A survey of image registration techniques
ACM Computing Surveys (CSUR)
Active shape models—their training and application
Computer Vision and Image Understanding
Shape alignment—optimal initial point and pose estimation
Pattern Recognition Letters
Non-rigid registration using distance functions
Computer Vision and Image Understanding - Special issue on nonrigid image registration
Kernel Density Estimation and Intrinsic Alignment for Shape Priors in Level Set Segmentation
International Journal of Computer Vision
Fourier Descriptors for Plane Closed Curves
IEEE Transactions on Computers
Hi-index | 0.00 |
This paper presents a fast algorithm for robust registration of shapes implicitly represented by signed distance functions(SDFs). The proposed algorithm aims to recover the transformation parameters( scaling, rotation, and translation) by minimizing the dissimilarity between two shapes. To achieve a robust and fast algorithm, linear orthogonal transformations are employed to minimize the dissimilarity measures. The algorithm is applied to various shape registration problems, to address issues such as topological invariance, shape complexity, and convergence speed and stability. The outcomes are compared with other state-of-the-art shape registration algorithms to show the advantages of the new technique.