3D Human Skeleton Extraction Based on Sagittal Plane and Neural Network
Ding Yong-sheng · 2012
In allusion to the problem of interference to the skeleton extraction of different human posture,a new algorithm of skeleton extraction is presented.Through the combination of the depth information based on sagittal plane of 3D human model with improved Hopfield neural network,the rate of convergence speeds up by using a new input-output function of network to improve traditional human skeleton extraction algorithm.The network gets away from local minimum successfully and decreases the running time of network which is decided to depth information of feature points.Experimental result shows that the displacement on skeleton feature points using new algorithm is obvious less than that of traditional algorithm.In addition,the computation time is decreased.Therefore,the new algorithm has better effect on human skeleton extraction.