Superpixels and Attention for High-quality Object Proposals in Complex Environments

Link:
Autor/in:
Beteiligte Person:
  • Frintrop, Simone
Verlag/Körperschaft:
Staats- und Universitätsbibliothek Hamburg Carl von Ossietzky
Erscheinungsjahr:
2022
Medientyp:
Text
Schlagworte:
  • 004: Informatik
  • Bildverarbeitung
  • ddc:004:
  • Bildverarbeitung
Beschreibung:
  • The class-agnostic discovery of objects in images, known as object proposal generation, is a fundamental task in computer vision. In this thesis, we mainly address two major challenges in object proposal generation. First, we introduce a system that improves the challenging discovery of small objects. Second, we propose a refinement method that allows a more accurate segmentation of the discovered objects. Overall, this leads to high-quality object proposals for objects of all sizes.
Lizenzen:
  • http://purl.org/coar/access_right/c_abf2
  • info:eu-repo/semantics/openAccess
  • https://creativecommons.org/licenses/by/4.0/
Quellsystem:
E-Dissertationen der UHH

Interne Metadaten
Quelldatensatz
oai:ediss.sub.uni-hamburg.de:ediss/9668