Motion estimation is a uncertainty problem

which can't be actually solved because of discontinuity

data distortion and random noise of an image if we only start with the algorithm of MAP (maximum a posteriori probability). In this paper

in order to improve the effect of motion estimation

the fundamental idea of fuzzy data fusion and Gibbs distributing have been adopted to change the computation results of Gibbs energy function

and the risk restriction condition of motion field is effectively brought into the local updating process of GNC (graduated non-convexity function). Moreover

a Gibbs energy function based on the discontinuity adaptive Markov model has been established firstly

which can fuse two classes of vectors

one based on feature and the other on gradient under some restriction conditions; Secondly

a Risk Decision Table about the vectors field have been constructed by some experience information

by which each iterative convergence result was supervised and revised so that data fusing can be well realized. In view of the convergence and robustness of the algorithms

the results of fuzzy fusion are obviously better than that of simple Gibbs's estimation.