Point Correspondence Recovery in Non-Rigid Motion
Abstract
A method for the estimation of point correspondences on a surface undergoing nonrigid motion, based on changes in Gaussian curvature, is described. An approach for estimating the point correspondences and stretching of a surface undergoing conformal motion with constant (homothetic), linear, or polynomial stretching is proposed. Small motion assumption is utilized to hypothesize all possible point correspondences. Curvature changes are then computed for each hypothesis. The difference between computed curvature changes and the one predicted by the conformal motion assumption is calculated. The hypothesis with the smallest error gives point correspondences between consecutive time frames. Simulations performed on ellipsoidal data illustrate the performance and accuracy of derived algorithms. The algorithm is applied to volumetric CT data of the left ventricle of a dog's heart.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Cite
Text
Kambhamettu and Goldgof. "Point Correspondence Recovery in Non-Rigid Motion." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1992. doi:10.1109/CVPR.1992.223271Markdown
[Kambhamettu and Goldgof. "Point Correspondence Recovery in Non-Rigid Motion." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1992.](https://mlanthology.org/cvpr/1992/kambhamettu1992cvpr-point/) doi:10.1109/CVPR.1992.223271BibTeX
@inproceedings{kambhamettu1992cvpr-point,
title = {{Point Correspondence Recovery in Non-Rigid Motion}},
author = {Kambhamettu, Chandra and Goldgof, Dmitry B.},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {1992},
pages = {222-227},
doi = {10.1109/CVPR.1992.223271},
url = {https://mlanthology.org/cvpr/1992/kambhamettu1992cvpr-point/}
}