中文摘要：

针对视角变换下的形状识别问题, 提出一种基于交比上下文关系的层次化形状特征提取及匹配算法. 首先通过由粗到精的采样方式建立层次化的形状特征描述子, 实现对形状从整体到局部的描述关系; 其次通过对传统的交比不变量进行扩展, 建立每5 个采样点之间的射影不变关系; 最后在形状匹配方面使用动态规划算法计算形状间相似度. 实验结果表明, 该算法对形状变形具有很好的识别效果, 并且计算复杂度低、特征维度小. 此外, 文中层次化的方法也适用于其他不变量特征, 便于和传统的形状特征表示方法进行融合, 充分发挥2 种描述子各自的优势, 具有一定的扩展性.

英文摘要：

In this paper, a new shape descriptor named hierarchical cross ratio contexts for shape matching is proposed to solve the shape recognition problem under projective transformations. First, a coarse-to-fine approach is used hierarchically to calculate the feature for each contour point, which makes the proposed description com-bine both global geometry and local contextual information. Second, this algorithm modifies the traditional cross ratio, and constructs the invariants using each five points extracted from the shape contour. Finally, dynamic pro-gramming is used to compute the similarity between shapes. Experiments demonstrate that our method obtains high recognition performance with relatively low computation complexity and low feature dimension. Moreover, our representation is also open to any other projective invariants, and is easy to extend by combining with other popular descriptors, which well displays their respective advantages.