Robust non-rigid point set registration method based on asymmetric Gaussian and structural feature

Abstract

Point set registration is a fundamental problem in many domains of computer vision. In previous work on the registration, the point sets are often represented using Gaussian mixture models and the registration process is represented as a form of a probabilistic solution. For non-rigid point set registration, however, the asymmetric Gaussian (AG) model can capture spatially asymmetric distributions compared with symmetric Gaussian, and the structural feature of the point sets reserve relatively complete and has important significance in registration. In this work, the authors designed a new shape context (SC) descriptor which combines the local and global structures of the point set. Meanwhile, they proposed a non-rigid point set registration algorithm which formulates a registration process as the mixture probability density estimation of the AG mixture model, and the method introduce the structural feature by the new SC. Extensive experiments show that the proposed algorithm has a clear improvement over the state-of-the-art methods.