Local Features in Image and Video Processing - Object Class Matching and Video Shot Detection
Lankinen, Jukka (2014-05-22)
Väitöskirja
Lankinen, Jukka
22.05.2014
Lappeenranta University of Technology
Acta Universitatis Lappeenrantaensis
Julkaisun pysyvä osoite on
https://urn.fi/URN:ISBN:978-952-265-593-6
https://urn.fi/URN:ISBN:978-952-265-593-6
Tiivistelmä
The usage of digital content, such as video clips and images, has increased dramatically
during the last decade. Local image features have been applied increasingly in various
image and video retrieval applications. This thesis evaluates local features and applies
them to image and video processing tasks. The results of the study show that 1) the
performance of different local feature detector and descriptor methods vary significantly
in object class matching, 2) local features can be applied in image alignment with superior
results against the state-of-the-art, 3) the local feature based shot boundary detection
method produces promising results, and 4) the local feature based hierarchical video
summarization method shows promising new new research direction. In conclusion, this
thesis presents the local features as a powerful tool in many applications and the imminent
future work should concentrate on improving the quality of the local features.
during the last decade. Local image features have been applied increasingly in various
image and video retrieval applications. This thesis evaluates local features and applies
them to image and video processing tasks. The results of the study show that 1) the
performance of different local feature detector and descriptor methods vary significantly
in object class matching, 2) local features can be applied in image alignment with superior
results against the state-of-the-art, 3) the local feature based shot boundary detection
method produces promising results, and 4) the local feature based hierarchical video
summarization method shows promising new new research direction. In conclusion, this
thesis presents the local features as a powerful tool in many applications and the imminent
future work should concentrate on improving the quality of the local features.
Kokoelmat
- Väitöskirjat [1099]