基于带约束的隐表示非线性形状配准

主要通过对形状进行带约束的隐表示来研究非线性形状配准. 首先, 采用隐函数的零水平集来表示形状, 并结合从整体到局部的策略,对形状配准问题进行了建模. 其次, 为提高模型精度, 对全局尺度形变和局部非线性形变引入了尺度约束和带状约束. 进一步, 给出了一阶变分, 并应用负梯度流进行数值求解. 最后，多个数据集上与现有经典算法的对比实验表明, 给出的算法具有更优的精度.

Nonlinear shape registration via constrained implicit representation

This paper addresses the nonlinear shape registration by using the constrained implicit representation for the shape. First, we adopt the level set of the implicit function to represent the shape and model the shape registration, where a global-to-local strategy is applied. Then, in order to improve the regularity, the scale and the bending constraints are introduced to the global and local parameters respectively. Further, a first order variational model is deduced and solved by the gradient descent method. Finally, the comparison of the proposed algorithm with some state-of-the-art approaches on a variety of data sets validates our better performance.