A precise and stable foreground segmentation using fine-to-coarse approach in transform domain
Hiroaki Tezuka, Takao Nishitani · 2008
This paper describes a precise and stable foreground segmentation using computationally efficient fine-to-coarse strategy based on a Gaussian mixture model (GMM). In our algorithm, a set of GMMs is employed on multiple block sizes by using Walsh transform (WT). Four neighboring WTs can be easily merged into a WT of four times wider block without using the inverse transform. The precise and stable processing comes from the multiresolutional GMM, and the WT spectral nature drastically reduces the computational steps. Experimental results show that our approach gives stable performance in many conditions, such as scenery in heavy snow and global lighting changes.