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Generally, there are two approaches for solving the problem of human pose estimation from monocular images. One is the learning-based approach, and the other is the model-based approach. The former method can estimate the poses rapidly but has the disadvantage of low estimation accuracy. While the latter method is able to accurately estimate the poses, its computational cost is high. In this paper, we propose a method to integrate the learning-based and modelbased approaches to improve thedoi:10.4236/jsea.2010.311125 fatcat:5vqeaafdabhl7cbgvgu6jplila