A Markov Random Field Model for Foreground-background Separation
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چکیده
In this paper we introduce a novel approach of foreground-background-shadow separation. Our method tries to extract the accurate silhouettes of foreground objects even if they have partly background like colors and shadows are observable on the image. It does not need any a priory information about the shapes of the objects, it assumes only they are not point-wise. The method exploits temporal statistics to characterize the background and shadow, and spatial statistics to the foreground. A Markov Random Field model is used to enhance the accuracy of the separation.
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