中文摘要：

为在形状匹配的过程中利用较少的计算时间获取较高的识别率，同时兼顾形状特征对几何变形的鲁棒性，提出一种以度量分段约束为特征的形状匹配算法．通过提取形状轮廓上采样点间的度量信息，如欧氏距离、三角形半径等，约束每个采样点与整体形状之间的几何关系；进一步，将度量信息进行分段描述，以增强该算法对几何变形的稳定性；最终结合动态规划算法完成形状的匹配．在国际通用数据库上的实验结果表明，文中算法能够快速、有效地实现形状的匹配，且对于形状变形具有较好的适应性；此外，该算法适用于多种几何度量信息，便于扩展和推广．

英文摘要：

A novel shape matching method is proposed based on metric partition constraint. The method can obtain high recognition rate with low time complexity, and is also robust to geometric deformations. First of all, the metric information, such as Euclidean distance and triangle radius, is extracted for each sample point, which can constrain the geometric relations between all sample points and the whole shape. Furthermore, the outer contour of the shape is divided into several partitions, and smoothed separately to make it robust to deformations. Finally, dynamic programming algorithm is employed to compare different shapes. Experiments on public databases show that the proposed method is effective and fast, and is also adapt to various deformations. It is worth mentioning that the proposed method can be extended with numerous metrics.