This thesis tackles the challenge of registering modern to historic images in the context of urban rephotography. It aims at automatically identifying stable image features in scenes, which have been exposed to medium to tremendous changes across the years. Instead, the related field of location recognition mainly focuses on illumination and seasonal changes. This work illustrates that common feature descriptors are applicable in the context of historic and modern image matching, while local detectors are not, but most important is the choice of appropriate correspondence filters. It is verified that major structural changes are most challenging for traditional image matching approaches and the methods developed in this work are applicable to challenging image pairs beyond rephotography. Besides, features extracted from Convolutional Neural Networks (CNNs), originally trained for the task of location recognition, show high performance and should be further developed for the specific task of historic to modern image matching. At last, practical developments are presented, including an online portal for presenting and organizing rephotographs as well as an initial version of a mobile application, which supports recovering the original viewpoint of an image.
| Identifer | oai:union.ndltd.org:uni-osnabrueck.de/oai:repositorium.ub.uni-osnabrueck.de:urn:nbn:de:gbv:700-202007133290 |
| Date | 13 July 2020 |
| Creators | Becker, Ann-Katrin |
| Contributors | Prof. Dr. Oliver Vornberger, Prof. Dr. Christian Heipke |
| Source Sets | Universität Osnabrück |
| Language | English |
| Detected Language | English |
| Type | doc-type:doctoralThesis |
| Format | application/pdf, application/zip |
| Rights | Attribution 3.0 Germany, http://creativecommons.org/licenses/by/3.0/de/ |
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