doi:

DOI: 10.3724/SP.J.1089.2010.11073

Journal of Computer-Aided Design & Computer Graphics (计算机辅助设计与图形学学报) 2010/22:9 PP.1545-1553

A PrimarySecondary Foreground Segmentation Method with Window Series PCA De-noising


Abstract:
Noise characteristic and motion properties of different foreground objects under various weather conditions are analyzed for outdoor videos, and a primary-secondary foreground segmentation method is proposed. A window series PCA algorithm, combined with the Gaussian mixture model, is used to model the videos after de-nosing and segmenting all foreground objects primarily. After that, the probabilities of the overlapped regions are calculated to describe the motion properties of different objects, and a second segmentation step is carried out to extract the interesting objects. Finally, the uninteresting objects, such as raindrops and snowflakes, are treated via a background-inpainting step to improve the video quality. Experimental results show that our proposed method can effectively reduce noise, diminish the interference of rain or snow, and enhance video effects.

Key words:window series PCA,primary-secondary foreground segmentation,rain and snow removal,Gaussian mixture model,video processing

ReleaseDate:2014-07-21 15:25:46



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