A Convex Solution to Spatially-Regularized Correspondence Problems
Abstract
We propose a convex formulation of the correspondence problem between two images with respect to an energy function measuring data consistency and spatial regularity. To this end, we formulate the general correspondence problem as the search for a minimal two-dimensional surface in $\mathbb {R}^4$ R 4 . We then use tools from geometric measure theory and introduce 2-vector fields as a representation of two-dimensional surfaces in $\mathbb {R}^4$ R 4 . We propose a discretization of this surface formulation that gives rise to a convex minimization problem and compute a globally optimal solution using an efficient primal-dual algorithm.
Cite
Text
Windheuser and Cremers. "A Convex Solution to Spatially-Regularized Correspondence Problems." European Conference on Computer Vision, 2016. doi:10.1007/978-3-319-46475-6_52Markdown
[Windheuser and Cremers. "A Convex Solution to Spatially-Regularized Correspondence Problems." European Conference on Computer Vision, 2016.](https://mlanthology.org/eccv/2016/windheuser2016eccv-convex/) doi:10.1007/978-3-319-46475-6_52BibTeX
@inproceedings{windheuser2016eccv-convex,
title = {{A Convex Solution to Spatially-Regularized Correspondence Problems}},
author = {Windheuser, Thomas and Cremers, Daniel},
booktitle = {European Conference on Computer Vision},
year = {2016},
pages = {853-868},
doi = {10.1007/978-3-319-46475-6_52},
url = {https://mlanthology.org/eccv/2016/windheuser2016eccv-convex/}
}