THESIS
2015
viii, 29 pages : color illustrations ; 30 cm
Abstract
Background extraction, which is to compute a background image given a set of images,
has real life applications in computer vision such as video surveillance and High Dynamic
Range imaging. Moving objects in the provided set of images make the problem challenging.
Obtaining such a background image is usually the first step in the aforementioned tasks and the
performance is heavily influenced by the extracted background image.
Background extraction has been studied and different methods have been proposed to solve
the tasks under various constraints. A difficult scenario is to extract the background with very
few images. We proposed a simple framework that utilizes sampling and clustering techniques
to solve the background extraction problem in this setting. Experiments are condu...[
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Background extraction, which is to compute a background image given a set of images,
has real life applications in computer vision such as video surveillance and High Dynamic
Range imaging. Moving objects in the provided set of images make the problem challenging.
Obtaining such a background image is usually the first step in the aforementioned tasks and the
performance is heavily influenced by the extracted background image.
Background extraction has been studied and different methods have been proposed to solve
the tasks under various constraints. A difficult scenario is to extract the background with very
few images. We proposed a simple framework that utilizes sampling and clustering techniques
to solve the background extraction problem in this setting. Experiments are conducted to evaluate
the performance of the proposed method with real world datasets. We also qualitatively
compared the results with several existing state-of-the-art methods.
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