P01 - Non-Overlap Image Registration
- Event
- SMSI 2023
2023-05-08 - 2023-05-11
Nürnberg - Band
- Poster
- Chapter
- Poster
- Author(s)
- S. Siemens, M. Kästner, E. Reithmeier - Leibniz Universität Hannover, Garbsen (Germany)
- Pages
- 282 - 283
- DOI
- 10.5162/SMSI2023/P01
- ISBN
- 978-3-9819376-8-8
- Price
- free
Abstract
This work aims to predict the relative position of non-overlapping image pairs consisting of a moving and a fixed image. For this purpose, a modified VGG16 convolutional neural network is proposed. The network is trained on a large dataset with microtopographic measurement data of different materials and processing methods. The proposed method shows a high prediction accuracy on the test data and the potential for developing non-overlap registration algorithms.