Landmark-Based Shape Deformation with Topology-Preserving Constraints
Abstract
This paper presents a novel approach for landmark-based shape deformation, in which fitting error and shape difference are formulated into a support vector machine (SVM) regression problem. To well describe nonrigid shape deformation, this paper measures the shape difference using a thin-plate spline model. The proposed approach is capable of preserving the topology of the template shape in the deformation. This property is achieved by inserting a set of additional points and imposing a set of linear equality and/or inequality constraints. The underlying optimization problem is solved using a quadratic programming algorithm. The proposed method has been tested using practical data in the context of shape-based image segmentation. Some relevant practical issues, such as missing detected landmarks and selection of the regularization parameter are also briefly discussed.
Cite
Text
Wang et al. "Landmark-Based Shape Deformation with Topology-Preserving Constraints." IEEE/CVF International Conference on Computer Vision, 2003. doi:10.1109/ICCV.2003.1238447Markdown
[Wang et al. "Landmark-Based Shape Deformation with Topology-Preserving Constraints." IEEE/CVF International Conference on Computer Vision, 2003.](https://mlanthology.org/iccv/2003/wang2003iccv-landmark/) doi:10.1109/ICCV.2003.1238447BibTeX
@inproceedings{wang2003iccv-landmark,
title = {{Landmark-Based Shape Deformation with Topology-Preserving Constraints}},
author = {Wang, Song and Ji, Jim Xiuquan and Liang, Zhi-Pei},
booktitle = {IEEE/CVF International Conference on Computer Vision},
year = {2003},
pages = {923-930},
doi = {10.1109/ICCV.2003.1238447},
url = {https://mlanthology.org/iccv/2003/wang2003iccv-landmark/}
}