PT Unknown
AU Mohammad Rouhani
   Angel Sappa
TI Non-Rigid Shape Registration: A Single Linear Least Squares Framework
BT 12th European Conference on Computer Vision
PY 2012
BP 264
EP 277
VL 7578
DI 10.1007/978-3-642-33786-4_20
AB 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.
ER