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Multi-view Segmentation Based on Human Pose Estimation in Images

Meng Liu, Jia Qingxuan


Human pose estimation in images is one of the important issues in the field of image analysis in recent years. The main difficulties are that there’s less available information in static image, background disturbance and shading, etc., which make the problem challenging. Aiming at the deficiency of existing algorithm, a new algorithm was proposed for human pose estimation by 2D model and multi-view segmentation. We establish 2D model; and Robust Segmentation of multi-view images; introduce a novel shape prior for segmenting that integrates the previously estimated poses and shapes. The experiment shows that the algorithm performs better than the classic algorithm on the public datasets.


2D Model, Multi-view Segmentation, Shape prior, Pose estimation

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