Based on the new idea of randomly sampling the redundant minimal subset,a new method is developed of robustly and accurately restoring the 3-D vision information of an object from the model data and image data of its key point set of a single perspective view.
基于随机采样最小冗余子集的新概念，依据单视图特征点集的模型数据和图象数据，开发了一项目标三维视觉信息鲁棒精确复原新方法在强噪声高出格率的恶劣条件下，该方法仍可高精度地复原目标的三维信息实验表明，对于由１００个特征点组成的数据集而言，当出格率高达９０％，内点信噪比低达２８ｄＢ时，仍能以１％的相对误差复原特征点的三维坐标
Secondly, by using the connection, we prove that the minimal continuous semi-flow is the minimal continuous flow if its time-one map is open, and for any minimal continuous semi-flow, there is an invariant residual set such that the restriction to the set is a minimal continuous flow.
为此，我们首先建立了它与其时间1映射极小集的联系；然后，利用这种联系证明了：若时间1映射为开映射，则它是极小的连续流，并且一般地说来，对任意极小连续半流，存在不变的剩余集，使得它在这不变集上的限制是极小的连续流。
Then the fast and efficient algorithm for pruning redundant itemsets and redundant rules,ORD algorithm is applied to obtaining assoc.
该算法首先采用基于粗糙集理论的属性约简算法进行属性约简,然后采用快速、高效的冗余项集和冗余规则修剪算法——ORD算法获取关联规则。
