und Geoinformatik, Univ. Hannover
    Show associated volumes
    UID:
    kobvindex_GFZ176793534X
    Format: 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
    Note: Auch veröffentlicht in: Deutsche Geodätische Kommission bei der Bayerischen Akademie der Wissenschaften, Reihe C, Nr. 867, München 2021, ISBN 978-3-7696-5279-6 , Dissertation, Gottfried Wilhelm Leibniz Universität Hannover, 2021 , 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
    In: Wissenschaftliche Arbeiten der Fachrichtung Geodäsie und Geoinformatik der Leibniz Universität Hannover
    Language: English
    Keywords: Hochschulschrift
    Author information: Chen, Lin 1987-
    Library Location Call Number Volume/Issue/Year Availability
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