This paper proposes a non-rigid registration formulation capturing both global and local deformations in a single framework. This formulation is based on a quadratic estimation of the registration distance together with a quadratic regularization term. Hence, the optimal transformation parameters are easily obtained by solving a liner system of equations, which guarantee a fast convergence. Experimental results with challenging 2D and 3D shapes are presented to show the validity of the proposed framework. Furthermore, comparisons with the most relevant approaches are provided. © 2012 Springer-Verlag.
CITATION STYLE
Rouhani, M., & Sappa, A. D. (2012). Non-rigid shape registration: A single linear least squares framework. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7578 LNCS, pp. 264–277). https://doi.org/10.1007/978-3-642-33786-4_20
Mendeley helps you to discover research relevant for your work.