中文摘要：

我们建议为 2-D 的新技术形状 / 轮廓结束，它是与形状分析和计算机视觉有关的重要研究话题之一，例如由于吸藏和噪音的不完全的对象的察觉。形状结束的目的是发现充满失踪的轮廓部分的最佳的曲线片断，完成目标形状以便获得原版的最好的评价。不同于用本地光滑或最小的弯曲 priors 的以前的工作，我们在利用全球形状的贝叶斯的明确的表达下面解决这个问题优先的知识。与 priors，我们的方法是恢复重要形状结构并且处理大吸藏盒子的专家。有二不同 priors，在这篇论文采用：(i) A 比较喜欢恢复完全的形状的最小的全球形状转变(关于参考书目标形状的包括的非刚性的变丑和仿射的转变) 的通用优先的模型；并且(ii ) 优先的模型从训练一个目标范畴的例子学习了的班特定的形状，它比较喜欢重建的形状跟随有学问的形状变化范畴当模特儿。有效轮廓结束算法被建议相应于 priors 的二种类型。我们的试验性的结果表明结束与存在技术相比接近的建议形状的优点，特别为有在严重吸藏下面的复杂结构的目标。

英文摘要：

We propose new techniques for 2-D shape/contour completion, which is one of the important research topics related to shape analysis and computer vision, e.g. the detection of incomplete objects due to occlusion and noises. The purpose of shape completion is to find the optimal curve segments that fill the missing contour parts, so as to acquire the best estimation of the original complete object shapes. Unlike the previous work using local smoothness or minimum curvature priors, we solve the problem under a Bayesian formulation taking advantage of global shape prior knowledge. With the priors, our methods are expert in recovering significant shape structures and dealing with large occlusion cases. There are two different priors adopted in this paper： （i） A generic prior model that prefers minimal global shape transformation （including non-rigid deformation and affine transformation with respect to a reference object shape） of the recovered complete shape; and （ii） a class-specific shape prior model learned from training examples of an object category, which prefers the reconstructed shape to follow the learned shape variation models of the category. Efficient contour completion algorithms are suggested corresponding to the two types of priors. Our experimental results demonstrate the advantage of the proposed shape completion approaches compared to the existing techniques, especially for objects with complex structure under severe occlusion.