Jiang Yifeng, Xie Jun, Tsui Hung-Tat
Department of Electronic Engineering, Chinese University of Hong Kong, Shatin, Hong Kong.
IEEE Trans Inf Technol Biomed. 2008 Sep;12(5):627-35. doi: 10.1109/TITB.2008.920798.
This paper formulates shape registration as an optimal coding problem. It employs a set of landmarks to establish the correspondence between shapes, and assumes that the best correspondence can be achieved when the polygons formed by the landmarks optimally code all the shape contours, i.e., obtain their minimum description length (MDL). This is different from previous MDL-based shape registration methods, which code the landmark locations. In this paper, each contour is discretized to be a set of points to make the coding feasible, and a number of strategies are adopted to tackle the difficult optimization problem involved. The resulting algorithm, called CAP, is able to yield statistical shape model with better quality in terms of model generalization error, which is demonstrated on both synthetic and biomedical shapes.
本文将形状配准公式化为一个最优编码问题。它采用一组地标点来建立形状之间的对应关系，并假设当地标点形成的多边形能最优地编码所有形状轮廓时，即获得其最小描述长度（MDL）时，可实现最佳对应。这与以往基于MDL的形状配准方法不同，以往方法是对标标点位置进行编码。在本文中，每个轮廓被离散化为一组点以使编码可行，并采用了多种策略来解决所涉及的困难优化问题。所得算法称为CAP，在模型泛化误差方面能够生成质量更好的统计形状模型，这在合成形状和生物医学形状上均得到了验证。
