摘要:非刚性点集配准是当前多个领域中的一项重要研究问题.现今流行的配准算法通常使用基于单一特征的对应关系评估与包含单一约束条件的空间变换更新，而单特征与单约束限制了其配准效果与应用领域.提出了一种基于双特征高斯混合模型和双约束空间变换的非刚性点集配准算法.首先定义了双特征描述子，并用全局特征和局部特征构建它；随后，基于此描述子将高斯混合模型改进为双特征高斯混合模型.定义了局部结构约束项，并与全局结构约束项分别维护点集在进行空间变换更新时的局部与全局结构稳定.通过交替进行基于双特征高斯混合模型评估点集之间的对应关系和基于高斯径向基函数（Gaussian radial basis function）更新双约束空间变换，使该算法准确地完成非刚性点集配准.通过人造点集配准、CMU序列图像配准、遥感图像配准、IMM人脸数据配准和真实图像特征点配准对该算法进行了性能测试，同时也与当前流行的8种算法进行了性能比较实验，该算法展现出了卓越的非刚性配准性能，并在大部分实验中超越了当前的相关算法.



Abstract:Non-Rigid point set registration is very important for many fields of study. Currently, the famous algorithms generally use correspondence estimation and transformation update based on single feature and single constraint. But performance and application area of the single feature and constraint based algorithms are limited. This paper presents a non-rigid point set registration method based on dual-feature Gaussian mixture model and dual-constraint transformation. Firstly, a dual-feature descriptor is defined and global feature and local feature are used to build the dual-feature descriptor. Then, Gaussian mixture model is improved to obtain a dual-feature Gaussian mixture model by the dual-feature descriptor. Finally, a local structure constraint descriptor is defined and used together with global structure constraint descriptor to preserve the local and global structures of point set. A method is presented for running estimate correspondence that uses dual-feature Gaussian mixture model and updates dual-constraint transformation based on Gaussian radial basis function iteratively to match non-rigid point set accurately. Performance of the presented method is evaluated by synthetic point set registration, CMU sequence image registration, remote sensing registration, IMM face data registration and true image feature point registration. Comparing with other eight state-of-the-ate methods, the new method shows the best alignments in most scenarios.



PDF全文下载地址:

http://jos.org.cn/jos/article/pdf/5300