An efficient point-set registration algorithm with dual terms based on total least squares

Point set registration (PSR) is competitive with related techniques because it purposefully captures the overall structure between two point-set patterns. Typically, the point set registration problem can be divided into two sub-problems: (1) search point set correspondence (PSC); (2) estimate spatial transformation matrix (STM). Searching for the best PSC is a classical combinatorial explosion problem, and estimating the STM is a continuous space optimization problem. Also, two feature point sets detected by two low-quality images include point-position errors and often involve bilateral outliers composed of such feature points that cannot form a correspondence relationship. To address the above problems, we propose an efficient PSR algorithm with dual (symmetrical) terms based on the total least squares (DT-TLS), which can correct errors-in-variables and suppress multiple outliers. Meanwhile, the framework of soft decision-making is presented, and a TLS-based criterion is constructed to efficiently exploit the probability and global structures of two-point sets. Such TLS-based criterion with single row-orthonormal STM includes two interesting dual (symmetrical) terms that can be conveniently exploited to suppress bilateral outliers. The experimental results show that DT-TLS achieves better performance than the state-of-the-art algorithms in some multi-view computer vision tasks, indicating that our proposed algorithm is suitable for solving PSR problems.