Abstract: 3-D elastic motion estimation from monocular image sequence is crucial for many important applications. In this paper, an approach is proposed for 3-D elastic motion from monocular image sequence using vector-entropy regularization. First, with the established correspondences of feature points between consecutive frames, the least squares estimation model is proposed based on affine motion model and central projection model. Then, in order to overcome the ill-posed 3-D motion estimation problem, a method using regularization is proposed. A vector entropy consisting of the second order entropy (Ent-2) and the cross entropy is constructed as the regularization term which incorporates the prior motion knowledge into the estimation process. By imposing the motion constraints, the vector-entropy regularization converts the ill-posed problem into a well-posed one and guarantees the robust solution. Experimental results from a synthetic image sequence demonstrate the feasibility of the proposed approach.
Yaming Wang and Zhou Xu , 2004. 3-D Elastic Motion Estimation Based on Vector-Entropy Regularization . Asian Journal of Information Technology, 3: 481-487.