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GIBBS RANDOM FIELD MODEL BASED 3-D MOTION ESTIMATION BY WEAKENED RIGIDITY

3-D motion estimation from a video sequence remains a challenging problem. Modelling the local interactions between the 3-D motion parameters is possible by using Gibbs random fields. An energy function which gives the joint probability distribution of the motion vectors, is constructed. The most probable motion vector set is found by maximizing the probability, represented by this distribution. Since the 3-D motion estimation problem is ill-posed, the regularization is achieved by an initial rigidity assumption. Afterwards, the rigidity is weakened hierarchically, until the finest level is reached. At the finest level, each point has its own motion vector and the "weak-connection" between these vectors are described by the energy function. The high computational cost Q decreased considerably by the multiprecision approach. The simulation results support all our discussions.