| Titre : |
Deep learning for feature based image matching |
| Type de document : |
Thèse/HDR |
| Auteurs : |
Lin Chen, Auteur ; Christian Heipke, Directeur de thèse |
| Editeur : |
Munich : Bayerische Akademie der Wissenschaften |
| Année de publication : |
2021 |
| Collection : |
DGK - C, ISSN 0065-5325 num. 867  |
| Importance : |
159 p. |
| Format : |
21 x 30 cm |
| Note générale : |
bibliographie
Diese Arbeit ist gleichzeitig veröffentlicht in: Wissenschaftliche Arbeiten der Fachrichtung Geodäsie und Geoinformatik der Leibniz UniversitätHannoverISSN 0174-1454, Nr. 369, Hannover 2021 |
| Langues : |
Anglais (eng) |
| Descripteur : |
[Vedettes matières IGN] Photogrammétrie numérique
[Termes IGN] appariement d'images
[Termes IGN] chaîne de traitement
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] descripteur
[Termes IGN] image aérienne oblique
[Termes IGN] orientation d'image
[Termes IGN] orthoimage
|
| Résumé : |
(auteur) Feature based image matching aims at finding matched features between two or more images. It is one of the most fundamental research topics in photogrammetry and computer vision. The matching features area prerequisite for applications such as image orientation, Simultaneous Localization and Mapping (SLAM) and robot vision. A typical feature based matching algorithm is composed of five steps: feature detection, affine shape estimation, orientation, description and descriptor matching. Today, the employment of deep neural network has framed those different steps as machine learning problems and the matching performance has been improved significantly. One of the main reasons why feature based image matching may still prove difficult is the complex change between different images, including geometric and radiometric transformations. If the change between images exceeds a certain level, it will also exceed the tolerance of those aforementioned separate steps and, in turn, cause feature based image matching to fail.
This thesis focuses on improving feature based image matching against large viewpoint and viewing direction change between images. In order to improve the feature based image matching performance under these circumstances, affine shape estimation, orientation and description are solved with deep learning architectures. In particular, Convolutional Neural Networks (CNN) are used. For the affine shape and orientation learning, the main contribution of this thesis is two fold. First, instead of a Siamese CNN, only one branch is needed and the loss is built based on the geometric measures calculated from the mean gradient or second moment matrix. Therefore, for each of the input patches, a global minimum, namely the canonical feature, exists. Second, both the affine shape and orientation are solved simultaneously within one network by combining the loss used for affine shape and orientation learning. To the best of the author’s knowledge, this is the first time these two modules are reported to have been successfully trained simultaneously. For the descriptor learning part, a new weak match is defined. For any input feature patch, a slightly transformed patch that lies far from the input feature patch in descriptor space is defined as a weak match feature. A weak match finder network is proposed to actively find these weak match features. In a following step, the found weak matches are used in the standard descriptor learning framework. In this way, the intra-variance of the appearance of matched feature patch pairs is explored in depth and, accordingly, the invariance of feature descriptors against viewpoint and viewing direction change is improved. The proposed feature based image matching method is evaluated on standard benchmarks and is used to solve for the parameters of image orientation. For the image orientation task, aerial oblique images are taken into account. Through analysis of the experiments conducted for small image blocks, it is shown that deep learning feature based image matching leads to more registered images, more reconstructed 3D points and a more stable block connection. |
| Note de contenu : |
1- Introduction
2- Basics
3- Related work
4- Deep learning feature representation
5- Experiments and results
6- Discussion
7- Conclusion and outlook |
| Numéro de notice : |
17673 |
| Affiliation des auteurs : |
non IGN |
| Thématique : |
IMAGERIE |
| Nature : |
Thèse étrangère |
| Note de thèse : |
PhD dissertation : Fachrichtung Geodäsie und Geoinformatik : Hanovre : 2021 |
| En ligne : |
https://dgk.badw.de/fileadmin/user_upload/Files/DGK/docs/c-867.pdf |
| Format de la ressource électronique : |
URL |
| Permalink : |
https://documentation.ensg.eu/index.php?lvl=notice_display&id=97999 |
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Deep learning for feature based image matching (2021)
