<div class="csl-bib-body">
<div class="csl-entry">Dachs, F. (2024). <i>Change Detection in Graffiti Images</i> [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2024.106190</div>
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dc.identifier.uri
https://doi.org/10.34726/hss.2024.106190
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/202614
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dc.description
Arbeit an der Bibliothek noch nicht eingelangt - Daten nicht geprüft
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dc.description.abstract
The thesis presents an empirical evaluation of change detection in graffiti images, a largely unexplored domain for change detection. To conduct this evaluation three steps were conducted:1. Preparation of a dataset: As the available graffiti data is very scarce and highlycorrelated a new dataset had to be established. This was achieved by creating asynthetic dataset by adding digital graffiti to images of a graffiti wall.2. Training of the models: To increase the performance of the state-of-the-artchange detection models in the domain of graffiti images, the models were trained on graffiti data. The training was performed in different settings (finetuning and training from-scratch) as well as using different combinations of the available data. In this step, a simpler baseline model was implemented as well, to be able to evaluate the complexity of the task.3. Evaluation: Finally, the models were evaluated on the synthetic as well as on ahand-labeled real-world dataset.The evaluation showed that the original models cannot be used without finetuning for change detection in graffiti images, achieving an average F1-Score of 0.134. All models showed a significant improvement after finetuning, on verage the F1-Score increased to 0.612 for the models trained on all available data. For most models, synthetic data had an overall positive effect, especially since the precision could be improved for all models using synthetic data. The best-performing model was the baseline model, only trained on hand-labeled real-world data, achieving an F1-Score of 0.692.
en
dc.language
English
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dc.language.iso
en
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dc.rights.uri
http://rightsstatements.org/vocab/InC/1.0/
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dc.subject
Computer Vision
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dc.subject
Change Detection
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dc.subject
Deep Learning
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dc.title
Change Detection in Graffiti Images
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dc.type
Thesis
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dc.type
Hochschulschrift
de
dc.rights.license
In Copyright
en
dc.rights.license
Urheberrechtsschutz
de
dc.identifier.doi
10.34726/hss.2024.106190
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dc.contributor.affiliation
TU Wien, Österreich
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dc.rights.holder
Fabian Dachs
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dc.publisher.place
Wien
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tuw.version
vor
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tuw.thesisinformation
Technische Universität Wien
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tuw.publication.orgunit
E193 - Institut für Visual Computing and Human-Centered Technology
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dc.type.qualificationlevel
Diploma
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dc.identifier.libraryid
AC17336636
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dc.description.numberOfPages
55
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dc.thesistype
Diplomarbeit
de
dc.thesistype
Diploma Thesis
en
dc.rights.identifier
In Copyright
en
dc.rights.identifier
Urheberrechtsschutz
de
tuw.advisor.staffStatus
staff
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tuw.advisor.orcid
0000-0002-3459-8122
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item.cerifentitytype
Publications
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item.grantfulltext
open
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item.openairetype
master thesis
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item.fulltext
with Fulltext
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item.openaccessfulltext
Open Access
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item.openairecristype
http://purl.org/coar/resource_type/c_bdcc
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item.languageiso639-1
en
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crisitem.author.dept
E193 - Institut für Visual Computing and Human-Centered Technology