Improving video segmentation algorithms by detection of and adaption to altered illumination
Författare
Summary, in English
Changing illumination constitutes a serious challenge for video segmentation
algorithms, especially in outdoor scenes under cloudy conditions.
Rapid illumination changes, e.g. caused by varying cloud cover,
often cause existing segmentation algorithms to erroneously classify
large parts of the image as foreground.
Here a method that extends existing segmentation algorithms by
detecting illumination changes using a CUSUM detector and adjusting
the background model to conform with the new illumination is
presented. The method is shown to work for two segmentation algorithms,
and it is indicated how the method could be extended to other
algorithms.
algorithms, especially in outdoor scenes under cloudy conditions.
Rapid illumination changes, e.g. caused by varying cloud cover,
often cause existing segmentation algorithms to erroneously classify
large parts of the image as foreground.
Here a method that extends existing segmentation algorithms by
detecting illumination changes using a CUSUM detector and adjusting
the background model to conform with the new illumination is
presented. The method is shown to work for two segmentation algorithms,
and it is indicated how the method could be extended to other
algorithms.
Avdelning/ar
Publiceringsår
2008
Språk
Engelska
Publikation/Tidskrift/Serie
Preprints in Mathematical Sciences
Volym
2008:9
Fulltext
Dokumenttyp
Artikel i tidskrift
Förlag
Lund University
Ämne
- Mathematics
- Probability Theory and Statistics
Status
Unpublished
ISBN/ISSN/Övrigt
- ISSN: 1403-9338
- LUTFMS-5075-2008