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Domain Adaptation of Unreal Images for Image Classification
Linköping University, Department of Electrical Engineering, Computer Vision.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Domänöversättning av syntetiska bilder för bildklassificiering (Swedish)
Abstract [en]

Deep learning has been intensively researched in computer vision tasks like im-age classification. Collecting and labeling images that these neural networks aretrained on is labor-intensive, which is why alternative methods of collecting im-ages are of interest. Virtual environments allow rendering images and automaticlabeling,  which could speed up the process of generating training data and re-duce costs.This  thesis  studies  the  problem  of  transfer  learning  in  image  classificationwhen the classifier has been trained on rendered images using a game engine andtested on real images. The goal is to render images using a game engine to createa classifier that can separate images depicting people wearing civilian clothingor camouflage.  The thesis also studies how domain adaptation techniques usinggenerative  adversarial  networks  could  be  used  to  improve  the  performance  ofthe classifier.  Experiments show that it is possible to generate images that canbe used for training a classifier capable of separating the two classes.  However,the experiments with domain adaptation were unsuccessful.  It is instead recom-mended to improve the quality of the rendered images in terms of features usedin the target domain to achieve better results.

Place, publisher, year, edition, pages
2019. , p. 34
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:liu:diva-165758ISRN: LiTH-ISY-EX–20/5282–SEOAI: oai:DiVA.org:liu-165758DiVA, id: diva2:1431281
External cooperation
Swedish Defence Research Agency
Subject / course
Computer Vision Laboratory
Available from: 2020-05-19 Created: 2020-05-19 Last updated: 2020-05-20Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
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  • de-DE
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Output format
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