中文摘要：

许多三维重建算法的结果以二进制体数据的形式给出,然而直接从中抽取等值面会造成阶梯化、锯齿化等走样现象.为此提出基于最大后验概率？马尔科夫随机场的二进制体数据优化方法.假设目标数据是随机变量并具有马尔科夫性,通过计算其最大后验概率推导了通用的优化公式,以及在常用模型下的优化公式;在此基础上,用户可选择不同的先验模型和观察模型来预测数据最有可能的取值,并将其作为优化结果.实验结果表明,文中方法可用于二进制体数据的可视化,光顺,去噪,修复等.

英文摘要：

Many 3D reconstruction algorithms produce binary volume data. However, directly extracting iso-surfaces from them results in serious aliasing. To this end, we introduce a binary volume optimization method based on a maximum a posteriori-Markov random field （MAP-MRF）. We assume that the target values are random variables and have the Markov property. By maximizing their posteriori probability, we deduce a general optimization formula, as well as formulae in several special cases. Based on these formulae, users can choose different prior and observation models to predict the most possible data values, which is considered as optimal. Experimental results show that our method can be applied in visualization, smoothing, de-noising, and repairing.