A Correlation-Based Approach to Robust Point Set Registration
Abstract
Correlation is a very effective way to align intensity images. We extend the correlation technique to point set registration using a method we call kernel correlation. Kernel correlation is an affinity measure, and it is also a function of the point set entropy. We define the point set registration problem as finding the maximum kernel correlation configuration of the the two point sets to be registered. The new registration method has intuitive interpretations, simple to implement algorithm and easy to prove convergence property. Our method shows favorable performance when compared with the iterative closest point (ICP) and EM-ICP methods.
Cite
Text
Tsin and Kanade. "A Correlation-Based Approach to Robust Point Set Registration." European Conference on Computer Vision, 2004. doi:10.1007/978-3-540-24672-5_44Markdown
[Tsin and Kanade. "A Correlation-Based Approach to Robust Point Set Registration." European Conference on Computer Vision, 2004.](https://mlanthology.org/eccv/2004/tsin2004eccv-correlation/) doi:10.1007/978-3-540-24672-5_44BibTeX
@inproceedings{tsin2004eccv-correlation,
title = {{A Correlation-Based Approach to Robust Point Set Registration}},
author = {Tsin, Yanghai and Kanade, Takeo},
booktitle = {European Conference on Computer Vision},
year = {2004},
pages = {558-569},
doi = {10.1007/978-3-540-24672-5_44},
url = {https://mlanthology.org/eccv/2004/tsin2004eccv-correlation/}
}