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Moving objects segmentation and extraction based on motion blur features
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DOI：10.1016/j.compeleceng.2018.05.003.png)
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Abstract
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In order to restore the spatially-varying blur images whose background is sharp, it is necessary that the moving objects are segmented and extracted from the blur images. So this paper proposes a novel segmentation and extraction method based on motion blur features. Firstly, the blur features are extracted by using contourlet transform and considered as the prior information. Then an energy function is proposed for extracting the moving object from the original images. Moreover, considering that there exists some noise in segmented images, some morphological methods are introduced to remove the noise. The experimental results demonstrate the proposed scheme achieves high-quality segmentation performance and it outperforms the existing methods when used for moving objects segmentation.
Keywords:
Image segmentation
Motion blur features
Prior information
Energy function
Morphological method
Contourlet transform
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