IEEE Trans Image Process - A novel recursive Bayesian learning-based method for the efficient and accurate segmentation of video with dynamic background.

Tópicos

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Resumo

Segmentation of video with dynamic background is an important research topic in image analysis and computer vision domains. In this paper, we present a novel recursive Bayesian learning-based method for the efficient and accurate segmentation of video with dynamic background. In the algorithm, each frame pixel is represented as the layered normal distributions which correspond to different background contents in the scene. The layers are associated with a confident term and only the layers satisfy the given confidence which will be updated via the recursive Bayesian estimation. This makes learning of background motion trajectories more accurate and efficient. To improve the segmentation quality, the coarse foreground is obtained via simple background subtraction first. Then, a local texture correlation operator is introduced to fill the vacancies and remove the fractional false foreground regions. Extensive experiments on a variety of public video datasets and comparisons with some classical and recent algorithms are used to demonstrate its improvements in both segmentation accuracy and efficiency.

Resumo Limpo

segment video dynam background import research topic imag analysi comput vision domain paper present novel recurs bayesian learningbas method effici accur segment video dynam background algorithm frame pixel repres layer normal distribut correspond differ background content scene layer associ confid term layer satisfi given confid will updat via recurs bayesian estim make learn background motion trajectori accur effici improv segment qualiti coars foreground obtain via simpl background subtract first local textur correl oper introduc fill vacanc remov fraction fals foreground region extens experi varieti public video dataset comparison classic recent algorithm use demonstr improv segment accuraci effici

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