Metric-Based Pairwise and Multiple Image Registration
Abstract
Registering pairs or groups of images is a widely-studied problem that has seen a variety of solutions in recent years. Most of these solutions are variational, using objective functions that should satisfy several basic and desired properties. In this paper, we pursue two additional properties – (1) invariance of objective function under identical warping of input images and (2) the objective function induces a proper metric on the set of equivalence classes of images – and motivate their importance. Then, a registration framework that satisfies these properties, using the L ^2-norm between a novel representation of images, is introduced. Additionally, for multiple images, the induced metric enables us to compute a mean image, or a template, and perform joint registration. We demonstrate this framework using examples from a variety of image types and compare performances with some recent methods.
Cite
Text
Xie et al. "Metric-Based Pairwise and Multiple Image Registration." European Conference on Computer Vision, 2014. doi:10.1007/978-3-319-10605-2_16Markdown
[Xie et al. "Metric-Based Pairwise and Multiple Image Registration." European Conference on Computer Vision, 2014.](https://mlanthology.org/eccv/2014/xie2014eccv-metric/) doi:10.1007/978-3-319-10605-2_16BibTeX
@inproceedings{xie2014eccv-metric,
title = {{Metric-Based Pairwise and Multiple Image Registration}},
author = {Xie, Qian and Kurtek, Sebastian and Klassen, Eric and Christensen, Gary E. and Srivastava, Anuj},
booktitle = {European Conference on Computer Vision},
year = {2014},
pages = {236-250},
doi = {10.1007/978-3-319-10605-2_16},
url = {https://mlanthology.org/eccv/2014/xie2014eccv-metric/}
}