Non-Rigid Shape Registration: A Single Linear Least Squares Framework
Abstract
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.
Cite
Text
Rouhani and Sappa. "Non-Rigid Shape Registration: A Single Linear Least Squares Framework." European Conference on Computer Vision, 2012. doi:10.1007/978-3-642-33786-4_20Markdown
[Rouhani and Sappa. "Non-Rigid Shape Registration: A Single Linear Least Squares Framework." European Conference on Computer Vision, 2012.](https://mlanthology.org/eccv/2012/rouhani2012eccv-non/) doi:10.1007/978-3-642-33786-4_20BibTeX
@inproceedings{rouhani2012eccv-non,
title = {{Non-Rigid Shape Registration: A Single Linear Least Squares Framework}},
author = {Rouhani, Mohammad and Sappa, Angel Domingo},
booktitle = {European Conference on Computer Vision},
year = {2012},
pages = {264-277},
doi = {10.1007/978-3-642-33786-4_20},
url = {https://mlanthology.org/eccv/2012/rouhani2012eccv-non/}
}