Projects per year
Abstract
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.
| Original language | English |
|---|---|
| Number of pages | 20 |
| Journal | Preprints in Mathematical Sciences |
| Volume | 2008:9 |
| Publication status | Unpublished - 2008 |
Subject classification (UKÄ)
- Mathematics
- Probability Theory and Statistics
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Dive into the research topics of 'Improving video segmentation algorithms by detection of and adaption to altered illumination'. Together they form a unique fingerprint.Projects
- 1 Finished
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Spatio-Temporal Estimation for Mixture Models and Gaussian Markov Random Fields - Applications to Video Analysis and Environmental Modelling
Lindström, J. (Research student), Holst, U. (Supervisor) & Lindgren, F. (Assistant supervisor)
2004/01/01 → 2008/05/23
Project: Dissertation