运动估计问题具有不适定性，单纯采用最大后验概率算法，实际上并未解决运动矢量的不连续、矢量的失真与随机噪声等棘手问题。本文应用模糊数据融合与Gibbs分布的基本思想，将运动场风险约束条件的概率分布模式有效地纳入阶段非凸函数(GNC)算法的局部迭代过程中，从而提高了运动估计效果。首先建立Gibbs的自适应能量模型，该模型可将基于特征和基于梯度的两类矢量按照优化约束条件进行融合；其次利用各种运动经验知识构造矢量的模糊风险决策表，该决策表可对Gibbs能量方程的每一步迭代收敛结果进行监督和修正，从而实现模糊数据融合。从收敛性和鲁棒性两方面说，模糊融合后的结果在原有基础上有明显提高。

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.