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A Rule-based approach for detection of spatial object relations in images
Halmstad University, School of Information Technology, Center for Applied Intelligent Systems Research (CAISR).
2023 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Deep learning and Computer vision are becoming a part of everyday objects and machines. Involvement of artificial intelligence in human’s daily life open doors to new opportunities and research. This involvement provides the idea of improving upon the in-hand research of spatial relations and coming up with a more generic and robust algorithm that provides us with 2-D and 3-D spatial relations and uses RGB and RGB-D images which can help us with few complex relations such as ‘on’ or ‘in’ as well. Suggested methods are tested on the dataset with animated and real objects, where the number of objects varies in every image from at least 4 to at most 10 objects. The size and orientation of objects are also different in every image.  

Place, publisher, year, edition, pages
2023. , p. 63
Keywords [en]
Spatial relations, Deep learning, Computer vision, Artificial intelligence.
National Category
Computer Systems Embedded Systems
Identifiers
URN: urn:nbn:se:hh:diva-49826OAI: oai:DiVA.org:hh-49826DiVA, id: diva2:1726623
Educational program
Master's Programme in Embedded and Intelligent Systems, 120 credits
Supervisors
Examiners
Available from: 2023-01-13 Created: 2023-01-13 Last updated: 2023-01-13Bibliographically approved

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fulltext(1881 kB)201 downloads
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Type fulltextMimetype application/pdf

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Center for Applied Intelligent Systems Research (CAISR)
Computer SystemsEmbedded Systems

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf