Human fringe skeleton extraction by an improved Hopfield neural network with direction features

The fringe skeleton is a useful means of shape description to 3D human bodies. A new method is proposed to extract human fringe skeleton by matching feature points of torso and tracking on direction of limb. We primarily extract the depth direction feature based on coronal plane of 3D human model. By introducing three transverse cross sections on torso and the concept of triangle pairs, the extraction result of coronal plane and its depth direction feature will not be affected by shading and interference of limb on different postures. With the local direction feature, an improved Hopfield neural network (IMHNN) is used to locate feature points of any position by matching feature points of template model and the personalized target model. Subsequently feature points on limb are extracted by limb direction feature using shoulder and hip feature points. Finally, the fringe skeleton of human model is obtained by the combination of feature points on torso and limb. Experiments show that the proposed algorithm has lower computation time and better extracted results compared with the existing fringe skeleton extraction algorithms.