Point Set Registration with Global-Local Correspondence and Transformation Estimation
Point Set Registration with Global-Local Correspondence and Transformation Estimation
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DOI：
10.1109/iccv.2017.291
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发表时间：
2017-10
期刊：
影响因子：
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通讯作者：
Su Zhang;Yang Yang-Yang;Kun Yang;Yi Luo;S. Ong
中科院分区：
文献类型：
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作者：
Su Zhang;Yang Yang-Yang;Kun Yang;Yi Luo;S. Ong
提出了一种新的点集配准方法--全局-局部对应变换估计法（GL-CATE）。该算法将点集的全局特征点到点欧氏距离与基于椭圆高斯软计数策略生成直方图的局部特征形状距离（SD）相结合，利用点集的几何结构。通过双向确定性退火算法直接控制两种特征的搜索范围，构造混合特征高斯混合模型（MGMM）来恢复点集的对应关系。提出了一种新的基于向量的结构约束项来正则化变换。通过在全局和局部尺度上约束空间结构，提高了变换更新的精度。应用退火方案来逐渐减小正则化的强度并实现最大重叠。上述两个过程都包含在EM算法中，这是一个统一的优化框架。我们测试了我们的GL-CATE在轮廓配准，序列图像，真实的图像，医学图像，指纹图像和遥感图像的性能，并与八个国家的最先进的方法相比，我们的GL-CATE在大多数情况下表现出良好的性能。
We present a new point set registration method with global-local correspondence and transformation estimation (GL-CATE). The geometric structures of point sets are exploited by combining the global feature, the point-to-point Euclidean distance, with the local feature, the shape distance (SD) which is based on the histograms generated by an elliptical Gaussian soft count strategy. By using a bidirectional deterministic annealing scheme to directly control the searching ranges of the two features, the mixture-feature Gaussian mixture model (MGMM) is constructed to recover the correspondences of point sets. A new vector based structure constraint term is formulated to regularize the transformation. The accuracy of transformation updating is improved by constraining spatial structure at both global and local scales. An annealing scheme is applied to progressively decrease the strength of the regularization and to achieve the maximum overlap. Both of the aforementioned processes are incorporated in the EM algorithm, a unified optimization framework. We test the performances of our GL-CATE in contour registration, sequence images, real images, medical images, fingerprint images and remote sensing images, and compare with eight state-of-the-art methods where our GL-CATE shows favorable performances in most scenarios.