• Media type: E-Book; Thesis
  • Title: Graph-based object understanding
  • Contributor: Teich, Florian [VerfasserIn]; Wörgötter, Florentin [AkademischeR BetreuerIn]; May, Wolfgang [AkademischeR BetreuerIn]; Damm, Carsten [AkademischeR BetreuerIn]; Kurth, Winfried [AkademischeR BetreuerIn]; Waack, Stephan [AkademischeR BetreuerIn]; Yahyapour, Ramin [AkademischeR BetreuerIn]
  • imprint: Göttingen, 2021
  • Extent: 1 Online-Ressource; Illustrationen, Diagramme
  • Language: English
  • Identifier:
  • Keywords: Hochschulschrift
  • Origination:
  • University thesis: Dissertation, Georg-August-Universität Göttingen, 2021
  • Footnote:
  • Description: Computer Vision algorithms become increasingly prevalent in our everyday lives. Especially recognition systems are often employed to automatize certain tasks (i.e. quality control). In State-of-the-Art approaches global shape char acteristics are leveraged, discarding nuanced shape varieties in the individual parts of the object. Thus, these systems fall short on both learning and utilizing the inherent underlying part structures of objects. By recognizing common substructures between known and queried objects, part-based systems may identify objects more robustly in lieu of occlusion or re...
  • Access State: Open Access